0s autopkgtest [11:51:35]: starting date and time: 2025-11-17 11:51:35+0000 0s autopkgtest [11:51:35]: git checkout: 4b346b80 nova: make wait_reboot return success even when a no-op 0s autopkgtest [11:51:35]: host juju-7f2275-prod-proposed-migration-environment-15; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.bn697jff/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-cpu2-ram4-disk20-amd64 --security-groups autopkgtest-juju-7f2275-prod-proposed-migration-environment-15@sto01-4.secgroup --name adt-resolute-amd64-cnvkit-20251117-115135-juju-7f2275-prod-proposed-migration-environment-15-235995ba-c25b-4ed2-b16a-d68505032711 --image adt/ubuntu-resolute-amd64-server --keyname testbed-juju-7f2275-prod-proposed-migration-environment-15 --net-id=net_prod-autopkgtest-workers-amd64 -e TERM=linux --mirror=http://ftpmaster.internal/ubuntu/ 4s Creating nova instance adt-resolute-amd64-cnvkit-20251117-115135-juju-7f2275-prod-proposed-migration-environment-15-235995ba-c25b-4ed2-b16a-d68505032711 from image adt/ubuntu-resolute-amd64-server-20251117.img (UUID 9762b0cc-7c5b-4854-acd5-cc74ad0de8c6)... 46s autopkgtest [11:52:21]: testbed dpkg architecture: amd64 46s autopkgtest [11:52:21]: testbed apt version: 3.1.11 46s autopkgtest [11:52:21]: @@@@@@@@@@@@@@@@@@@@ test bed setup 46s autopkgtest [11:52:21]: testbed release detected to be: None 47s autopkgtest [11:52:22]: updating testbed package index (apt update) 47s Get:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease [87.8 kB] 47s Hit:2 http://ftpmaster.internal/ubuntu resolute InRelease 47s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease 47s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease 47s Get:5 http://ftpmaster.internal/ubuntu resolute-proposed/restricted Sources [9848 B] 47s Get:6 http://ftpmaster.internal/ubuntu resolute-proposed/main Sources [81.1 kB] 47s Get:7 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse Sources [22.9 kB] 48s Get:8 http://ftpmaster.internal/ubuntu resolute-proposed/universe Sources [868 kB] 48s Get:9 http://ftpmaster.internal/ubuntu resolute-proposed/main amd64 Packages [159 kB] 48s Get:10 http://ftpmaster.internal/ubuntu resolute-proposed/main i386 Packages [118 kB] 48s Get:11 http://ftpmaster.internal/ubuntu resolute-proposed/main amd64 c-n-f Metadata [3096 B] 48s Get:12 http://ftpmaster.internal/ubuntu resolute-proposed/restricted i386 Packages [3744 B] 48s Get:13 http://ftpmaster.internal/ubuntu resolute-proposed/restricted amd64 Packages [64.6 kB] 48s Get:14 http://ftpmaster.internal/ubuntu resolute-proposed/restricted amd64 c-n-f Metadata [336 B] 48s Get:15 http://ftpmaster.internal/ubuntu resolute-proposed/universe amd64 Packages [607 kB] 48s Get:16 http://ftpmaster.internal/ubuntu resolute-proposed/universe i386 Packages [279 kB] 48s Get:17 http://ftpmaster.internal/ubuntu resolute-proposed/universe amd64 c-n-f Metadata [21.2 kB] 48s Get:18 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse amd64 Packages [13.4 kB] 48s Get:19 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse i386 Packages [6516 B] 48s Get:20 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse amd64 c-n-f Metadata [680 B] 50s Fetched 2346 kB in 1s (1977 kB/s) 50s Reading package lists... 51s Hit:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease 51s Hit:2 http://ftpmaster.internal/ubuntu resolute InRelease 51s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease 51s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease 52s Reading package lists... 52s Reading package lists... 52s Building dependency tree... 52s Reading state information... 52s Calculating upgrade... 52s The following packages will be upgraded: 52s libpython3-stdlib python3 python3-minimal usbutils 52s 4 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 52s Need to get 146 kB of archives. 52s After this operation, 0 B of additional disk space will be used. 52s Get:1 http://ftpmaster.internal/ubuntu resolute-proposed/main amd64 python3-minimal amd64 3.13.7-2 [27.8 kB] 52s Get:2 http://ftpmaster.internal/ubuntu resolute-proposed/main amd64 python3 amd64 3.13.7-2 [23.9 kB] 52s Get:3 http://ftpmaster.internal/ubuntu resolute-proposed/main amd64 libpython3-stdlib amd64 3.13.7-2 [10.6 kB] 52s Get:4 http://ftpmaster.internal/ubuntu resolute/main amd64 usbutils amd64 1:019-1 [83.9 kB] 52s dpkg-preconfigure: unable to re-open stdin: No such file or directory 52s Fetched 146 kB in 0s (0 B/s) 52s (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 ... 83372 files and directories currently installed.) 52s Preparing to unpack .../python3-minimal_3.13.7-2_amd64.deb ... 52s Unpacking python3-minimal (3.13.7-2) over (3.13.7-1) ... 52s Setting up python3-minimal (3.13.7-2) ... 52s (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 ... 83372 files and directories currently installed.) 53s Preparing to unpack .../python3_3.13.7-2_amd64.deb ... 53s running python pre-rtupdate hooks for python3.13... 53s Unpacking python3 (3.13.7-2) over (3.13.7-1) ... 53s Preparing to unpack .../libpython3-stdlib_3.13.7-2_amd64.deb ... 53s Unpacking libpython3-stdlib:amd64 (3.13.7-2) over (3.13.7-1) ... 53s Preparing to unpack .../usbutils_1%3a019-1_amd64.deb ... 53s Unpacking usbutils (1:019-1) over (1:018-2) ... 53s Setting up usbutils (1:019-1) ... 53s Setting up libpython3-stdlib:amd64 (3.13.7-2) ... 53s Setting up python3 (3.13.7-2) ... 53s running python rtupdate hooks for python3.13... 53s running python post-rtupdate hooks for python3.13... 53s Processing triggers for man-db (2.13.1-1) ... 53s autopkgtest [11:52:28]: upgrading testbed (apt dist-upgrade and autopurge) 53s Reading package lists... 54s Building dependency tree... 54s Reading state information... 54s Calculating upgrade... 54s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 54s Reading package lists... 54s Building dependency tree... 54s Reading state information... 54s Solving dependencies... 54s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 56s autopkgtest [11:52:31]: testbed running kernel: Linux 6.17.0-5-generic #5-Ubuntu SMP PREEMPT_DYNAMIC Mon Sep 22 10:00:33 UTC 2025 56s autopkgtest [11:52:31]: @@@@@@@@@@@@@@@@@@@@ apt-source cnvkit 61s Get:1 http://ftpmaster.internal/ubuntu resolute/universe cnvkit 0.9.12-1 (dsc) [2483 B] 61s Get:2 http://ftpmaster.internal/ubuntu resolute/universe cnvkit 0.9.12-1 (tar) [32.1 MB] 61s Get:3 http://ftpmaster.internal/ubuntu resolute/universe cnvkit 0.9.12-1 (diff) [20.8 kB] 61s gpgv: Signature made Thu Feb 6 14:25:04 2025 UTC 61s gpgv: using RSA key 724D609337113C710550D7473C26763F6C67E6E2 61s gpgv: issuer "crusoe@debian.org" 61s gpgv: Can't check signature: No public key 61s dpkg-source: warning: cannot verify inline signature for ./cnvkit_0.9.12-1.dsc: no acceptable signature found 62s autopkgtest [11:52:37]: testing package cnvkit version 0.9.12-1 62s autopkgtest [11:52:37]: build not needed 67s autopkgtest [11:52:42]: test run-unit-test: preparing testbed 67s Reading package lists... 67s Building dependency tree... 67s Reading state information... 67s Solving dependencies... 67s The following NEW packages will be installed: 67s blt cnvkit cython3 fontconfig fontconfig-config fonts-dejavu-core 67s fonts-dejavu-mono fonts-lyx fonts-urw-base35 libblas3 libcairo2 libdatrie1 67s libdeflate0 libfontconfig1 libfontenc1 libgfortran5 libgomp1 libgpgmepp6t64 67s libgraphite2-3 libharfbuzz0b libhts3t64 libhtscodecs2 libice6 libimagequant0 67s libjbig0 libjpeg-turbo8 libjpeg8 liblapack3 liblcms2-2 liblerc4 libopenjp2-7 67s libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 libpaper-utils 67s libpaper2 libpixman-1-0 libpoppler147 libqhull-r8.0 libraqm0 libsharpyuv0 67s libsm6 libtcl8.6 libthai-data libthai0 libtiff6 libtk8.6 libwebp7 67s libwebpdemux2 libwebpmux3 libxcb-render0 libxcb-shm0 libxft2 libxrender1 67s libxslt1.1 libxss1 libxt6t64 libzopfli1 poppler-utils python-matplotlib-data 67s python3-biopython python3-brotli python3-cairo python3-charset-normalizer 67s python3-contourpy python3-cycler python3-decorator python3-fonttools 67s python3-freetype python3-fs python3-joblib python3-kiwisolver python3-lxml 67s python3-lz4 python3-matplotlib python3-mpmath python3-networkx python3-numpy 67s python3-numpy-dev python3-pandas python3-pandas-lib python3-pil 67s python3-pil.imagetk python3-platformdirs python3-pomegranate python3-pyfaidx 67s python3-pysam python3-pytz python3-reportlab python3-rlpycairo python3-scipy 67s python3-sklearn python3-sklearn-lib python3-sympy python3-threadpoolctl 67s python3-tk python3-ufolib2 python3-unicodedata2 python3-zopfli python3.13-tk 67s python3.14-tk r-base-core r-bioc-biocgenerics r-bioc-dnacopy sgml-base 67s tk8.6-blt2.5 unicode-data unzip w3c-sgml-lib x11-common xdg-utils 67s xfonts-encodings xfonts-utils xml-core zip 67s 0 upgraded, 115 newly installed, 0 to remove and 0 not upgraded. 67s Need to get 192 MB of archives. 67s After this operation, 873 MB of additional disk space will be used. 67s Get:1 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-numpy-dev amd64 1:2.2.4+ds-1ubuntu1 [147 kB] 67s Get:2 http://ftpmaster.internal/ubuntu resolute/main amd64 libblas3 amd64 3.12.1-7 [259 kB] 67s Get:3 http://ftpmaster.internal/ubuntu resolute/main amd64 libgfortran5 amd64 15.2.0-7ubuntu1 [939 kB] 68s Get:4 http://ftpmaster.internal/ubuntu resolute/main amd64 liblapack3 amd64 3.12.1-7 [2739 kB] 68s Get:5 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-numpy amd64 1:2.2.4+ds-1ubuntu1 [5377 kB] 68s Get:6 http://ftpmaster.internal/ubuntu resolute/main amd64 libtcl8.6 amd64 8.6.17+dfsg-1 [1036 kB] 68s Get:7 http://ftpmaster.internal/ubuntu resolute/main amd64 fonts-dejavu-mono all 2.37-8 [502 kB] 68s Get:8 http://ftpmaster.internal/ubuntu resolute/main amd64 fonts-dejavu-core all 2.37-8 [835 kB] 68s Get:9 http://ftpmaster.internal/ubuntu resolute/main amd64 libfontenc1 amd64 1:1.1.8-1build1 [14.0 kB] 68s Get:10 http://ftpmaster.internal/ubuntu resolute/main amd64 x11-common all 1:7.7+24ubuntu1 [22.4 kB] 68s Get:11 http://ftpmaster.internal/ubuntu resolute/main amd64 xfonts-encodings all 1:1.0.5-0ubuntu2 [578 kB] 68s Get:12 http://ftpmaster.internal/ubuntu resolute/main amd64 xfonts-utils amd64 1:7.7+7 [97.1 kB] 68s Get:13 http://ftpmaster.internal/ubuntu resolute/main amd64 fonts-urw-base35 all 20200910-8 [11.0 MB] 69s Get:14 http://ftpmaster.internal/ubuntu resolute/main amd64 fontconfig-config amd64 2.15.0-2.3ubuntu1 [38.0 kB] 69s Get:15 http://ftpmaster.internal/ubuntu resolute/main amd64 libfontconfig1 amd64 2.15.0-2.3ubuntu1 [141 kB] 69s Get:16 http://ftpmaster.internal/ubuntu resolute/main amd64 libxrender1 amd64 1:0.9.12-1 [19.8 kB] 69s Get:17 http://ftpmaster.internal/ubuntu resolute/main amd64 libxft2 amd64 2.3.6-1build1 [45.3 kB] 69s Get:18 http://ftpmaster.internal/ubuntu resolute/main amd64 libxss1 amd64 1:1.2.3-1build3 [7204 B] 69s Get:19 http://ftpmaster.internal/ubuntu resolute/main amd64 libtk8.6 amd64 8.6.17-1 [823 kB] 69s Get:20 http://ftpmaster.internal/ubuntu resolute/main amd64 tk8.6-blt2.5 amd64 2.5.3+dfsg-8 [694 kB] 69s Get:21 http://ftpmaster.internal/ubuntu resolute/main amd64 blt amd64 2.5.3+dfsg-8 [4824 B] 69s Get:22 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-charset-normalizer amd64 3.4.3-1 [174 kB] 69s Get:23 http://ftpmaster.internal/ubuntu resolute/main amd64 python3.14-tk amd64 3.14.0-4 [108 kB] 69s Get:24 http://ftpmaster.internal/ubuntu resolute/main amd64 python3.13-tk amd64 3.13.9-1 [108 kB] 69s Get:25 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-tk amd64 3.13.9-1 [8946 B] 69s Get:26 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-pil.imagetk amd64 11.3.0-1ubuntu2 [9804 B] 69s Get:27 http://ftpmaster.internal/ubuntu resolute/main amd64 libgomp1 amd64 15.2.0-7ubuntu1 [151 kB] 69s Get:28 http://ftpmaster.internal/ubuntu resolute/main amd64 libimagequant0 amd64 2.18.0-1build1 [36.3 kB] 69s Get:29 http://ftpmaster.internal/ubuntu resolute/main amd64 libjpeg-turbo8 amd64 2.1.5-4ubuntu2 [152 kB] 69s Get:30 http://ftpmaster.internal/ubuntu resolute/main amd64 libjpeg8 amd64 8c-2ubuntu11 [2148 B] 69s Get:31 http://ftpmaster.internal/ubuntu resolute/main amd64 liblcms2-2 amd64 2.17-1 [170 kB] 69s Get:32 http://ftpmaster.internal/ubuntu resolute/main amd64 libopenjp2-7 amd64 2.5.3-2.1 [188 kB] 69s Get:33 http://ftpmaster.internal/ubuntu resolute/main amd64 libgraphite2-3 amd64 1.3.14-2ubuntu1 [73.1 kB] 69s Get:34 http://ftpmaster.internal/ubuntu resolute/main amd64 libharfbuzz0b amd64 12.1.0-1 [535 kB] 69s Get:35 http://ftpmaster.internal/ubuntu resolute/main amd64 libraqm0 amd64 0.10.3-1 [15.4 kB] 69s Get:36 http://ftpmaster.internal/ubuntu resolute/main amd64 libdeflate0 amd64 1.23-2 [49.9 kB] 69s Get:37 http://ftpmaster.internal/ubuntu resolute/main amd64 libjbig0 amd64 2.1-6.1ubuntu2 [29.7 kB] 69s Get:38 http://ftpmaster.internal/ubuntu resolute/main amd64 liblerc4 amd64 4.0.0+ds-5ubuntu1 [271 kB] 69s Get:39 http://ftpmaster.internal/ubuntu resolute/main amd64 libsharpyuv0 amd64 1.5.0-0.1 [25.9 kB] 69s Get:40 http://ftpmaster.internal/ubuntu resolute/main amd64 libwebp7 amd64 1.5.0-0.1 [378 kB] 69s Get:41 http://ftpmaster.internal/ubuntu resolute/main amd64 libtiff6 amd64 4.7.0-3ubuntu3 [209 kB] 69s Get:42 http://ftpmaster.internal/ubuntu resolute/main amd64 libwebpdemux2 amd64 1.5.0-0.1 [13.0 kB] 69s Get:43 http://ftpmaster.internal/ubuntu resolute/main amd64 libwebpmux3 amd64 1.5.0-0.1 [27.6 kB] 69s Get:44 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-pil amd64 11.3.0-1ubuntu2 [504 kB] 69s Get:45 http://ftpmaster.internal/ubuntu resolute/main amd64 libpixman-1-0 amd64 0.46.4-1 [287 kB] 69s Get:46 http://ftpmaster.internal/ubuntu resolute/main amd64 libxcb-render0 amd64 1.17.0-2build1 [17.4 kB] 69s Get:47 http://ftpmaster.internal/ubuntu resolute/main amd64 libxcb-shm0 amd64 1.17.0-2build1 [6120 B] 69s Get:48 http://ftpmaster.internal/ubuntu resolute/main amd64 libcairo2 amd64 1.18.4-1build1 [611 kB] 69s Get:49 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-cairo amd64 1.27.0-2build1 [140 kB] 69s Get:50 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-freetype all 2.5.1-2 [92.2 kB] 69s Get:51 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-rlpycairo all 0.3.0-4 [9332 B] 69s Get:52 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-reportlab all 4.4.4-2 [1147 kB] 69s Get:53 http://ftpmaster.internal/ubuntu resolute/main amd64 sgml-base all 1.31+nmu1 [11.0 kB] 69s Get:54 http://ftpmaster.internal/ubuntu resolute/main amd64 xml-core all 0.19 [20.3 kB] 69s Get:55 http://ftpmaster.internal/ubuntu resolute/universe amd64 w3c-sgml-lib all 1.3-3 [280 kB] 69s Get:56 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-biopython amd64 1.85+dfsg-4 [1719 kB] 69s Get:57 http://ftpmaster.internal/ubuntu resolute/universe amd64 fonts-lyx all 2.4.4-2 [171 kB] 69s Get:58 http://ftpmaster.internal/ubuntu resolute/universe amd64 python-matplotlib-data all 3.10.7+dfsg1-1 [2930 kB] 69s Get:59 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-contourpy amd64 1.3.1-2 [255 kB] 69s Get:60 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-cycler all 0.12.1-2 [9850 B] 69s Get:61 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-brotli amd64 1.1.0-2build6 [340 kB] 69s Get:62 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-platformdirs all 4.3.7-1 [16.9 kB] 69s Get:63 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-fs all 2.4.16-9ubuntu1 [91.5 kB] 69s Get:64 http://ftpmaster.internal/ubuntu resolute/main amd64 libxslt1.1 amd64 1.1.43-0.3 [172 kB] 69s Get:65 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-lxml amd64 6.0.2-1 [2333 kB] 69s Get:66 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-lz4 amd64 4.4.4+dfsg-3 [27.5 kB] 69s Get:67 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-decorator all 5.2.1-2 [28.1 kB] 69s Get:68 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-scipy amd64 1.15.3-1ubuntu1 [20.3 MB] 70s Get:69 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-mpmath all 1.3.0-2 [423 kB] 70s Get:70 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-sympy all 1.14.0-2 [4306 kB] 70s Get:71 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-ufolib2 all 0.17.1+dfsg1-1 [33.5 kB] 70s Get:72 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-unicodedata2 amd64 16.0.0+ds-1build1 [400 kB] 70s Get:73 http://ftpmaster.internal/ubuntu resolute/universe amd64 libzopfli1 amd64 1.0.3-3 [141 kB] 70s Get:74 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-zopfli amd64 0.4.0-1 [11.1 kB] 70s Get:75 http://ftpmaster.internal/ubuntu resolute/universe amd64 unicode-data all 16.0.0-1 [9513 kB] 70s Get:76 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-fonttools amd64 4.57.0-2build1 [1731 kB] 70s Get:77 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-kiwisolver amd64 1.4.10~rc0-1 [65.5 kB] 70s Get:78 http://ftpmaster.internal/ubuntu resolute/universe amd64 libqhull-r8.0 amd64 2020.2-7 [197 kB] 70s Get:79 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-matplotlib amd64 3.10.7+dfsg1-1 [17.2 MB] 70s Get:80 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-pytz all 2025.2-4 [32.3 kB] 70s Get:81 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-pandas-lib amd64 2.3.3+dfsg-1ubuntu1 [7668 kB] 71s Get:82 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-pandas all 2.3.3+dfsg-1ubuntu1 [2948 kB] 71s Get:83 http://ftpmaster.internal/ubuntu resolute/universe amd64 cython3 amd64 3.1.6+dfsg-1ubuntu1 [3428 kB] 71s Get:84 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-joblib all 1.4.2-4 [205 kB] 71s Get:85 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-networkx all 3.2.1-4ubuntu1 [11.5 MB] 71s Get:86 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-pomegranate amd64 0.15.0-2 [4637 kB] 71s Get:87 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-pyfaidx all 0.8.1.3-2 [29.7 kB] 71s Get:88 http://ftpmaster.internal/ubuntu resolute/universe amd64 libhtscodecs2 amd64 1.6.1-2 [132 kB] 71s Get:89 http://ftpmaster.internal/ubuntu resolute/universe amd64 libhts3t64 amd64 1.22.1+ds2-1 [461 kB] 71s Get:90 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-pysam amd64 0.23.3+ds-2 [4536 kB] 71s Get:91 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-sklearn-lib amd64 1.7.2+dfsg-3ubuntu1 [6361 kB] 71s Get:92 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-threadpoolctl all 3.1.0-1 [21.3 kB] 71s Get:93 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-sklearn all 1.7.2+dfsg-3ubuntu1 [2616 kB] 71s Get:94 http://ftpmaster.internal/ubuntu resolute/main amd64 zip amd64 3.0-15ubuntu2 [178 kB] 71s Get:95 http://ftpmaster.internal/ubuntu resolute/main amd64 unzip amd64 6.0-28ubuntu7 [180 kB] 71s Get:96 http://ftpmaster.internal/ubuntu resolute/main amd64 libpaper2 amd64 2.2.5-0.3 [17.4 kB] 71s Get:97 http://ftpmaster.internal/ubuntu resolute/main amd64 libpaper-utils amd64 2.2.5-0.3 [15.5 kB] 71s Get:98 http://ftpmaster.internal/ubuntu resolute/main amd64 xdg-utils all 1.2.1-2ubuntu1 [66.0 kB] 71s Get:99 http://ftpmaster.internal/ubuntu resolute/main amd64 fontconfig amd64 2.15.0-2.3ubuntu1 [180 kB] 71s Get:100 http://ftpmaster.internal/ubuntu resolute/main amd64 libthai-data all 0.1.29-2build1 [158 kB] 71s Get:101 http://ftpmaster.internal/ubuntu resolute/main amd64 libdatrie1 amd64 0.2.13-4 [19.3 kB] 71s Get:102 http://ftpmaster.internal/ubuntu resolute/main amd64 libthai0 amd64 0.1.29-2build1 [18.9 kB] 71s Get:103 http://ftpmaster.internal/ubuntu resolute/main amd64 libpango-1.0-0 amd64 1.56.3-2 [239 kB] 71s Get:104 http://ftpmaster.internal/ubuntu resolute/main amd64 libpangoft2-1.0-0 amd64 1.56.3-2 [52.5 kB] 71s Get:105 http://ftpmaster.internal/ubuntu resolute/main amd64 libpangocairo-1.0-0 amd64 1.56.3-2 [29.0 kB] 71s Get:106 http://ftpmaster.internal/ubuntu resolute/main amd64 libice6 amd64 2:1.1.1-1 [44.1 kB] 71s Get:107 http://ftpmaster.internal/ubuntu resolute/main amd64 libsm6 amd64 2:1.2.6-1 [16.4 kB] 71s Get:108 http://ftpmaster.internal/ubuntu resolute/main amd64 libxt6t64 amd64 1:1.2.1-1.3 [173 kB] 71s Get:109 http://ftpmaster.internal/ubuntu resolute/universe amd64 r-base-core amd64 4.5.2-1 [28.8 MB] 72s Get:110 http://ftpmaster.internal/ubuntu resolute/universe amd64 r-bioc-biocgenerics all 0.52.0-2 [624 kB] 72s Get:111 http://ftpmaster.internal/ubuntu resolute/universe amd64 r-bioc-dnacopy amd64 1.80.0-2 [500 kB] 72s Get:112 http://ftpmaster.internal/ubuntu resolute/universe amd64 cnvkit all 0.9.12-1 [20.6 MB] 72s Get:113 http://ftpmaster.internal/ubuntu resolute/main amd64 libgpgmepp6t64 amd64 1.24.2-3ubuntu2 [124 kB] 72s Get:114 http://ftpmaster.internal/ubuntu resolute/main amd64 libpoppler147 amd64 25.03.0-11.1 [1224 kB] 72s Get:115 http://ftpmaster.internal/ubuntu resolute/main amd64 poppler-utils amd64 25.03.0-11.1 [229 kB] 73s Preconfiguring packages ... 73s Fetched 192 MB in 5s (37.8 MB/s) 73s Selecting previously unselected package python3-numpy-dev:amd64. 73s (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 ... 83372 files and directories currently installed.) 73s Preparing to unpack .../000-python3-numpy-dev_1%3a2.2.4+ds-1ubuntu1_amd64.deb ... 73s Unpacking python3-numpy-dev:amd64 (1:2.2.4+ds-1ubuntu1) ... 73s Selecting previously unselected package libblas3:amd64. 73s Preparing to unpack .../001-libblas3_3.12.1-7_amd64.deb ... 73s Unpacking libblas3:amd64 (3.12.1-7) ... 73s Selecting previously unselected package libgfortran5:amd64. 73s Preparing to unpack .../002-libgfortran5_15.2.0-7ubuntu1_amd64.deb ... 73s Unpacking libgfortran5:amd64 (15.2.0-7ubuntu1) ... 73s Selecting previously unselected package liblapack3:amd64. 73s Preparing to unpack .../003-liblapack3_3.12.1-7_amd64.deb ... 73s Unpacking liblapack3:amd64 (3.12.1-7) ... 73s Selecting previously unselected package python3-numpy. 73s Preparing to unpack .../004-python3-numpy_1%3a2.2.4+ds-1ubuntu1_amd64.deb ... 73s Unpacking python3-numpy (1:2.2.4+ds-1ubuntu1) ... 73s Selecting previously unselected package libtcl8.6:amd64. 73s Preparing to unpack .../005-libtcl8.6_8.6.17+dfsg-1_amd64.deb ... 73s Unpacking libtcl8.6:amd64 (8.6.17+dfsg-1) ... 73s Selecting previously unselected package fonts-dejavu-mono. 73s Preparing to unpack .../006-fonts-dejavu-mono_2.37-8_all.deb ... 73s Unpacking fonts-dejavu-mono (2.37-8) ... 73s Selecting previously unselected package fonts-dejavu-core. 73s Preparing to unpack .../007-fonts-dejavu-core_2.37-8_all.deb ... 73s Unpacking fonts-dejavu-core (2.37-8) ... 73s Selecting previously unselected package libfontenc1:amd64. 73s Preparing to unpack .../008-libfontenc1_1%3a1.1.8-1build1_amd64.deb ... 73s Unpacking libfontenc1:amd64 (1:1.1.8-1build1) ... 73s Selecting previously unselected package x11-common. 73s Preparing to unpack .../009-x11-common_1%3a7.7+24ubuntu1_all.deb ... 73s Unpacking x11-common (1:7.7+24ubuntu1) ... 73s Selecting previously unselected package xfonts-encodings. 73s Preparing to unpack .../010-xfonts-encodings_1%3a1.0.5-0ubuntu2_all.deb ... 73s Unpacking xfonts-encodings (1:1.0.5-0ubuntu2) ... 73s Selecting previously unselected package xfonts-utils. 73s Preparing to unpack .../011-xfonts-utils_1%3a7.7+7_amd64.deb ... 73s Unpacking xfonts-utils (1:7.7+7) ... 73s Selecting previously unselected package fonts-urw-base35. 73s Preparing to unpack .../012-fonts-urw-base35_20200910-8_all.deb ... 73s Unpacking fonts-urw-base35 (20200910-8) ... 73s Selecting previously unselected package fontconfig-config. 73s Preparing to unpack .../013-fontconfig-config_2.15.0-2.3ubuntu1_amd64.deb ... 73s Unpacking fontconfig-config (2.15.0-2.3ubuntu1) ... 73s Selecting previously unselected package libfontconfig1:amd64. 73s Preparing to unpack .../014-libfontconfig1_2.15.0-2.3ubuntu1_amd64.deb ... 73s Unpacking libfontconfig1:amd64 (2.15.0-2.3ubuntu1) ... 73s Selecting previously unselected package libxrender1:amd64. 73s Preparing to unpack .../015-libxrender1_1%3a0.9.12-1_amd64.deb ... 73s Unpacking libxrender1:amd64 (1:0.9.12-1) ... 73s Selecting previously unselected package libxft2:amd64. 73s Preparing to unpack .../016-libxft2_2.3.6-1build1_amd64.deb ... 73s Unpacking libxft2:amd64 (2.3.6-1build1) ... 73s Selecting previously unselected package libxss1:amd64. 73s Preparing to unpack .../017-libxss1_1%3a1.2.3-1build3_amd64.deb ... 73s Unpacking libxss1:amd64 (1:1.2.3-1build3) ... 73s Selecting previously unselected package libtk8.6:amd64. 73s Preparing to unpack .../018-libtk8.6_8.6.17-1_amd64.deb ... 73s Unpacking libtk8.6:amd64 (8.6.17-1) ... 73s Selecting previously unselected package tk8.6-blt2.5. 73s Preparing to unpack .../019-tk8.6-blt2.5_2.5.3+dfsg-8_amd64.deb ... 73s Unpacking tk8.6-blt2.5 (2.5.3+dfsg-8) ... 73s Selecting previously unselected package blt. 73s Preparing to unpack .../020-blt_2.5.3+dfsg-8_amd64.deb ... 73s Unpacking blt (2.5.3+dfsg-8) ... 73s Selecting previously unselected package python3-charset-normalizer. 73s Preparing to unpack .../021-python3-charset-normalizer_3.4.3-1_amd64.deb ... 73s Unpacking python3-charset-normalizer (3.4.3-1) ... 73s Selecting previously unselected package python3.14-tk. 73s Preparing to unpack .../022-python3.14-tk_3.14.0-4_amd64.deb ... 73s Unpacking python3.14-tk (3.14.0-4) ... 73s Selecting previously unselected package python3.13-tk. 73s Preparing to unpack .../023-python3.13-tk_3.13.9-1_amd64.deb ... 73s Unpacking python3.13-tk (3.13.9-1) ... 73s Selecting previously unselected package python3-tk:amd64. 73s Preparing to unpack .../024-python3-tk_3.13.9-1_amd64.deb ... 73s Unpacking python3-tk:amd64 (3.13.9-1) ... 73s Selecting previously unselected package python3-pil.imagetk:amd64. 73s Preparing to unpack .../025-python3-pil.imagetk_11.3.0-1ubuntu2_amd64.deb ... 73s Unpacking python3-pil.imagetk:amd64 (11.3.0-1ubuntu2) ... 73s Selecting previously unselected package libgomp1:amd64. 73s Preparing to unpack .../026-libgomp1_15.2.0-7ubuntu1_amd64.deb ... 73s Unpacking libgomp1:amd64 (15.2.0-7ubuntu1) ... 73s Selecting previously unselected package libimagequant0:amd64. 73s Preparing to unpack .../027-libimagequant0_2.18.0-1build1_amd64.deb ... 73s Unpacking libimagequant0:amd64 (2.18.0-1build1) ... 73s Selecting previously unselected package libjpeg-turbo8:amd64. 73s Preparing to unpack .../028-libjpeg-turbo8_2.1.5-4ubuntu2_amd64.deb ... 73s Unpacking libjpeg-turbo8:amd64 (2.1.5-4ubuntu2) ... 73s Selecting previously unselected package libjpeg8:amd64. 73s Preparing to unpack .../029-libjpeg8_8c-2ubuntu11_amd64.deb ... 73s Unpacking libjpeg8:amd64 (8c-2ubuntu11) ... 73s Selecting previously unselected package liblcms2-2:amd64. 73s Preparing to unpack .../030-liblcms2-2_2.17-1_amd64.deb ... 73s Unpacking liblcms2-2:amd64 (2.17-1) ... 73s Selecting previously unselected package libopenjp2-7:amd64. 73s Preparing to unpack .../031-libopenjp2-7_2.5.3-2.1_amd64.deb ... 73s Unpacking libopenjp2-7:amd64 (2.5.3-2.1) ... 73s Selecting previously unselected package libgraphite2-3:amd64. 73s Preparing to unpack .../032-libgraphite2-3_1.3.14-2ubuntu1_amd64.deb ... 73s Unpacking libgraphite2-3:amd64 (1.3.14-2ubuntu1) ... 73s Selecting previously unselected package libharfbuzz0b:amd64. 73s Preparing to unpack .../033-libharfbuzz0b_12.1.0-1_amd64.deb ... 73s Unpacking libharfbuzz0b:amd64 (12.1.0-1) ... 74s Selecting previously unselected package libraqm0:amd64. 74s Preparing to unpack .../034-libraqm0_0.10.3-1_amd64.deb ... 74s Unpacking libraqm0:amd64 (0.10.3-1) ... 74s Selecting previously unselected package libdeflate0:amd64. 74s Preparing to unpack .../035-libdeflate0_1.23-2_amd64.deb ... 74s Unpacking libdeflate0:amd64 (1.23-2) ... 74s Selecting previously unselected package libjbig0:amd64. 74s Preparing to unpack .../036-libjbig0_2.1-6.1ubuntu2_amd64.deb ... 74s Unpacking libjbig0:amd64 (2.1-6.1ubuntu2) ... 74s Selecting previously unselected package liblerc4:amd64. 74s Preparing to unpack .../037-liblerc4_4.0.0+ds-5ubuntu1_amd64.deb ... 74s Unpacking liblerc4:amd64 (4.0.0+ds-5ubuntu1) ... 74s Selecting previously unselected package libsharpyuv0:amd64. 74s Preparing to unpack .../038-libsharpyuv0_1.5.0-0.1_amd64.deb ... 74s Unpacking libsharpyuv0:amd64 (1.5.0-0.1) ... 74s Selecting previously unselected package libwebp7:amd64. 74s Preparing to unpack .../039-libwebp7_1.5.0-0.1_amd64.deb ... 74s Unpacking libwebp7:amd64 (1.5.0-0.1) ... 74s Selecting previously unselected package libtiff6:amd64. 74s Preparing to unpack .../040-libtiff6_4.7.0-3ubuntu3_amd64.deb ... 74s Unpacking libtiff6:amd64 (4.7.0-3ubuntu3) ... 74s Selecting previously unselected package libwebpdemux2:amd64. 74s Preparing to unpack .../041-libwebpdemux2_1.5.0-0.1_amd64.deb ... 74s Unpacking libwebpdemux2:amd64 (1.5.0-0.1) ... 74s Selecting previously unselected package libwebpmux3:amd64. 74s Preparing to unpack .../042-libwebpmux3_1.5.0-0.1_amd64.deb ... 74s Unpacking libwebpmux3:amd64 (1.5.0-0.1) ... 74s Selecting previously unselected package python3-pil:amd64. 74s Preparing to unpack .../043-python3-pil_11.3.0-1ubuntu2_amd64.deb ... 74s Unpacking python3-pil:amd64 (11.3.0-1ubuntu2) ... 74s Selecting previously unselected package libpixman-1-0:amd64. 74s Preparing to unpack .../044-libpixman-1-0_0.46.4-1_amd64.deb ... 74s Unpacking libpixman-1-0:amd64 (0.46.4-1) ... 74s Selecting previously unselected package libxcb-render0:amd64. 74s Preparing to unpack .../045-libxcb-render0_1.17.0-2build1_amd64.deb ... 74s Unpacking libxcb-render0:amd64 (1.17.0-2build1) ... 74s Selecting previously unselected package libxcb-shm0:amd64. 74s Preparing to unpack .../046-libxcb-shm0_1.17.0-2build1_amd64.deb ... 74s Unpacking libxcb-shm0:amd64 (1.17.0-2build1) ... 74s Selecting previously unselected package libcairo2:amd64. 74s Preparing to unpack .../047-libcairo2_1.18.4-1build1_amd64.deb ... 74s Unpacking libcairo2:amd64 (1.18.4-1build1) ... 74s Selecting previously unselected package python3-cairo. 74s Preparing to unpack .../048-python3-cairo_1.27.0-2build1_amd64.deb ... 74s Unpacking python3-cairo (1.27.0-2build1) ... 74s Selecting previously unselected package python3-freetype. 74s Preparing to unpack .../049-python3-freetype_2.5.1-2_all.deb ... 74s Unpacking python3-freetype (2.5.1-2) ... 74s Selecting previously unselected package python3-rlpycairo. 74s Preparing to unpack .../050-python3-rlpycairo_0.3.0-4_all.deb ... 74s Unpacking python3-rlpycairo (0.3.0-4) ... 74s Selecting previously unselected package python3-reportlab. 74s Preparing to unpack .../051-python3-reportlab_4.4.4-2_all.deb ... 74s Unpacking python3-reportlab (4.4.4-2) ... 74s Selecting previously unselected package sgml-base. 74s Preparing to unpack .../052-sgml-base_1.31+nmu1_all.deb ... 74s Unpacking sgml-base (1.31+nmu1) ... 74s Selecting previously unselected package xml-core. 74s Preparing to unpack .../053-xml-core_0.19_all.deb ... 74s Unpacking xml-core (0.19) ... 74s Selecting previously unselected package w3c-sgml-lib. 74s Preparing to unpack .../054-w3c-sgml-lib_1.3-3_all.deb ... 74s Unpacking w3c-sgml-lib (1.3-3) ... 74s Selecting previously unselected package python3-biopython. 74s Preparing to unpack .../055-python3-biopython_1.85+dfsg-4_amd64.deb ... 74s Unpacking python3-biopython (1.85+dfsg-4) ... 74s Selecting previously unselected package fonts-lyx. 74s Preparing to unpack .../056-fonts-lyx_2.4.4-2_all.deb ... 74s Unpacking fonts-lyx (2.4.4-2) ... 74s Selecting previously unselected package python-matplotlib-data. 74s Preparing to unpack .../057-python-matplotlib-data_3.10.7+dfsg1-1_all.deb ... 74s Unpacking python-matplotlib-data (3.10.7+dfsg1-1) ... 74s Selecting previously unselected package python3-contourpy. 74s Preparing to unpack .../058-python3-contourpy_1.3.1-2_amd64.deb ... 74s Unpacking python3-contourpy (1.3.1-2) ... 74s Selecting previously unselected package python3-cycler. 74s Preparing to unpack .../059-python3-cycler_0.12.1-2_all.deb ... 74s Unpacking python3-cycler (0.12.1-2) ... 74s Selecting previously unselected package python3-brotli. 74s Preparing to unpack .../060-python3-brotli_1.1.0-2build6_amd64.deb ... 74s Unpacking python3-brotli (1.1.0-2build6) ... 74s Selecting previously unselected package python3-platformdirs. 74s Preparing to unpack .../061-python3-platformdirs_4.3.7-1_all.deb ... 74s Unpacking python3-platformdirs (4.3.7-1) ... 74s Selecting previously unselected package python3-fs. 74s Preparing to unpack .../062-python3-fs_2.4.16-9ubuntu1_all.deb ... 74s Unpacking python3-fs (2.4.16-9ubuntu1) ... 74s Selecting previously unselected package libxslt1.1:amd64. 74s Preparing to unpack .../063-libxslt1.1_1.1.43-0.3_amd64.deb ... 74s Unpacking libxslt1.1:amd64 (1.1.43-0.3) ... 74s Selecting previously unselected package python3-lxml:amd64. 74s Preparing to unpack .../064-python3-lxml_6.0.2-1_amd64.deb ... 74s Unpacking python3-lxml:amd64 (6.0.2-1) ... 74s Selecting previously unselected package python3-lz4. 74s Preparing to unpack .../065-python3-lz4_4.4.4+dfsg-3_amd64.deb ... 74s Unpacking python3-lz4 (4.4.4+dfsg-3) ... 74s Selecting previously unselected package python3-decorator. 74s Preparing to unpack .../066-python3-decorator_5.2.1-2_all.deb ... 74s Unpacking python3-decorator (5.2.1-2) ... 74s Selecting previously unselected package python3-scipy. 74s Preparing to unpack .../067-python3-scipy_1.15.3-1ubuntu1_amd64.deb ... 74s Unpacking python3-scipy (1.15.3-1ubuntu1) ... 74s Selecting previously unselected package python3-mpmath. 74s Preparing to unpack .../068-python3-mpmath_1.3.0-2_all.deb ... 74s Unpacking python3-mpmath (1.3.0-2) ... 74s Selecting previously unselected package python3-sympy. 74s Preparing to unpack .../069-python3-sympy_1.14.0-2_all.deb ... 74s Unpacking python3-sympy (1.14.0-2) ... 74s Selecting previously unselected package python3-ufolib2. 74s Preparing to unpack .../070-python3-ufolib2_0.17.1+dfsg1-1_all.deb ... 74s Unpacking python3-ufolib2 (0.17.1+dfsg1-1) ... 75s Selecting previously unselected package python3-unicodedata2. 75s Preparing to unpack .../071-python3-unicodedata2_16.0.0+ds-1build1_amd64.deb ... 75s Unpacking python3-unicodedata2 (16.0.0+ds-1build1) ... 75s Selecting previously unselected package libzopfli1. 75s Preparing to unpack .../072-libzopfli1_1.0.3-3_amd64.deb ... 75s Unpacking libzopfli1 (1.0.3-3) ... 75s Selecting previously unselected package python3-zopfli. 75s Preparing to unpack .../073-python3-zopfli_0.4.0-1_amd64.deb ... 75s Unpacking python3-zopfli (0.4.0-1) ... 75s Selecting previously unselected package unicode-data. 75s Preparing to unpack .../074-unicode-data_16.0.0-1_all.deb ... 75s Unpacking unicode-data (16.0.0-1) ... 75s Selecting previously unselected package python3-fonttools. 75s Preparing to unpack .../075-python3-fonttools_4.57.0-2build1_amd64.deb ... 75s Unpacking python3-fonttools (4.57.0-2build1) ... 75s Selecting previously unselected package python3-kiwisolver. 75s Preparing to unpack .../076-python3-kiwisolver_1.4.10~rc0-1_amd64.deb ... 75s Unpacking python3-kiwisolver (1.4.10~rc0-1) ... 75s Selecting previously unselected package libqhull-r8.0:amd64. 75s Preparing to unpack .../077-libqhull-r8.0_2020.2-7_amd64.deb ... 75s Unpacking libqhull-r8.0:amd64 (2020.2-7) ... 75s Selecting previously unselected package python3-matplotlib. 75s Preparing to unpack .../078-python3-matplotlib_3.10.7+dfsg1-1_amd64.deb ... 75s Unpacking python3-matplotlib (3.10.7+dfsg1-1) ... 75s Selecting previously unselected package python3-pytz. 75s Preparing to unpack .../079-python3-pytz_2025.2-4_all.deb ... 75s Unpacking python3-pytz (2025.2-4) ... 75s Selecting previously unselected package python3-pandas-lib:amd64. 75s Preparing to unpack .../080-python3-pandas-lib_2.3.3+dfsg-1ubuntu1_amd64.deb ... 75s Unpacking python3-pandas-lib:amd64 (2.3.3+dfsg-1ubuntu1) ... 75s Selecting previously unselected package python3-pandas. 75s Preparing to unpack .../081-python3-pandas_2.3.3+dfsg-1ubuntu1_all.deb ... 75s Unpacking python3-pandas (2.3.3+dfsg-1ubuntu1) ... 75s Selecting previously unselected package cython3. 75s Preparing to unpack .../082-cython3_3.1.6+dfsg-1ubuntu1_amd64.deb ... 75s Unpacking cython3 (3.1.6+dfsg-1ubuntu1) ... 75s Selecting previously unselected package python3-joblib. 75s Preparing to unpack .../083-python3-joblib_1.4.2-4_all.deb ... 75s Unpacking python3-joblib (1.4.2-4) ... 75s Selecting previously unselected package python3-networkx. 75s Preparing to unpack .../084-python3-networkx_3.2.1-4ubuntu1_all.deb ... 75s Unpacking python3-networkx (3.2.1-4ubuntu1) ... 76s Selecting previously unselected package python3-pomegranate. 76s Preparing to unpack .../085-python3-pomegranate_0.15.0-2_amd64.deb ... 76s Unpacking python3-pomegranate (0.15.0-2) ... 76s Selecting previously unselected package python3-pyfaidx. 76s Preparing to unpack .../086-python3-pyfaidx_0.8.1.3-2_all.deb ... 76s Unpacking python3-pyfaidx (0.8.1.3-2) ... 76s Selecting previously unselected package libhtscodecs2:amd64. 76s Preparing to unpack .../087-libhtscodecs2_1.6.1-2_amd64.deb ... 76s Unpacking libhtscodecs2:amd64 (1.6.1-2) ... 76s Selecting previously unselected package libhts3t64:amd64. 76s Preparing to unpack .../088-libhts3t64_1.22.1+ds2-1_amd64.deb ... 76s Unpacking libhts3t64:amd64 (1.22.1+ds2-1) ... 76s Selecting previously unselected package python3-pysam. 76s Preparing to unpack .../089-python3-pysam_0.23.3+ds-2_amd64.deb ... 76s Unpacking python3-pysam (0.23.3+ds-2) ... 76s Selecting previously unselected package python3-sklearn-lib:amd64. 76s Preparing to unpack .../090-python3-sklearn-lib_1.7.2+dfsg-3ubuntu1_amd64.deb ... 76s Unpacking python3-sklearn-lib:amd64 (1.7.2+dfsg-3ubuntu1) ... 76s Selecting previously unselected package python3-threadpoolctl. 76s Preparing to unpack .../091-python3-threadpoolctl_3.1.0-1_all.deb ... 76s Unpacking python3-threadpoolctl (3.1.0-1) ... 76s Selecting previously unselected package python3-sklearn. 76s Preparing to unpack .../092-python3-sklearn_1.7.2+dfsg-3ubuntu1_all.deb ... 76s Unpacking python3-sklearn (1.7.2+dfsg-3ubuntu1) ... 76s Selecting previously unselected package zip. 76s Preparing to unpack .../093-zip_3.0-15ubuntu2_amd64.deb ... 76s Unpacking zip (3.0-15ubuntu2) ... 76s Selecting previously unselected package unzip. 76s Preparing to unpack .../094-unzip_6.0-28ubuntu7_amd64.deb ... 76s Unpacking unzip 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up python3-platformdirs (4.3.7-1) ... 78s Setting up python3-fs (2.4.16-9ubuntu1) ... 78s Setting up unicode-data (16.0.0-1) ... 78s Setting up python3-decorator (5.2.1-2) ... 79s Setting up zip (3.0-15ubuntu2) ... 79s Setting up libfontenc1:amd64 (1:1.1.8-1build1) ... 79s Setting up libblas3:amd64 (3.12.1-7) ... 79s update-alternatives: using /usr/lib/x86_64-linux-gnu/blas/libblas.so.3 to provide /usr/lib/x86_64-linux-gnu/libblas.so.3 (libblas.so.3-x86_64-linux-gnu) in auto mode 79s Setting up libzopfli1 (1.0.3-3) ... 79s Setting up python3-brotli (1.1.0-2build6) ... 79s Setting up xfonts-encodings (1:1.0.5-0ubuntu2) ... 79s Setting up python3-cycler (0.12.1-2) ... 79s Setting up libimagequant0:amd64 (2.18.0-1build1) ... 79s Setting up fonts-dejavu-mono (2.37-8) ... 79s Setting up python3-kiwisolver (1.4.10~rc0-1) ... 79s Setting up python3-numpy-dev:amd64 (1:2.2.4+ds-1ubuntu1) ... 79s Setting up cython3 (3.1.6+dfsg-1ubuntu1) ... 79s Setting up libtcl8.6:amd64 (8.6.17+dfsg-1) ... 79s 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libice6:amd64 (2:1.1.1-1) ... 83s Setting up liblapack3:amd64 (3.12.1-7) ... 83s update-alternatives: using /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3 to provide /usr/lib/x86_64-linux-gnu/liblapack.so.3 (liblapack.so.3-x86_64-linux-gnu) in auto mode 83s Setting up python3-pysam (0.23.3+ds-2) ... 83s Setting up fontconfig-config (2.15.0-2.3ubuntu1) ... 83s Setting up libwebpdemux2:amd64 (1.5.0-0.1) ... 83s Setting up libpaper-utils (2.2.5-0.3) ... 83s Setting up xfonts-utils (1:7.7+7) ... 83s Setting up python3-zopfli (0.4.0-1) ... 83s Setting up libthai0:amd64 (0.1.29-2build1) ... 83s Setting up libraqm0:amd64 (0.10.3-1) ... 83s Setting up python3-numpy (1:2.2.4+ds-1ubuntu1) ... 84s Setting up python3-lxml:amd64 (6.0.2-1) ... 84s Setting up libtiff6:amd64 (4.7.0-3ubuntu3) ... 84s Setting up xml-core (0.19) ... 84s Setting up python3-contourpy (1.3.1-2) ... 85s Setting up libfontconfig1:amd64 (2.15.0-2.3ubuntu1) ... 85s Setting up libsm6:amd64 (2:1.2.6-1) ... 85s Setting up fontconfig (2.15.0-2.3ubuntu1) ... 87s Regenerating fonts cache... done. 87s Setting up libxft2:amd64 (2.3.6-1build1) ... 87s Setting up python3-scipy (1.15.3-1ubuntu1) ... 89s Setting up libpoppler147:amd64 (25.03.0-11.1) ... 89s Setting up python3-pomegranate (0.15.0-2) ... 89s Setting up libtk8.6:amd64 (8.6.17-1) ... 89s Setting up python3-pandas-lib:amd64 (2.3.3+dfsg-1ubuntu1) ... 89s Setting up libpango-1.0-0:amd64 (1.56.3-2) ... 89s Setting up python3-sklearn-lib:amd64 (1.7.2+dfsg-3ubuntu1) ... 89s Setting up fonts-urw-base35 (20200910-8) ... 89s Setting up libcairo2:amd64 (1.18.4-1build1) ... 89s Setting up python3.13-tk (3.13.9-1) ... 89s Setting up python3-pil:amd64 (11.3.0-1ubuntu2) ... 89s Setting up python3-pandas (2.3.3+dfsg-1ubuntu1) ... 92s Setting up libxt6t64:amd64 (1:1.2.1-1.3) ... 92s Setting up python3-sklearn (1.7.2+dfsg-3ubuntu1) ... 93s Setting up poppler-utils (25.03.0-11.1) ... 93s Setting up libpangoft2-1.0-0:amd64 (1.56.3-2) ... 93s Setting up libpangocairo-1.0-0:amd64 (1.56.3-2) ... 93s Setting up tk8.6-blt2.5 (2.5.3+dfsg-8) ... 93s Setting up python3.14-tk (3.14.0-4) ... 93s Setting up python3-cairo (1.27.0-2build1) ... 93s Setting up blt (2.5.3+dfsg-8) ... 93s Setting up python3-tk:amd64 (3.13.9-1) ... 93s Setting up python3-pil.imagetk:amd64 (11.3.0-1ubuntu2) ... 93s Setting up python3-rlpycairo (0.3.0-4) ... 93s Setting up r-base-core (4.5.2-1) ... 93s Creating config file /etc/R/Renviron with new version 93s Setting up python3-reportlab (4.4.4-2) ... 94s Setting up r-bioc-biocgenerics (0.52.0-2) ... 94s Setting up r-bioc-dnacopy (1.80.0-2) ... 94s Setting up python3-fonttools (4.57.0-2build1) ... 94s Setting up python3-ufolib2 (0.17.1+dfsg1-1) ... 94s Setting up python3-matplotlib (3.10.7+dfsg1-1) ... 95s Processing triggers for man-db (2.13.1-1) ... 96s Processing triggers for install-info (7.2-5) ... 96s Processing triggers for libc-bin (2.42-2ubuntu2) ... 96s Processing triggers for sgml-base (1.31+nmu1) ... 96s Setting up w3c-sgml-lib (1.3-3) ... 111s Setting up python3-biopython (1.85+dfsg-4) ... 112s Setting up cnvkit (0.9.12-1) ... 113s autopkgtest [11:53:28]: test run-unit-test: [----------------------- 113s cnvkit.py batch -n -f formats/chrM-Y-trunc.hg19.fa -m wgs formats/na12878-chrM-Y-trunc.bam -d build 114s CNVkit 0.9.12 114s WGS protocol: recommend '--annotate' option (e.g. refFlat.txt) to help locate genes in output files. 114s chrM: Scanning for accessible regions 114s Accessible region chrM:0-121 (size 121) 114s Accessible region chrM:122-1271 (size 1149) 114s Accessible region chrM:1274-1288 (size 14) 114s Accessible region chrM:1289-1547 (size 258) 114s Accessible region chrM:1553-16571 (size 15018) 114s chrY: Scanning for accessible regions 114s Accessible region chrY:500-14900 (size 14400) 114s Accessible region chrY:15600-22966 (size 7366) 114s chrY: Joining over small gaps 114s Joining chrY 500-14900 and 15600-22966 (gap size 700) 114s Wrote chrM-Y-trunc.hg19.bed with 1 regions 114s Detected file format: bed 114s Splitting large targets 114s Created directory build 114s Wrote build/chrM-Y-trunc.hg19.target.bed with 4 regions 114s Wrote build/chrM-Y-trunc.hg19.antitarget.bed with 0 regions 114s Building a flat reference... 114s Detected file format: bed 114s Calculating GC and RepeatMasker content in formats/chrM-Y-trunc.hg19.fa ... 114s Extracting sequences from chromosome chrY 114s Wrote build/reference.cnn with 4 regions 114s Running 1 samples in serial 114s Running the CNVkit pipeline on formats/na12878-chrM-Y-trunc.bam ... 114s Indexing BAM file formats/na12878-chrM-Y-trunc.bam 114s Processing reads in na12878-chrM-Y-trunc.bam 114s Time: 0.005 seconds (7525 reads/sec, 813 bins/sec) 114s Summary: #bins=4, #reads=37, mean=9.2500, min=0.0, max=21.0 114s Percent reads in regions: 0.063 (of 58636 mapped) 114s Wrote build/na12878-chrM-Y-trunc.targetcoverage.cnn with 4 regions 114s Skip processing na12878-chrM-Y-trunc.bam with empty regions file build/chrM-Y-trunc.hg19.antitarget.bed 114s Wrote build/na12878-chrM-Y-trunc.antitargetcoverage.cnn with 0 regions 114s Processing target: na12878-chrM-Y-trunc 114s Keeping 4 of 4 bins 114s Correcting for GC bias... 114s Processing antitarget: na12878-chrM-Y-trunc 114s Wrote build/na12878-chrM-Y-trunc.cnr with 4 regions 114s Segmenting build/na12878-chrM-Y-trunc.cnr ... 114s Segmenting with method 'cbs', significance threshold 1e-06, in 1 processes 114s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 114s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 114s A typical example is when you are setting values in a column of a DataFrame, like: 114s 114s df["col"][row_indexer] = value 114s 114s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 114s 114s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 114s 114s segments.start.iat[0] = bins_start 114s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 114s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 114s A typical example is when you are setting values in a column of a DataFrame, like: 114s 114s df["col"][row_indexer] = value 114s 114s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 114s 114s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 114s 114s segments.end.iat[-1] = bins_end 114s Post-processing build/na12878-chrM-Y-trunc.cns ... 114s Wrote build/na12878-chrM-Y-trunc.cns with 1 regions 114s Applying filter 'ci' 114s Filtered by 'ci' from 1 to 1 rows 114s Calling copy number with thresholds: -1.1 => 0, -0.25 => 1, 0.2 => 2, 0.7 => 3 114s Wrote build/na12878-chrM-Y-trunc.call.cns with 1 regions 114s Significant hits in 4/4 bins (100%) 114s Wrote build/na12878-chrM-Y-trunc.bintest.cns with 4 regions 115s 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/ 116s Wrote build/p2-20_5.antitargetcoverage.cnn with 12563 regions 116s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 116s Wrote build/p2-20_5.targetcoverage.cnn with 6646 regions 116s Wrote build/p2-5_5.antitargetcoverage.cnn with 12563 regions 116s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 116s Wrote build/p2-5_5.targetcoverage.cnn with 6646 regions 116s Wrote build/p2-9_5.antitargetcoverage.cnn with 12563 regions 116s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 116s Wrote build/p2-9_5.targetcoverage.cnn with 6646 regions 116s cnvkit.py reference build/p2-*_5.*targetcoverage.cnn -y -o build/reference-picard.cnn 117s Number of target and antitarget files: 3, 3 117s No FASTA reference genome provided; skipping GC, RM calculations 117s Sample sex not provided; inferring from samples. 117s Relative log2 coverage of chrX=-0.325, chrY=-6.57 (maleness=0.0191 x 0.532 = 0.0102) --> assuming female 117s Relative log2 coverage of chrX=-0.324, chrY=-11 (maleness=0.0317 x 0.532 = 0.0168) --> assuming female 117s Relative log2 coverage of chrX=-0.17, chrY=-17.9 (maleness=0.0141 x 0.532 = 0.00752) --> assuming female 117s Relative log2 coverage of chrX=-0.522, chrY=-11.2 (maleness=0.179 x 0.809 = 0.145) --> assuming female 117s Relative log2 coverage of chrX=-0.531, chrY=-12.6 (maleness=0.11 x 0.895 = 0.0984) --> assuming female 117s Relative log2 coverage of chrX=-0.412, chrY=-16.1 (maleness=0.0599 x 0.895 = 0.0536) --> assuming female 117s Loading build/p2-20_5.targetcoverage.cnn 117s Correcting for GC bias for p2-20_5... 117s Correcting for density bias for p2-20_5... 117s Loading build/p2-5_5.targetcoverage.cnn 117s Correcting for GC bias for p2-5_5... 117s Correcting for density bias for p2-5_5... 117s Loading build/p2-9_5.targetcoverage.cnn 117s Correcting for GC bias for p2-9_5... 117s Correcting for density bias for p2-9_5... 117s Loading build/p2-20_5.antitargetcoverage.cnn 117s Correcting for GC bias for p2-20_5... 117s Loading build/p2-5_5.antitargetcoverage.cnn 117s Correcting for GC bias for p2-5_5... 117s Loading build/p2-9_5.antitargetcoverage.cnn 117s Correcting for GC bias for p2-9_5... 117s Calculating average bin coverages 120s Calculating bin spreads 121s Targets: 338 (5.086%) bins failed filters (log2 < -5.0, log2 > 5.0, spread > 1.0) 121s PLCH2 chr1:2433503-2433878 log2=-0.586 spread=0.505 121s " chr1:2435319-2436685 log2=-0.709 spread=0.503 121s ARID1A chr1:27022844-27024053 log2=-1.481 spread=0.110 121s MYCL1 chr1:40366439-40367601 log2=0.085 spread=0.245 121s LRRC8B chr1:90000112-90000296 log2=0.225 spread=0.274 121s CGH chr1:106510773-106510953 log2=-0.394 spread=0.325 121s NOTCH2 chr1:120572470-120572622 log2=-2.593 spread=1.502 121s " chr1:120611883-120612051 log2=0.070 spread=0.050 121s NTRK1 chr1:156830676-156830968 log2=-0.814 spread=0.513 121s " chr1:156842391-156842479 log2=-0.398 spread=0.290 121s MPC2 chr1:167906099-167906278 log2=0.166 spread=0.393 121s ABL2 chr1:179198326-179198565 log2=-0.546 spread=0.389 121s SMG7 chr1:183441669-183441848 log2=0.368 spread=0.171 121s " chr1:183481886-183481951 log2=-0.040 spread=0.119 121s CDC73 chr1:193219724-193219907 log2=0.087 spread=0.141 121s AKT3 chr1:243675572-243675743 log2=0.222 spread=0.106 121s " chr1:243800859-243801066 log2=0.023 spread=0.063 121s MYCN chr2:16082132-16082989 log2=-1.145 spread=0.371 121s DNMT3A chr2:25475011-25475213 log2=-1.033 spread=0.468 121s " chr2:25536716-25536891 log2=0.702 spread=0.033 121s MSH2 chr2:47698049-47698231 log2=0.129 spread=0.196 121s MSH6 chr2:48010322-48010657 log2=-0.616 spread=0.644 121s VRK2 chr2:58386820-58386989 log2=0.422 spread=0.287 121s REL chr2:61108881-61109061 log2=1.026 spread=0.559 121s " chr2:61128072-61128256 log2=0.115 spread=0.173 121s " chr2:61145275-61145462 log2=0.014 spread=0.099 121s CGH chr2:82511447-82511627 log2=-0.205 spread=0.129 121s DUSP2 chr2:96810448-96811208 log2=-0.655 spread=0.435 121s MAP3K2 chr2:128081427-128081603 log2=-0.218 spread=0.144 121s " chr2:128095237-128095425 log2=0.215 spread=0.212 121s METTL8 chr2:172291040-172291240 log2=-0.027 spread=0.068 121s VHL chr3:10183481-10183898 log2=-0.155 spread=0.209 121s TGFBR2 chr3:30648320-30648501 log2=0.496 spread=0.504 121s EPHA6 chr3:96585608-96585773 log2=0.045 spread=0.100 121s " chr3:97160189-97160367 log2=-0.050 spread=0.191 121s ATR chr3:142254935-142255077 log2=0.024 spread=0.027 121s " chr3:142286854-142287031 log2=-0.076 spread=0.116 121s GAK chr4:896278-896357 log2=-1.505 spread=1.261 121s FGFR3 chr4:1795608-1795799 log2=-0.750 spread=0.491 121s " chr4:1803044-1803507 log2=-0.073 spread=0.117 121s " chr4:1808792-1809022 log2=0.252 spread=0.108 121s CGH chr4:31509625-31509802 log2=-0.057 spread=0.101 121s EPHA5 chr4:66535226-66535490 log2=-0.681 spread=0.567 121s CGH chr4:163514273-163514460 log2=-0.403 spread=0.271 121s " chr4:178514600-178514725 log2=-0.098 spread=0.096 121s TERT chr5:1293376-1294792 log2=-0.724 spread=0.511 121s TERT Promoter chr5:1294836-1295203 log2=-1.970 spread=0.777 121s " chr5:1295312-1295381 log2=-0.690 spread=0.462 121s CGH chr5:12000292-12000462 log2=-0.342 spread=0.056 121s " chr5:25519857-25520034 log2=-0.015 spread=0.095 121s RICTOR chr5:38958717-38958965 log2=0.011 spread=0.018 121s " chr5:38962338-38962525 log2=0.005 spread=0.019 121s " chr5:38966685-38966862 log2=0.006 spread=0.165 121s " chr5:38978620-38978763 log2=0.040 spread=0.030 121s " chr5:39074133-39074327 log2=-0.158 spread=0.139 121s CGH chr5:42002796-42002944 log2=-0.083 spread=0.071 121s " chr5:51030131-51030313 log2=-0.034 spread=0.192 121s MAP3K1 chr5:56111354-56111894 log2=-1.478 spread=1.050 121s " chr5:56168412-56168590 log2=0.479 spread=0.038 121s CGH chr5:61573234-61573421 log2=-0.072 spread=0.161 121s PIK3R1 chr5:67589487-67589699 log2=0.349 spread=0.264 121s RASA1 chr5:86633752-86633938 log2=0.119 spread=0.112 121s " chr5:86637023-86637168 log2=0.054 spread=0.111 121s " chr5:86642414-86642592 log2=0.254 spread=0.162 121s " chr5:86648912-86649096 log2=0.092 spread=0.062 121s " chr5:86670598-86670778 log2=0.222 spread=0.198 121s MCTP1 chr5:94619518-94620311 log2=-1.064 spread=0.754 121s CGH chr5:99008357-99008506 log2=-0.069 spread=0.390 121s APC chr5:112101965-112102138 log2=0.104 spread=0.217 121s NPM1 chr5:170832371-170832440 log2=-0.398 spread=0.335 121s " chr5:170837567-170837631 log2=0.085 spread=0.349 121s FLT4 chr5:180045962-180046145 log2=-0.884 spread=0.532 121s " chr5:180046203-180046404 log2=-1.531 spread=0.722 121s " chr5:180076418-180076603 log2=-2.224 spread=1.416 121s TPMT chr6:18130863-18131048 log2=0.022 spread=0.099 121s DOM3Z chr6:31938905-31939081 log2=-20.014 spread=0.042 121s " chr6:31939599-31940313 log2=-19.582 spread=0.181 121s STK19 chr6:31940351-31940562 log2=-18.990 spread=0.181 121s " chr6:31946632-31946824 log2=-20.052 spread=0.088 121s " chr6:31947142-31947366 log2=-20.114 spread=0.066 121s " chr6:31948176-31948360 log2=-19.982 spread=0.149 121s " chr6:31948382-31948612 log2=-19.757 spread=0.165 121s " chr6:31948730-31949253 log2=-20.048 spread=0.014 121s NOTCH4 chr6:32163159-32163956 log2=-19.306 spread=0.203 121s " chr6:32164046-32164227 log2=-19.755 spread=0.189 121s " chr6:32164652-32164888 log2=-19.984 spread=0.154 121s " chr6:32165021-32165403 log2=-19.625 spread=0.196 121s " chr6:32166163-32166545 log2=-20.043 spread=0.072 121s " chr6:32166648-32166962 log2=-3.514 spread=5.742 121s " chr6:32168554-32168821 log2=-19.601 spread=0.197 121s " chr6:32168844-32169306 log2=-19.683 spread=0.166 121s " chr6:32169801-32170391 log2=-19.405 spread=0.205 121s " chr6:32171492-32171691 log2=-20.095 spread=0.101 121s " chr6:32171861-32172198 log2=-3.141 spread=7.288 121s " chr6:32178478-32178749 log2=-19.927 spread=0.164 121s " chr6:32180197-32180441 log2=-10.902 spread=9.532 121s " chr6:32180544-32180722 log2=-19.675 spread=0.186 121s " chr6:32180857-32181062 log2=-19.562 spread=0.227 121s " chr6:32181411-32181646 log2=-19.966 spread=0.171 121s " chr6:32181832-32182066 log2=-19.961 spread=0.172 121s " chr6:32182948-32183194 log2=-19.935 spread=0.168 121s " chr6:32184667-32185079 log2=-19.923 spread=0.165 121s " chr6:32185720-32185919 log2=-19.707 spread=0.197 121s " chr6:32187317-32187601 log2=-19.765 spread=0.173 121s " chr6:32187871-32188094 log2=-19.660 spread=0.188 121s " chr6:32188127-32188451 log2=-19.622 spread=0.169 121s " chr6:32188481-32188693 log2=-19.902 spread=0.132 121s " chr6:32188704-32189133 log2=-19.501 spread=0.193 121s " chr6:32190258-32190616 log2=-19.570 spread=0.209 121s " chr6:32190724-32190908 log2=-19.540 spread=0.232 121s " chr6:32191570-32191668 log2=-19.773 spread=0.178 121s " chr6:32191676-32191753 log2=-19.374 spread=0.201 121s FOXP4 chr6:41565467-41565725 log2=-0.259 spread=0.369 121s CCND3 chr6:41909139-41909415 log2=0.039 spread=0.240 121s NFKBIE chr6:44232672-44233479 log2=0.034 spread=0.050 121s CGH chr6:49502071-49502248 log2=-0.093 spread=0.241 121s POU3F2 chr6:99282701-99283145 log2=-1.559 spread=0.536 121s ROS1 chr6:117657131-117657335 log2=-0.377 spread=0.282 121s RSPO3 chr6:127510871-127511054 log2=-0.128 spread=0.105 121s PTPRK chr6:128313744-128313923 log2=0.177 spread=0.190 121s " chr6:128316545-128316699 log2=-0.070 spread=0.050 121s MAP3K5 chr6:137026207-137026289 log2=0.053 spread=0.094 121s ARID1B chr6:157099014-157099324 log2=-0.621 spread=0.410 121s " chr6:157099442-157099991 log2=-0.846 spread=0.504 121s " chr6:157100042-157100634 log2=-1.570 spread=0.825 121s IGF2R chr6:160390228-160390461 log2=-2.660 spread=0.959 121s RAC1 chr7:6414287-6414459 log2=0.162 spread=0.232 121s COL28A1 chr7:7521080-7521229 log2=0.015 spread=0.050 121s FKBP9 chr7:32997131-32997427 log2=-0.367 spread=0.272 121s CGH chr7:52520101-52520286 log2=-1.525 spread=1.176 121s EGFR chr7:55086913-55087091 log2=-0.081 spread=0.180 121s CDK6 chr7:92462355-92462676 log2=0.309 spread=0.198 121s TRRAP chr7:98479525-98479705 log2=0.060 spread=0.087 121s " chr7:98491365-98491541 log2=0.209 spread=0.153 121s " chr7:98493314-98493496 log2=0.389 spread=0.207 121s SMO chr7:128828938-128829041 log2=-2.056 spread=1.317 121s " chr7:128829048-128829358 log2=-0.474 spread=0.315 121s BRAF chr7:140481968-140482374 log2=0.013 spread=0.017 121s " chr7:140484736-140484912 log2=0.081 spread=0.150 121s " chr7:140487956-140488430 log2=-0.089 spread=0.142 121s " chr7:140493336-140493442 log2=-0.340 spread=0.283 121s " chr7:140624315-140624540 log2=-0.497 spread=0.342 121s TNKS chr8:9609998-9610174 log2=0.021 spread=0.082 121s WRN chr8:30925752-30925890 log2=0.120 spread=0.158 121s " chr8:30941153-30941335 log2=0.079 spread=0.294 121s " chr8:30942625-30942802 log2=0.044 spread=0.247 121s " chr8:30947918-30948092 log2=0.151 spread=0.124 121s " chr8:31000128-31000252 log2=-0.099 spread=0.098 121s " chr8:31001006-31001182 log2=-0.002 spread=0.062 121s GPR124 chr8:37654740-37655077 log2=-1.617 spread=1.020 121s " chr8:37698557-37699898 log2=-0.612 spread=0.029 121s ADAM32 chr8:39022338-39022511 log2=-0.103 spread=0.127 121s PRKDC chr8:48827831-48828019 log2=0.018 spread=0.101 121s " chr8:48845528-48845758 log2=0.056 spread=0.196 121s " chr8:48866846-48867043 log2=-0.067 spread=0.097 121s " chr8:48868369-48868526 log2=-0.239 spread=0.174 121s " chr8:48872485-48872722 log2=-0.636 spread=0.464 121s CGH chr8:88503010-88503173 log2=-0.137 spread=0.254 121s FBXO43 chr8:101149746-101149928 log2=0.049 spread=0.082 121s SMARCA2 chr9:2047182-2047507 log2=-0.353 spread=0.368 121s " chr9:2101491-2101640 log2=0.244 spread=0.104 121s JAK2 chr9:5064539-5066968 log2=0.119 spread=0.077 121s " chr9:5067014-5067883 log2=0.146 spread=0.089 121s " chr9:5068184-5070306 log2=0.133 spread=0.092 121s " chr9:5076944-5077152 log2=0.046 spread=0.137 121s PTPRD chr9:8527276-8527424 log2=-0.239 spread=0.237 121s CDKN2A chr9:21973657-21973843 log2=0.148 spread=0.043 121s " chr9:21986759-21986899 log2=0.058 spread=0.175 121s LINGO2 chr9:28505910-28506090 log2=-0.396 spread=0.123 121s CGH chr9:39011524-39011705 log2=-0.205 spread=0.230 121s TRPM3 chr9:73240339-73240498 log2=0.009 spread=0.086 121s NTRK2 chr9:87356721-87356908 log2=0.166 spread=0.265 121s " chr9:87425375-87425452 log2=0.494 spread=0.066 121s PTCH1 chr9:98278654-98278833 log2=-0.560 spread=0.038 121s GPSM1 chr9:139222089-139222248 log2=-4.879 spread=2.118 121s " chr9:139250747-139251032 log2=-0.223 spread=0.273 121s " chr9:139252418-139252703 log2=-0.512 spread=0.264 121s NOTCH1 chr9:139396674-139396979 log2=-0.595 spread=0.295 121s " chr9:139417262-139417677 log2=-0.173 spread=0.191 121s " chr9:139440110-139440287 log2=-1.257 spread=0.500 121s MRC1 chr10:18136438-18136613 log2=-1.945 spread=3.242 121s CGH chr10:22505654-22505837 log2=0.072 spread=0.107 121s RET chr10:43572644-43572830 log2=-1.376 spread=1.092 121s " chr10:43600360-43600677 log2=-0.341 spread=0.222 121s PTEN chr10:89653722-89653907 log2=0.394 spread=0.030 121s " chr10:89685254-89685374 log2=0.244 spread=0.259 121s TNKS2 chr10:93558396-93558682 log2=-0.037 spread=0.051 121s " chr10:93579008-93579121 log2=0.166 spread=0.220 121s SUFU chr10:104263859-104264127 log2=0.269 spread=0.224 121s SHOC2 chr10:112679251-112679451 log2=-0.434 spread=0.304 121s " chr10:112679716-112679934 log2=-0.008 spread=0.028 121s CGH chr10:114003863-114003979 log2=-0.115 spread=0.290 121s WT1 chr11:32456191-32456924 log2=-1.020 spread=0.782 121s CCND1 chr11:69465991-69466081 log2=0.058 spread=0.209 121s GAB2 chr11:78128632-78128813 log2=-0.754 spread=0.552 121s MRE11A chr11:94153219-94153345 log2=-0.021 spread=0.076 121s " chr11:94170270-94170454 log2=0.095 spread=0.285 121s YAP1 chr11:101981528-101981920 log2=-0.331 spread=0.340 121s GUCY1A2 chr11:106888426-106888817 log2=-1.400 spread=0.707 121s ATM chr11:108153392-108153639 log2=0.113 spread=0.136 121s " chr11:108164020-108164234 log2=-0.056 spread=0.040 121s " chr11:108217986-108218135 log2=0.359 spread=0.277 121s MLL chr11:118307178-118307688 log2=-1.486 spread=0.278 121s ARHGAP32 chr11:129003788-129003964 log2=0.053 spread=0.102 121s ETV6 chr12:12018196-12018602 log2=-0.014 spread=0.112 121s KRAS chr12:25362676-25362878 log2=0.128 spread=0.150 121s " chr12:25391004-25391186 log2=0.368 spread=0.128 121s DIP2B chr12:51002431-51002557 log2=0.344 spread=0.188 121s ATF1 chr12:51206642-51206737 log2=0.009 spread=0.024 121s " chr12:51206782-51207287 log2=-0.012 spread=0.101 121s PTPN11 chr12:112856822-112857002 log2=-0.268 spread=0.071 121s CDK8 chr13:26911641-26911828 log2=-0.125 spread=0.167 121s " chr13:26970356-26970536 log2=0.137 spread=0.121 121s FLT3 chr13:28674525-28674708 log2=-2.257 spread=1.745 121s FLT1 chr13:29068850-29069036 log2=-0.914 spread=0.672 121s BRCA2 chr13:32900164-32900478 log2=-0.076 spread=0.110 121s " chr13:32903506-32903689 log2=0.019 spread=0.232 121s " chr13:32918645-32918823 log2=0.158 spread=0.114 121s " chr13:32920899-32921077 log2=0.014 spread=0.247 121s RB1 chr13:48877994-48878223 log2=-0.155 spread=0.250 121s " chr13:48919164-48919369 log2=0.025 spread=0.094 121s " chr13:48921877-48922044 log2=0.106 spread=0.147 121s " chr13:48923026-48923204 log2=0.360 spread=0.016 121s " chr13:48938965-48939147 log2=0.084 spread=0.356 121s " chr13:48941578-48941767 log2=0.238 spread=0.212 121s " chr13:48947486-48947665 log2=0.025 spread=0.126 121s " chr13:48954166-48954251 log2=0.061 spread=0.273 121s " chr13:49037815-49038005 log2=0.063 spread=0.062 121s " chr13:49047410-49047589 log2=0.053 spread=0.170 121s CGH chr13:63016375-63016514 log2=-0.434 spread=0.282 121s " chr13:85527771-85527948 log2=0.007 spread=0.222 121s GPC5 chr13:93003992-93004180 log2=-0.192 spread=0.289 121s IRS2 chr13:110434582-110438324 log2=-0.768 spread=0.252 121s " chr13:110438332-110438432 log2=-3.643 spread=2.325 121s CGH chr14:19220933-19221047 log2=-1.230 spread=1.205 121s " chr14:20291466-20291612 log2=0.697 spread=0.248 121s NKX2-1 chr14:36986433-36987264 log2=-1.444 spread=0.135 121s " chr14:36989195-36989373 log2=0.023 spread=0.162 121s SOS2 chr14:50596601-50596777 log2=0.237 spread=0.146 121s " chr14:50619741-50619923 log2=0.003 spread=0.272 121s " chr14:50697856-50698044 log2=0.277 spread=0.334 121s MAP3K9 chr14:71275435-71275784 log2=-0.385 spread=0.461 121s " chr14:71275790-71275923 log2=-2.815 spread=1.938 121s TSHR chr14:81528428-81528609 log2=0.070 spread=0.244 121s CGH chr15:20870700-20870881 log2=1.102 spread=0.611 121s " chr15:38544477-38545455 log2=-0.348 spread=0.179 121s SPRED1 chr15:38647081-38647177 log2=0.225 spread=0.350 121s LTK chr15:41803310-41803809 log2=-1.956 spread=1.443 121s " chr15:41803968-41804185 log2=-2.202 spread=0.188 121s " chr15:41804261-41804494 log2=-0.402 spread=0.392 121s NTRK3 chr15:88532873-88532940 log2=-0.337 spread=0.240 121s IDH2 chr15:90645456-90645663 log2=-0.951 spread=0.705 121s PDPK1 chr16:2588022-2588201 log2=-1.782 spread=1.082 121s " chr16:2611404-2611585 log2=-5.171 spread=1.963 121s " chr16:2611721-2611943 log2=-11.810 spread=9.172 121s " chr16:2615504-2615725 log2=-20.113 spread=0.007 121s " chr16:2616307-2616486 log2=-20.054 spread=0.072 121s " chr16:2631267-2631414 log2=-20.009 spread=0.061 121s " chr16:2631583-2631734 log2=-20.089 spread=0.090 121s " chr16:2633363-2633616 log2=-20.101 spread=0.089 121s CREBBP chr16:3799553-3799731 log2=0.082 spread=0.155 121s SOCS1 chr16:11349442-11349617 log2=0.110 spread=0.079 121s BOLA2B chr16:29466011-29466278 log2=-18.994 spread=0.172 121s CGH chr16:31526705-31526886 log2=0.057 spread=0.074 121s CYLD chr16:50821673-50821804 log2=0.007 spread=0.188 121s " chr16:50826460-50826650 log2=0.239 spread=0.204 121s CGH chr16:51098326-51098475 log2=-0.578 spread=0.376 121s " chr16:60005694-60005857 log2=-0.366 spread=0.298 121s CDH1 chr16:68771239-68771428 log2=-0.813 spread=0.625 121s CGH chr16:81005282-81005469 log2=-0.134 spread=0.229 121s MAP2K4 chr17:11924151-11924355 log2=-2.093 spread=1.900 121s " chr17:12011053-12011253 log2=0.028 spread=0.232 121s NF1 chr17:29422259-29422433 log2=-0.466 spread=0.381 121s " chr17:29496854-29497046 log2=0.226 spread=0.193 121s RHOT1 chr17:30469629-30469811 log2=-0.034 spread=0.252 121s " chr17:30519172-30519355 log2=0.163 spread=0.289 121s " chr17:30525911-30526093 log2=-0.157 spread=0.046 121s ERBB2 chr17:37856430-37856609 log2=0.221 spread=0.369 121s RPTOR chr17:78896472-78896661 log2=0.267 spread=0.290 121s C18orf56 chr18:657548-657727 log2=-2.406 spread=0.189 121s CDH2 chr18:25756859-25757030 log2=-3.262 spread=0.283 121s KIAA1328 chr18:34512037-34512192 log2=0.081 spread=0.180 121s CGH chr18:42005882-42006049 log2=-0.031 spread=0.038 121s SMAD4 chr18:48575581-48575754 log2=0.224 spread=0.215 121s CGH chr18:58519179-58519311 log2=-0.254 spread=0.214 121s STK11 chr19:1226401-1226680 log2=-0.501 spread=0.149 121s DOT1L chr19:2164127-2164302 log2=-1.071 spread=0.866 121s " chr19:2210351-2210545 log2=-0.860 spread=0.261 121s " chr19:2226130-2227152 log2=-0.113 spread=0.120 121s GNA11 chr19:3094603-3094817 log2=-0.229 spread=0.152 121s GIPC3 chr19:3585549-3585853 log2=-1.516 spread=1.086 121s MAP2K2 chr19:4123728-4123907 log2=-0.004 spread=0.187 121s INSR chr19:7293751-7294042 log2=-2.020 spread=1.538 121s SMARCA4 chr19:11098288-11098630 log2=-0.647 spread=0.206 121s PODNL1 chr19:14063277-14063492 log2=-0.226 spread=0.272 121s NOTCH3 chr19:15288281-15288927 log2=-1.510 spread=0.106 121s " chr19:15311544-15311749 log2=-1.887 spread=1.202 121s JAK3 chr19:17953074-17953444 log2=-0.886 spread=0.423 121s CCNE1 chr19:30303376-30303718 log2=-0.486 spread=0.368 121s CEBPA chr19:33792189-33792764 log2=-0.086 spread=0.251 121s " chr19:33792774-33793010 log2=-1.781 spread=0.127 121s " chr19:33793014-33793356 log2=-0.541 spread=0.385 121s CD79A chr19:42384673-42384848 log2=-1.881 spread=1.227 121s ERCC2 chr19:45866950-45867408 log2=-0.750 spread=0.536 121s " chr19:45873698-45873881 log2=-0.522 spread=0.327 121s BCL2L12 chr19:50173423-50173770 log2=-0.005 spread=0.243 121s SRC chr20:36012507-36012834 log2=-0.373 spread=0.448 121s TOP1 chr20:39657625-39657796 log2=-0.187 spread=0.282 121s PLCG1 chr20:39766233-39766531 log2=-0.105 spread=0.074 121s AURKA chr20:54959250-54959432 log2=0.185 spread=0.183 121s SYCP2 chr20:58505615-58505798 log2=-0.018 spread=0.034 121s ARFRP1 chr20:62339169-62339402 log2=-1.471 spread=0.976 121s RUNX1 chr21:36164379-36164936 log2=0.163 spread=0.307 121s " chr21:36265140-36265318 log2=0.042 spread=0.039 121s CHEK2 chr22:29105918-29106101 log2=0.091 spread=0.114 121s " chr22:29115325-29115465 log2=0.055 spread=0.390 121s SOX10 chr22:38379312-38379815 log2=-0.427 spread=0.308 121s CGH chr22:41487738-41489134 log2=-0.174 spread=0.116 121s EP300 chr22:41562534-41562711 log2=0.160 spread=0.125 121s CRLF2 chrX:1314835-1315039 log2=-21.107 spread=0.037 121s " chrX:1317372-1317594 log2=-21.121 spread=0.049 121s " chrX:1321225-1321435 log2=-20.851 spread=0.196 121s " chrX:1325274-1325530 log2=-21.121 spread=0.011 121s " chrX:1327651-1327830 log2=-21.000 spread=0.029 121s " chrX:1331391-1331576 log2=-21.018 spread=0.052 121s KDM6A chrX:44820507-44820671 log2=-0.582 spread=0.279 121s PAK3 chrX:110435687-110435871 log2=-1.172 spread=0.053 121s STAG2 chrX:123182790-123182965 log2=-0.941 spread=0.228 121s " chrX:123185143-123185290 log2=-1.018 spread=0.062 121s " chrX:123202378-123202544 log2=-0.959 spread=0.103 121s FAM58A chrX:152864353-152864586 log2=-2.437 spread=0.754 121s CGH chrY:4564417-4564597 log2=-1.004 spread=0.063 121s " chrY:9008607-9008759 log2=-1.012 spread=0.097 121s " chrY:13131970-13132039 log2=-1.004 spread=0.074 121s " chrY:19506485-19506650 log2=-1.018 spread=0.034 121s " chrY:21033918-21034071 log2=-1.013 spread=0.057 121s " chrY:28463435-28463622 log2=-0.952 spread=0.039 121s " chrY:28514033-28514208 log2=-0.992 spread=0.026 121s Antitargets: 108 (0.8597%) bins failed filters 121s Wrote build/reference-picard.cnn with 19209 regions 121s 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 122s Processing target: p2-5_5 122s Keeping 6308 of 6646 bins 122s Correcting for GC bias... 122s Correcting for density bias... 122s Processing antitarget: p2-5_5 122s Keeping 12455 of 12563 bins 122s Correcting for GC bias... 122s WARNING: Skipping correction for RepeatMasker bias 122s Targets are 1.18 x more variable than antitargets 122s Wrote build/p2-5_5.cnr with 18763 regions 122s cnvkit.py import-picard picard/p2-9_2.targetcoverage.csv -d build/ 123s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 123s Wrote build/p2-9_2.targetcoverage.cnn with 6646 regions 123s cnvkit.py import-picard picard/p2-9_2.antitargetcoverage.csv -d build/ 124s Wrote build/p2-9_2.antitargetcoverage.cnn with 12563 regions 124s 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 125s Processing target: p2-9_2 125s Keeping 6308 of 6646 bins 125s Correcting for GC bias... 125s Correcting for density bias... 125s Processing antitarget: p2-9_2 126s Keeping 12455 of 12563 bins 126s Correcting for GC bias... 126s WARNING: Skipping correction for RepeatMasker bias 126s Targets are 1.72 x more variable than antitargets 126s Wrote build/p2-9_2.cnr with 18763 regions 126s cnvkit.py import-picard picard/p2-20_1.targetcoverage.csv -d build/ 127s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 127s Wrote build/p2-20_1.targetcoverage.cnn with 6646 regions 127s cnvkit.py import-picard picard/p2-20_1.antitargetcoverage.csv -d build/ 128s Wrote build/p2-20_1.antitargetcoverage.cnn with 12563 regions 128s 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 129s Processing target: p2-20_1 129s Keeping 6308 of 6646 bins 129s Correcting for GC bias... 129s Correcting for density bias... 129s Processing antitarget: p2-20_1 129s Keeping 12455 of 12563 bins 129s Correcting for GC bias... 129s WARNING: Skipping correction for RepeatMasker bias 129s Targets are 1.12 x more variable than antitargets 129s Wrote build/p2-20_1.cnr with 18763 regions 130s cnvkit.py import-picard picard/p2-20_2.targetcoverage.csv -d build/ 131s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 131s Wrote build/p2-20_2.targetcoverage.cnn with 6646 regions 131s cnvkit.py import-picard picard/p2-20_2.antitargetcoverage.csv -d build/ 132s Wrote build/p2-20_2.antitargetcoverage.cnn with 12563 regions 132s 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 133s Processing target: p2-20_2 133s Keeping 6308 of 6646 bins 133s Correcting for GC bias... 133s Correcting for density bias... 133s Processing antitarget: p2-20_2 133s Keeping 12455 of 12563 bins 133s Correcting for GC bias... 133s WARNING: Skipping correction for RepeatMasker bias 133s Targets are 1.10 x more variable than antitargets 133s Wrote build/p2-20_2.cnr with 18763 regions 133s cnvkit.py import-picard picard/p2-20_3.targetcoverage.csv -d build/ 134s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 134s Wrote build/p2-20_3.targetcoverage.cnn with 6646 regions 135s cnvkit.py import-picard picard/p2-20_3.antitargetcoverage.csv -d build/ 135s Wrote build/p2-20_3.antitargetcoverage.cnn with 12563 regions 136s 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 137s Processing target: p2-20_3 137s Keeping 6308 of 6646 bins 137s Correcting for GC bias... 137s Correcting for density bias... 137s Processing antitarget: p2-20_3 137s Keeping 12455 of 12563 bins 137s Correcting for GC bias... 137s WARNING: Skipping correction for RepeatMasker bias 137s Antitargets are 1.09 x more variable than targets 137s Wrote build/p2-20_3.cnr with 18763 regions 137s cnvkit.py import-picard picard/p2-20_4.targetcoverage.csv -d build/ 138s WARNING: Ambiguous gene name 'TERT|TERT Promoter'; using 'TERT Promoter' 138s Wrote build/p2-20_4.targetcoverage.cnn with 6646 regions 138s cnvkit.py import-picard picard/p2-20_4.antitargetcoverage.csv -d build/ 139s Wrote build/p2-20_4.antitargetcoverage.cnn with 12563 regions 139s 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 140s Processing target: p2-20_4 140s Keeping 6308 of 6646 bins 140s Correcting for GC bias... 140s Correcting for density bias... 140s Processing antitarget: p2-20_4 140s Keeping 12455 of 12563 bins 140s Correcting for GC bias... 140s WARNING: Skipping correction for RepeatMasker bias 141s Targets are 1.43 x more variable than antitargets 141s Wrote build/p2-20_4.cnr with 18763 regions 141s 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 142s Processing target: p2-20_5 142s Keeping 6308 of 6646 bins 142s Correcting for GC bias... 142s Correcting for density bias... 142s Processing antitarget: p2-20_5 142s Keeping 12455 of 12563 bins 142s Correcting for GC bias... 142s WARNING: Skipping correction for RepeatMasker bias 142s Targets are 2.50 x more variable than antitargets 142s Wrote build/p2-20_5.cnr with 18763 regions 142s cnvkit.py segment --rscript-path Rscript -p 2 --drop-low-coverage -t .01 build/p2-5_5.cnr -o build/p2-5_5.cns 143s Segmenting with method 'cbs', significance threshold 0.01, in 2 processes 143s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 143s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 143s A typical example is when you are setting values in a column of a DataFrame, like: 143s 143s df["col"][row_indexer] = value 143s 143s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 143s 143s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 143s 143s segments.start.iat[0] = bins_start 143s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 143s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 143s A typical example is when you are setting values in a column of a DataFrame, like: 143s 143s df["col"][row_indexer] = value 143s 143s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 143s 143s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 143s 143s segments.end.iat[-1] = bins_end 143s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 143s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 143s A typical example is when you are setting values in a column of a DataFrame, like: 143s 143s df["col"][row_indexer] = value 143s 143s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 143s 143s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 143s 143s segments.start.iat[0] = bins_start 143s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 143s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 143s A typical example is when you are setting values in a column of a DataFrame, like: 143s 143s df["col"][row_indexer] = value 143s 143s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 143s 143s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 143s 143s segments.end.iat[-1] = bins_end 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.start.iat[0] = bins_start 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.end.iat[-1] = bins_end 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.start.iat[0] = bins_start 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.end.iat[-1] = bins_end 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.start.iat[0] = bins_start 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.end.iat[-1] = bins_end 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.start.iat[0] = bins_start 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.end.iat[-1] = bins_end 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.start.iat[0] = bins_start 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.end.iat[-1] = bins_end 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.start.iat[0] = bins_start 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.end.iat[-1] = bins_end 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.start.iat[0] = bins_start 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.end.iat[-1] = bins_end 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.start.iat[0] = bins_start 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.end.iat[-1] = bins_end 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.start.iat[0] = bins_start 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.end.iat[-1] = bins_end 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.start.iat[0] = bins_start 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.end.iat[-1] = bins_end 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.start.iat[0] = bins_start 144s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 144s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 144s A typical example is when you are setting values in a column of a DataFrame, like: 144s 144s df["col"][row_indexer] = value 144s 144s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 144s 144s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 144s 144s segments.end.iat[-1] = bins_end 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.start.iat[0] = bins_start 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.end.iat[-1] = bins_end 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.start.iat[0] = bins_start 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.end.iat[-1] = bins_end 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.start.iat[0] = bins_start 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.end.iat[-1] = bins_end 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.start.iat[0] = bins_start 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.end.iat[-1] = bins_end 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.start.iat[0] = bins_start 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.end.iat[-1] = bins_end 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.start.iat[0] = bins_start 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.end.iat[-1] = bins_end 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.start.iat[0] = bins_start 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.end.iat[-1] = bins_end 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.start.iat[0] = bins_start 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.end.iat[-1] = bins_end 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.start.iat[0] = bins_start 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.end.iat[-1] = bins_end 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.start.iat[0] = bins_start 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.end.iat[-1] = bins_end 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.start.iat[0] = bins_start 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.end.iat[-1] = bins_end 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.start.iat[0] = bins_start 145s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 145s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 145s A typical example is when you are setting values in a column of a DataFrame, like: 145s 145s df["col"][row_indexer] = value 145s 145s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 145s 145s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 145s 145s segments.end.iat[-1] = bins_end 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.start.iat[0] = bins_start 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.end.iat[-1] = bins_end 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.start.iat[0] = bins_start 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.end.iat[-1] = bins_end 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.start.iat[0] = bins_start 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.end.iat[-1] = bins_end 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.start.iat[0] = bins_start 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.end.iat[-1] = bins_end 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.start.iat[0] = bins_start 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.end.iat[-1] = bins_end 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.start.iat[0] = bins_start 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.end.iat[-1] = bins_end 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.start.iat[0] = bins_start 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.end.iat[-1] = bins_end 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.start.iat[0] = bins_start 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.end.iat[-1] = bins_end 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.start.iat[0] = bins_start 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.end.iat[-1] = bins_end 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.start.iat[0] = bins_start 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.end.iat[-1] = bins_end 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.start.iat[0] = bins_start 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.end.iat[-1] = bins_end 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.start.iat[0] = bins_start 146s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 146s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 146s A typical example is when you are setting values in a column of a DataFrame, like: 146s 146s df["col"][row_indexer] = value 146s 146s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 146s 146s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 146s 146s segments.end.iat[-1] = bins_end 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.start.iat[0] = bins_start 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.end.iat[-1] = bins_end 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.start.iat[0] = bins_start 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.end.iat[-1] = bins_end 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.start.iat[0] = bins_start 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.end.iat[-1] = bins_end 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.start.iat[0] = bins_start 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.end.iat[-1] = bins_end 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.start.iat[0] = bins_start 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.end.iat[-1] = bins_end 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.start.iat[0] = bins_start 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.end.iat[-1] = bins_end 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.start.iat[0] = bins_start 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.end.iat[-1] = bins_end 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.start.iat[0] = bins_start 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.end.iat[-1] = bins_end 147s Dropped 8 / 49 bins on chromosome chrY 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.start.iat[0] = bins_start 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.end.iat[-1] = bins_end 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.start.iat[0] = bins_start 147s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 147s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 147s A typical example is when you are setting values in a column of a DataFrame, like: 147s 147s df["col"][row_indexer] = value 147s 147s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 147s 147s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 147s 147s segments.end.iat[-1] = bins_end 147s Wrote build/p2-5_5.cns with 71 regions 147s cnvkit.py segment --rscript-path Rscript -p 2 --drop-low-coverage -t .01 build/p2-9_2.cnr -o build/p2-9_2.cns 148s Segmenting with method 'cbs', significance threshold 0.01, in 2 processes 148s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 148s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 148s A typical example is when you are setting values in a column of a DataFrame, like: 148s 148s df["col"][row_indexer] = value 148s 148s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 148s 148s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 148s 148s segments.start.iat[0] = bins_start 148s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 148s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 148s A typical example is when you are setting values in a column of a DataFrame, like: 148s 148s df["col"][row_indexer] = value 148s 148s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 148s 148s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 148s 148s segments.end.iat[-1] = bins_end 148s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 148s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 148s A typical example is when you are setting values in a column of a DataFrame, like: 148s 148s df["col"][row_indexer] = value 148s 148s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 148s 148s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 148s 148s segments.start.iat[0] = bins_start 148s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 148s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 148s A typical example is when you are setting values in a column of a DataFrame, like: 148s 148s df["col"][row_indexer] = value 148s 148s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 148s 148s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 148s 148s segments.end.iat[-1] = bins_end 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.start.iat[0] = bins_start 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.end.iat[-1] = bins_end 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.start.iat[0] = bins_start 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.end.iat[-1] = bins_end 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.start.iat[0] = bins_start 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.end.iat[-1] = bins_end 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.start.iat[0] = bins_start 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.end.iat[-1] = bins_end 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.start.iat[0] = bins_start 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.end.iat[-1] = bins_end 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.start.iat[0] = bins_start 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.end.iat[-1] = bins_end 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.start.iat[0] = bins_start 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.end.iat[-1] = bins_end 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.start.iat[0] = bins_start 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.end.iat[-1] = bins_end 149s Dropped 1 / 949 bins on chromosome chr6 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.start.iat[0] = bins_start 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.end.iat[-1] = bins_end 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.start.iat[0] = bins_start 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.end.iat[-1] = bins_end 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.start.iat[0] = bins_start 149s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 149s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 149s A typical example is when you are setting values in a column of a DataFrame, like: 149s 149s df["col"][row_indexer] = value 149s 149s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 149s 149s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 149s 149s segments.end.iat[-1] = bins_end 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.start.iat[0] = bins_start 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.end.iat[-1] = bins_end 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.start.iat[0] = bins_start 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.end.iat[-1] = bins_end 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.start.iat[0] = bins_start 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.end.iat[-1] = bins_end 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.start.iat[0] = bins_start 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.end.iat[-1] = bins_end 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.start.iat[0] = bins_start 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.end.iat[-1] = bins_end 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.start.iat[0] = bins_start 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.end.iat[-1] = bins_end 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.start.iat[0] = bins_start 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.end.iat[-1] = bins_end 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.start.iat[0] = bins_start 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.end.iat[-1] = bins_end 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.start.iat[0] = bins_start 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.end.iat[-1] = bins_end 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.start.iat[0] = bins_start 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.end.iat[-1] = bins_end 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.start.iat[0] = bins_start 150s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 150s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 150s A typical example is when you are setting values in a column of a DataFrame, like: 150s 150s df["col"][row_indexer] = value 150s 150s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 150s 150s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 150s 150s segments.end.iat[-1] = bins_end 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.start.iat[0] = bins_start 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.end.iat[-1] = bins_end 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.start.iat[0] = bins_start 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.end.iat[-1] = bins_end 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.start.iat[0] = bins_start 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.end.iat[-1] = bins_end 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.start.iat[0] = bins_start 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.end.iat[-1] = bins_end 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.start.iat[0] = bins_start 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.end.iat[-1] = bins_end 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.start.iat[0] = bins_start 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.end.iat[-1] = bins_end 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.start.iat[0] = bins_start 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.end.iat[-1] = bins_end 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.start.iat[0] = bins_start 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.end.iat[-1] = bins_end 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.start.iat[0] = bins_start 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.end.iat[-1] = bins_end 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.start.iat[0] = bins_start 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.end.iat[-1] = bins_end 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.start.iat[0] = bins_start 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.end.iat[-1] = bins_end 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.start.iat[0] = bins_start 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.end.iat[-1] = bins_end 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.start.iat[0] = bins_start 151s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 151s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 151s A typical example is when you are setting values in a column of a DataFrame, like: 151s 151s df["col"][row_indexer] = value 151s 151s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 151s 151s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 151s 151s segments.end.iat[-1] = bins_end 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.start.iat[0] = bins_start 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.end.iat[-1] = bins_end 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.start.iat[0] = bins_start 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.end.iat[-1] = bins_end 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.start.iat[0] = bins_start 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.end.iat[-1] = bins_end 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.start.iat[0] = bins_start 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.end.iat[-1] = bins_end 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.start.iat[0] = bins_start 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.end.iat[-1] = bins_end 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.start.iat[0] = bins_start 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.end.iat[-1] = bins_end 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.start.iat[0] = bins_start 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.end.iat[-1] = bins_end 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.start.iat[0] = bins_start 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.end.iat[-1] = bins_end 152s Dropped 27 / 49 bins on chromosome chrY 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.start.iat[0] = bins_start 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.end.iat[-1] = bins_end 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.start.iat[0] = bins_start 152s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 152s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 152s A typical example is when you are setting values in a column of a DataFrame, like: 152s 152s df["col"][row_indexer] = value 152s 152s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 152s 152s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 152s 152s segments.end.iat[-1] = bins_end 152s Wrote build/p2-9_2.cns with 103 regions 152s cnvkit.py segment --rscript-path Rscript -p 2 --drop-low-coverage -t .01 build/p2-20_1.cnr -o build/p2-20_1.cns 153s Segmenting with method 'cbs', significance threshold 0.01, in 2 processes 153s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 153s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 153s A typical example is when you are setting values in a column of a DataFrame, like: 153s 153s df["col"][row_indexer] = value 153s 153s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 153s 153s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 153s 153s segments.start.iat[0] = bins_start 153s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 153s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 153s A typical example is when you are setting values in a column of a DataFrame, like: 153s 153s df["col"][row_indexer] = value 153s 153s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 153s 153s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 153s 153s segments.end.iat[-1] = bins_end 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.start.iat[0] = bins_start 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.end.iat[-1] = bins_end 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.start.iat[0] = bins_start 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.end.iat[-1] = bins_end 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.start.iat[0] = bins_start 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.end.iat[-1] = bins_end 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.start.iat[0] = bins_start 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.end.iat[-1] = bins_end 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.start.iat[0] = bins_start 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.end.iat[-1] = bins_end 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.start.iat[0] = bins_start 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.end.iat[-1] = bins_end 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.start.iat[0] = bins_start 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.end.iat[-1] = bins_end 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.start.iat[0] = bins_start 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.end.iat[-1] = bins_end 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.start.iat[0] = bins_start 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.end.iat[-1] = bins_end 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.start.iat[0] = bins_start 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.end.iat[-1] = bins_end 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.start.iat[0] = bins_start 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.end.iat[-1] = bins_end 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.start.iat[0] = bins_start 154s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 154s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 154s A typical example is when you are setting values in a column of a DataFrame, like: 154s 154s df["col"][row_indexer] = value 154s 154s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 154s 154s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 154s 154s segments.end.iat[-1] = bins_end 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.start.iat[0] = bins_start 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.end.iat[-1] = bins_end 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.start.iat[0] = bins_start 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.end.iat[-1] = bins_end 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.start.iat[0] = bins_start 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.end.iat[-1] = bins_end 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.start.iat[0] = bins_start 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.end.iat[-1] = bins_end 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.start.iat[0] = bins_start 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.end.iat[-1] = bins_end 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.start.iat[0] = bins_start 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.end.iat[-1] = bins_end 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.start.iat[0] = bins_start 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.end.iat[-1] = bins_end 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.start.iat[0] = bins_start 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.end.iat[-1] = bins_end 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.start.iat[0] = bins_start 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.end.iat[-1] = bins_end 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.start.iat[0] = bins_start 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.end.iat[-1] = bins_end 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.start.iat[0] = bins_start 155s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 155s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 155s A typical example is when you are setting values in a column of a DataFrame, like: 155s 155s df["col"][row_indexer] = value 155s 155s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 155s 155s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 155s 155s segments.end.iat[-1] = bins_end 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.start.iat[0] = bins_start 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.end.iat[-1] = bins_end 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.start.iat[0] = bins_start 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.end.iat[-1] = bins_end 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.start.iat[0] = bins_start 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.end.iat[-1] = bins_end 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.start.iat[0] = bins_start 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.end.iat[-1] = bins_end 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.start.iat[0] = bins_start 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.end.iat[-1] = bins_end 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.start.iat[0] = bins_start 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.end.iat[-1] = bins_end 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.start.iat[0] = bins_start 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.end.iat[-1] = bins_end 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.start.iat[0] = bins_start 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.end.iat[-1] = bins_end 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.start.iat[0] = bins_start 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.end.iat[-1] = bins_end 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.start.iat[0] = bins_start 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.end.iat[-1] = bins_end 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.start.iat[0] = bins_start 156s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 156s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 156s A typical example is when you are setting values in a column of a DataFrame, like: 156s 156s df["col"][row_indexer] = value 156s 156s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 156s 156s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 156s 156s segments.end.iat[-1] = bins_end 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.start.iat[0] = bins_start 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.end.iat[-1] = bins_end 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.start.iat[0] = bins_start 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.end.iat[-1] = bins_end 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.start.iat[0] = bins_start 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.end.iat[-1] = bins_end 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.start.iat[0] = bins_start 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.end.iat[-1] = bins_end 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.start.iat[0] = bins_start 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.end.iat[-1] = bins_end 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.start.iat[0] = bins_start 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.end.iat[-1] = bins_end 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.start.iat[0] = bins_start 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.end.iat[-1] = bins_end 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.start.iat[0] = bins_start 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.end.iat[-1] = bins_end 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.start.iat[0] = bins_start 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.end.iat[-1] = bins_end 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.start.iat[0] = bins_start 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.end.iat[-1] = bins_end 157s Dropped 6 / 49 bins on chromosome chrY 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.start.iat[0] = bins_start 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.end.iat[-1] = bins_end 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.start.iat[0] = bins_start 157s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 157s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 157s A typical example is when you are setting values in a column of a DataFrame, like: 157s 157s df["col"][row_indexer] = value 157s 157s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 157s 157s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 157s 157s segments.end.iat[-1] = bins_end 157s Wrote build/p2-20_1.cns with 121 regions 158s cnvkit.py segment --rscript-path Rscript -p 2 --drop-low-coverage -t .01 build/p2-20_2.cnr -o build/p2-20_2.cns 159s Segmenting with method 'cbs', significance threshold 0.01, in 2 processes 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.start.iat[0] = bins_start 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.end.iat[-1] = bins_end 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.start.iat[0] = bins_start 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.end.iat[-1] = bins_end 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.start.iat[0] = bins_start 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.end.iat[-1] = bins_end 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.start.iat[0] = bins_start 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.end.iat[-1] = bins_end 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.start.iat[0] = bins_start 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.end.iat[-1] = bins_end 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.start.iat[0] = bins_start 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.end.iat[-1] = bins_end 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.start.iat[0] = bins_start 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.end.iat[-1] = bins_end 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.start.iat[0] = bins_start 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.end.iat[-1] = bins_end 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.start.iat[0] = bins_start 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.end.iat[-1] = bins_end 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.start.iat[0] = bins_start 159s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 159s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 159s A typical example is when you are setting values in a column of a DataFrame, like: 159s 159s df["col"][row_indexer] = value 159s 159s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 159s 159s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 159s 159s segments.end.iat[-1] = bins_end 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.start.iat[0] = bins_start 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.end.iat[-1] = bins_end 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.start.iat[0] = bins_start 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.end.iat[-1] = bins_end 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.start.iat[0] = bins_start 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.end.iat[-1] = bins_end 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.start.iat[0] = bins_start 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.end.iat[-1] = bins_end 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.start.iat[0] = bins_start 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.end.iat[-1] = bins_end 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.start.iat[0] = bins_start 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.end.iat[-1] = bins_end 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.start.iat[0] = bins_start 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.end.iat[-1] = bins_end 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.start.iat[0] = bins_start 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.end.iat[-1] = bins_end 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.start.iat[0] = bins_start 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.end.iat[-1] = bins_end 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.start.iat[0] = bins_start 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.end.iat[-1] = bins_end 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.start.iat[0] = bins_start 160s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 160s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 160s A typical example is when you are setting values in a column of a DataFrame, like: 160s 160s df["col"][row_indexer] = value 160s 160s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 160s 160s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 160s 160s segments.end.iat[-1] = bins_end 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.start.iat[0] = bins_start 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.end.iat[-1] = bins_end 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.start.iat[0] = bins_start 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.end.iat[-1] = bins_end 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.start.iat[0] = bins_start 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.end.iat[-1] = bins_end 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.start.iat[0] = bins_start 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.end.iat[-1] = bins_end 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.start.iat[0] = bins_start 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.end.iat[-1] = bins_end 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.start.iat[0] = bins_start 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.end.iat[-1] = bins_end 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.start.iat[0] = bins_start 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.end.iat[-1] = bins_end 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.start.iat[0] = bins_start 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.end.iat[-1] = bins_end 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.start.iat[0] = bins_start 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.end.iat[-1] = bins_end 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.start.iat[0] = bins_start 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.end.iat[-1] = bins_end 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.start.iat[0] = bins_start 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.end.iat[-1] = bins_end 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.start.iat[0] = bins_start 161s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 161s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 161s A typical example is when you are setting values in a column of a DataFrame, like: 161s 161s df["col"][row_indexer] = value 161s 161s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 161s 161s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 161s 161s segments.end.iat[-1] = bins_end 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.start.iat[0] = bins_start 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.end.iat[-1] = bins_end 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.start.iat[0] = bins_start 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.end.iat[-1] = bins_end 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.start.iat[0] = bins_start 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.end.iat[-1] = bins_end 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.start.iat[0] = bins_start 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.end.iat[-1] = bins_end 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.start.iat[0] = bins_start 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.end.iat[-1] = bins_end 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.start.iat[0] = bins_start 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.end.iat[-1] = bins_end 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.start.iat[0] = bins_start 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.end.iat[-1] = bins_end 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.start.iat[0] = bins_start 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.end.iat[-1] = bins_end 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.start.iat[0] = bins_start 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.end.iat[-1] = bins_end 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.start.iat[0] = bins_start 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.end.iat[-1] = bins_end 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.start.iat[0] = bins_start 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.end.iat[-1] = bins_end 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.start.iat[0] = bins_start 162s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 162s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 162s A typical example is when you are setting values in a column of a DataFrame, like: 162s 162s df["col"][row_indexer] = value 162s 162s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 162s 162s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 162s 162s segments.end.iat[-1] = bins_end 162s Dropped 7 / 49 bins on chromosome chrY 163s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 163s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 163s A typical example is when you are setting values in a column of a DataFrame, like: 163s 163s df["col"][row_indexer] = value 163s 163s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 163s 163s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 163s 163s segments.start.iat[0] = bins_start 163s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 163s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 163s A typical example is when you are setting values in a column of a DataFrame, like: 163s 163s df["col"][row_indexer] = value 163s 163s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 163s 163s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 163s 163s segments.end.iat[-1] = bins_end 163s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 163s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 163s A typical example is when you are setting values in a column of a DataFrame, like: 163s 163s df["col"][row_indexer] = value 163s 163s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 163s 163s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 163s 163s segments.start.iat[0] = bins_start 163s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 163s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 163s A typical example is when you are setting values in a column of a DataFrame, like: 163s 163s df["col"][row_indexer] = value 163s 163s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 163s 163s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 163s 163s segments.end.iat[-1] = bins_end 163s Wrote build/p2-20_2.cns with 117 regions 163s cnvkit.py segment --rscript-path Rscript -p 2 --drop-low-coverage -t .01 build/p2-20_3.cnr -o build/p2-20_3.cns 164s Segmenting with method 'cbs', significance threshold 0.01, in 2 processes 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.start.iat[0] = bins_start 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.end.iat[-1] = bins_end 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.start.iat[0] = bins_start 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.end.iat[-1] = bins_end 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.start.iat[0] = bins_start 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.end.iat[-1] = bins_end 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.start.iat[0] = bins_start 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.end.iat[-1] = bins_end 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.start.iat[0] = bins_start 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.end.iat[-1] = bins_end 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.start.iat[0] = bins_start 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.end.iat[-1] = bins_end 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.start.iat[0] = bins_start 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.end.iat[-1] = bins_end 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.start.iat[0] = bins_start 164s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 164s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 164s A typical example is when you are setting values in a column of a DataFrame, like: 164s 164s df["col"][row_indexer] = value 164s 164s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 164s 164s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 164s 164s segments.end.iat[-1] = bins_end 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.start.iat[0] = bins_start 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.end.iat[-1] = bins_end 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.start.iat[0] = bins_start 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.end.iat[-1] = bins_end 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.start.iat[0] = bins_start 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.end.iat[-1] = bins_end 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.start.iat[0] = bins_start 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.end.iat[-1] = bins_end 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.start.iat[0] = bins_start 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.end.iat[-1] = bins_end 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.start.iat[0] = bins_start 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.end.iat[-1] = bins_end 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.start.iat[0] = bins_start 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.end.iat[-1] = bins_end 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.start.iat[0] = bins_start 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.end.iat[-1] = bins_end 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.start.iat[0] = bins_start 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.end.iat[-1] = bins_end 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.start.iat[0] = bins_start 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.end.iat[-1] = bins_end 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.start.iat[0] = bins_start 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.end.iat[-1] = bins_end 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.start.iat[0] = bins_start 165s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 165s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 165s A typical example is when you are setting values in a column of a DataFrame, like: 165s 165s df["col"][row_indexer] = value 165s 165s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 165s 165s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 165s 165s segments.end.iat[-1] = bins_end 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.start.iat[0] = bins_start 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.end.iat[-1] = bins_end 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.start.iat[0] = bins_start 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.end.iat[-1] = bins_end 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.start.iat[0] = bins_start 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.end.iat[-1] = bins_end 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.start.iat[0] = bins_start 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.end.iat[-1] = bins_end 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.start.iat[0] = bins_start 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.end.iat[-1] = bins_end 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.start.iat[0] = bins_start 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.end.iat[-1] = bins_end 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.start.iat[0] = bins_start 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.end.iat[-1] = bins_end 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.start.iat[0] = bins_start 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.end.iat[-1] = bins_end 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.start.iat[0] = bins_start 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.end.iat[-1] = bins_end 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.start.iat[0] = bins_start 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.end.iat[-1] = bins_end 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.start.iat[0] = bins_start 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.end.iat[-1] = bins_end 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.start.iat[0] = bins_start 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.end.iat[-1] = bins_end 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.start.iat[0] = bins_start 166s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 166s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 166s A typical example is when you are setting values in a column of a DataFrame, like: 166s 166s df["col"][row_indexer] = value 166s 166s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 166s 166s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 166s 166s segments.end.iat[-1] = bins_end 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.start.iat[0] = bins_start 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.end.iat[-1] = bins_end 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.start.iat[0] = bins_start 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.end.iat[-1] = bins_end 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.start.iat[0] = bins_start 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.end.iat[-1] = bins_end 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.start.iat[0] = bins_start 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.end.iat[-1] = bins_end 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.start.iat[0] = bins_start 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.end.iat[-1] = bins_end 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.start.iat[0] = bins_start 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.end.iat[-1] = bins_end 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.start.iat[0] = bins_start 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.end.iat[-1] = bins_end 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.start.iat[0] = bins_start 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.end.iat[-1] = bins_end 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.start.iat[0] = bins_start 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.end.iat[-1] = bins_end 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.start.iat[0] = bins_start 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.end.iat[-1] = bins_end 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.start.iat[0] = bins_start 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.end.iat[-1] = bins_end 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.start.iat[0] = bins_start 167s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 167s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 167s A typical example is when you are setting values in a column of a DataFrame, like: 167s 167s df["col"][row_indexer] = value 167s 167s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 167s 167s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 167s 167s segments.end.iat[-1] = bins_end 167s Dropped 11 / 49 bins on chromosome chrY 168s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 168s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 168s A typical example is when you are setting values in a column of a DataFrame, like: 168s 168s df["col"][row_indexer] = value 168s 168s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 168s 168s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 168s 168s segments.start.iat[0] = bins_start 168s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 168s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 168s A typical example is when you are setting values in a column of a DataFrame, like: 168s 168s df["col"][row_indexer] = value 168s 168s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 168s 168s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 168s 168s segments.end.iat[-1] = bins_end 168s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 168s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 168s A typical example is when you are setting values in a column of a DataFrame, like: 168s 168s df["col"][row_indexer] = value 168s 168s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 168s 168s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 168s 168s segments.start.iat[0] = bins_start 168s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 168s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 168s A typical example is when you are setting values in a column of a DataFrame, like: 168s 168s df["col"][row_indexer] = value 168s 168s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 168s 168s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 168s 168s segments.end.iat[-1] = bins_end 168s Wrote build/p2-20_3.cns with 64 regions 168s cnvkit.py segment --rscript-path Rscript -p 2 --drop-low-coverage -t .01 build/p2-20_4.cnr -o build/p2-20_4.cns 169s Segmenting with method 'cbs', significance threshold 0.01, in 2 processes 169s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 169s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 169s A typical example is when you are setting values in a column of a DataFrame, like: 169s 169s df["col"][row_indexer] = value 169s 169s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 169s 169s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 169s 169s segments.start.iat[0] = bins_start 169s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 169s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 169s A typical example is when you are setting values in a column of a DataFrame, like: 169s 169s df["col"][row_indexer] = value 169s 169s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 169s 169s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 169s 169s segments.end.iat[-1] = bins_end 169s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 169s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 169s A typical example is when you are setting values in a column of a DataFrame, like: 169s 169s df["col"][row_indexer] = value 169s 169s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 169s 169s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 169s 169s segments.start.iat[0] = bins_start 169s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 169s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 169s A typical example is when you are setting values in a column of a DataFrame, like: 169s 169s df["col"][row_indexer] = value 169s 169s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 169s 169s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 169s 169s segments.end.iat[-1] = bins_end 169s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 169s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 169s A typical example is when you are setting values in a column of a DataFrame, like: 169s 169s df["col"][row_indexer] = value 169s 169s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 169s 169s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 169s 169s segments.start.iat[0] = bins_start 169s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 169s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 169s A typical example is when you are setting values in a column of a DataFrame, like: 169s 169s df["col"][row_indexer] = value 169s 169s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 169s 169s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 169s 169s segments.end.iat[-1] = bins_end 169s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 169s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 169s A typical example is when you are setting values in a column of a DataFrame, like: 169s 169s df["col"][row_indexer] = value 169s 169s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 169s 169s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 169s 169s segments.start.iat[0] = bins_start 169s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 169s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 169s A typical example is when you are setting values in a column of a DataFrame, like: 169s 169s df["col"][row_indexer] = value 169s 169s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 169s 169s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 169s 169s segments.end.iat[-1] = bins_end 169s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 169s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 169s A typical example is when you are setting values in a column of a DataFrame, like: 169s 169s df["col"][row_indexer] = value 169s 169s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 169s 169s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 169s 169s segments.start.iat[0] = bins_start 169s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 169s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 169s A typical example is when you are setting values in a column of a DataFrame, like: 169s 169s df["col"][row_indexer] = value 169s 169s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 169s 169s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 169s 169s segments.end.iat[-1] = bins_end 169s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 169s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 169s A typical example is when you are setting values in a column of a DataFrame, like: 169s 169s df["col"][row_indexer] = value 169s 169s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 169s 169s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 169s 169s segments.start.iat[0] = bins_start 169s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 169s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 169s A typical example is when you are setting values in a column of a DataFrame, like: 169s 169s df["col"][row_indexer] = value 169s 169s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 169s 169s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 169s 169s segments.end.iat[-1] = bins_end 169s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 169s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 169s A typical example is when you are setting values in a column of a DataFrame, like: 169s 169s df["col"][row_indexer] = value 169s 169s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 169s 169s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 169s 169s segments.start.iat[0] = bins_start 169s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 169s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 169s A typical example is when you are setting values in a column of a DataFrame, like: 169s 169s df["col"][row_indexer] = value 169s 169s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 169s 169s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 169s 169s segments.end.iat[-1] = bins_end 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.start.iat[0] = bins_start 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.end.iat[-1] = bins_end 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.start.iat[0] = bins_start 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.end.iat[-1] = bins_end 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.start.iat[0] = bins_start 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.end.iat[-1] = bins_end 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.start.iat[0] = bins_start 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.end.iat[-1] = bins_end 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.start.iat[0] = bins_start 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.end.iat[-1] = bins_end 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.start.iat[0] = bins_start 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.end.iat[-1] = bins_end 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.start.iat[0] = bins_start 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.end.iat[-1] = bins_end 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.start.iat[0] = bins_start 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.end.iat[-1] = bins_end 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.start.iat[0] = bins_start 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.end.iat[-1] = bins_end 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.start.iat[0] = bins_start 170s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 170s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 170s A typical example is when you are setting values in a column of a DataFrame, like: 170s 170s df["col"][row_indexer] = value 170s 170s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 170s 170s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 170s 170s segments.end.iat[-1] = bins_end 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.start.iat[0] = bins_start 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.end.iat[-1] = bins_end 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.start.iat[0] = bins_start 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.end.iat[-1] = bins_end 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.start.iat[0] = bins_start 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.end.iat[-1] = bins_end 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.start.iat[0] = bins_start 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.end.iat[-1] = bins_end 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.start.iat[0] = bins_start 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.end.iat[-1] = bins_end 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.start.iat[0] = bins_start 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.end.iat[-1] = bins_end 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.start.iat[0] = bins_start 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.end.iat[-1] = bins_end 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.start.iat[0] = bins_start 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.end.iat[-1] = bins_end 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.start.iat[0] = bins_start 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.end.iat[-1] = bins_end 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.start.iat[0] = bins_start 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.end.iat[-1] = bins_end 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.start.iat[0] = bins_start 171s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 171s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 171s A typical example is when you are setting values in a column of a DataFrame, like: 171s 171s df["col"][row_indexer] = value 171s 171s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 171s 171s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 171s 171s segments.end.iat[-1] = bins_end 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.start.iat[0] = bins_start 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.end.iat[-1] = bins_end 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.start.iat[0] = bins_start 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.end.iat[-1] = bins_end 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.start.iat[0] = bins_start 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.end.iat[-1] = bins_end 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.start.iat[0] = bins_start 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.end.iat[-1] = bins_end 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.start.iat[0] = bins_start 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.end.iat[-1] = bins_end 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.start.iat[0] = bins_start 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.end.iat[-1] = bins_end 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.start.iat[0] = bins_start 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.end.iat[-1] = bins_end 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.start.iat[0] = bins_start 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.end.iat[-1] = bins_end 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.start.iat[0] = bins_start 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.end.iat[-1] = bins_end 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.start.iat[0] = bins_start 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.end.iat[-1] = bins_end 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.start.iat[0] = bins_start 172s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 172s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 172s A typical example is when you are setting values in a column of a DataFrame, like: 172s 172s df["col"][row_indexer] = value 172s 172s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 172s 172s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 172s 172s segments.end.iat[-1] = bins_end 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.start.iat[0] = bins_start 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.end.iat[-1] = bins_end 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.start.iat[0] = bins_start 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.end.iat[-1] = bins_end 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.start.iat[0] = bins_start 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.end.iat[-1] = bins_end 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.start.iat[0] = bins_start 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.end.iat[-1] = bins_end 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.start.iat[0] = bins_start 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.end.iat[-1] = bins_end 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.start.iat[0] = bins_start 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.end.iat[-1] = bins_end 173s Dropped 8 / 49 bins on chromosome chrY 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.start.iat[0] = bins_start 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.end.iat[-1] = bins_end 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.start.iat[0] = bins_start 173s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 173s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 173s A typical example is when you are setting values in a column of a DataFrame, like: 173s 173s df["col"][row_indexer] = value 173s 173s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 173s 173s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 173s 173s segments.end.iat[-1] = bins_end 173s Wrote build/p2-20_4.cns with 143 regions 173s cnvkit.py segment --rscript-path Rscript -p 2 --drop-low-coverage -t .01 build/p2-20_5.cnr -o build/p2-20_5.cns 174s Segmenting with method 'cbs', significance threshold 0.01, in 2 processes 174s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 174s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 174s A typical example is when you are setting values in a column of a DataFrame, like: 174s 174s df["col"][row_indexer] = value 174s 174s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 174s 174s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 174s 174s segments.start.iat[0] = bins_start 174s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 174s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 174s A typical example is when you are setting values in a column of a DataFrame, like: 174s 174s df["col"][row_indexer] = value 174s 174s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 174s 174s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 174s 174s segments.end.iat[-1] = bins_end 174s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 174s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 174s A typical example is when you are setting values in a column of a DataFrame, like: 174s 174s df["col"][row_indexer] = value 174s 174s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 174s 174s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 174s 174s segments.start.iat[0] = bins_start 174s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 174s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 174s A typical example is when you are setting values in a column of a DataFrame, like: 174s 174s df["col"][row_indexer] = value 174s 174s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 174s 174s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 174s 174s segments.end.iat[-1] = bins_end 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.start.iat[0] = bins_start 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.end.iat[-1] = bins_end 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.start.iat[0] = bins_start 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.end.iat[-1] = bins_end 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.start.iat[0] = bins_start 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.end.iat[-1] = bins_end 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.start.iat[0] = bins_start 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.end.iat[-1] = bins_end 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.start.iat[0] = bins_start 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.end.iat[-1] = bins_end 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.start.iat[0] = bins_start 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.end.iat[-1] = bins_end 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.start.iat[0] = bins_start 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.end.iat[-1] = bins_end 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.start.iat[0] = bins_start 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.end.iat[-1] = bins_end 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.start.iat[0] = bins_start 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.end.iat[-1] = bins_end 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.start.iat[0] = bins_start 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.end.iat[-1] = bins_end 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.start.iat[0] = bins_start 175s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 175s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 175s A typical example is when you are setting values in a column of a DataFrame, like: 175s 175s df["col"][row_indexer] = value 175s 175s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 175s 175s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 175s 175s segments.end.iat[-1] = bins_end 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.start.iat[0] = bins_start 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.end.iat[-1] = bins_end 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.start.iat[0] = bins_start 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.end.iat[-1] = bins_end 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.start.iat[0] = bins_start 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.end.iat[-1] = bins_end 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.start.iat[0] = bins_start 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.end.iat[-1] = bins_end 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.start.iat[0] = bins_start 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.end.iat[-1] = bins_end 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.start.iat[0] = bins_start 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.end.iat[-1] = bins_end 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.start.iat[0] = bins_start 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.end.iat[-1] = bins_end 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.start.iat[0] = bins_start 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.end.iat[-1] = bins_end 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.start.iat[0] = bins_start 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.end.iat[-1] = bins_end 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.start.iat[0] = bins_start 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.end.iat[-1] = bins_end 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.start.iat[0] = bins_start 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.end.iat[-1] = bins_end 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.start.iat[0] = bins_start 176s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 176s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 176s A typical example is when you are setting values in a column of a DataFrame, like: 176s 176s df["col"][row_indexer] = value 176s 176s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 176s 176s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 176s 176s segments.end.iat[-1] = bins_end 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.start.iat[0] = bins_start 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.end.iat[-1] = bins_end 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.start.iat[0] = bins_start 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.end.iat[-1] = bins_end 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.start.iat[0] = bins_start 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.end.iat[-1] = bins_end 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.start.iat[0] = bins_start 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.end.iat[-1] = bins_end 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.start.iat[0] = bins_start 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.end.iat[-1] = bins_end 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.start.iat[0] = bins_start 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.end.iat[-1] = bins_end 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.start.iat[0] = bins_start 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.end.iat[-1] = bins_end 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.start.iat[0] = bins_start 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.end.iat[-1] = bins_end 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.start.iat[0] = bins_start 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.end.iat[-1] = bins_end 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.start.iat[0] = bins_start 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.end.iat[-1] = bins_end 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.start.iat[0] = bins_start 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.end.iat[-1] = bins_end 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.start.iat[0] = bins_start 177s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 177s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 177s A typical example is when you are setting values in a column of a DataFrame, like: 177s 177s df["col"][row_indexer] = value 177s 177s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 177s 177s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 177s 177s segments.end.iat[-1] = bins_end 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.start.iat[0] = bins_start 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.end.iat[-1] = bins_end 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.start.iat[0] = bins_start 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.end.iat[-1] = bins_end 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.start.iat[0] = bins_start 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.end.iat[-1] = bins_end 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.start.iat[0] = bins_start 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.end.iat[-1] = bins_end 178s Smoothing overshot at 1 / 123 indices: (-0.30060035404086394, 0.32508880185194683) vs. original (-0.363302, 0.311218) 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.start.iat[0] = bins_start 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.end.iat[-1] = bins_end 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.start.iat[0] = bins_start 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.end.iat[-1] = bins_end 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.start.iat[0] = bins_start 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.end.iat[-1] = bins_end 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.start.iat[0] = bins_start 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.end.iat[-1] = bins_end 178s Dropped 6 / 49 bins on chromosome chrY 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.start.iat[0] = bins_start 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.end.iat[-1] = bins_end 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.start.iat[0] = bins_start 178s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 178s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 178s A typical example is when you are setting values in a column of a DataFrame, like: 178s 178s df["col"][row_indexer] = value 178s 178s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 178s 178s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 178s 178s segments.end.iat[-1] = bins_end 178s Wrote build/p2-20_5.cns with 120 regions 178s 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 180s Wrote p2-5_5-scatter.pdf 180s 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 181s Smoothing overshot at 3 / 97 indices: (-19.90010586862171, -3.3099069223720505) vs. original (-19.294, -0.16431) 182s Wrote p2-9_2-scatter.pdf 182s cnvkit.py scatter -s build/p2-9_2.cns build/p2-9_2.cnr -c chr1 -t -o p2-9_2-chr1-scatter.pdf 183s Showing 1480 probes and 0 selected genes in region chr1 183s Wrote p2-9_2-chr1-scatter.pdf 183s cnvkit.py scatter -s build/p2-9_2.cns build/p2-9_2.cnr -c chr21 -t -o p2-9_2-chr21-scatter.pdf 184s Showing 201 probes and 0 selected genes in region chr21 184s Wrote p2-9_2-chr21-scatter.pdf 184s 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 185s Showing 330 probes and 13 selected genes in region chr9:149999-45000000 185s Wrote p2-9_2-chr9p-scatter.pdf 186s 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 186s Showing 179 probes and 2 selected genes in region chr9:0-14504268.0 187s Wrote p2-9_2-SMARCA2-PTPRD-scatter.pdf 187s 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 188s Detected file format: bed 188s Showing 41 probes and 1 selected genes in region chr9:2000000-4000000 188s Showing 53 probes and 1 selected genes in region chr9:8000000-12000000 188s 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 188s /usr/bin/pdfunite 188s cnvkit.py diagram -y build/p2-5_5.cnr -o p2-5_5-diagram.pdf 189s Treating sample p2-5_5 as female 193s Wrote p2-5_5-diagram.pdf 193s cnvkit.py diagram -y build/p2-9_2.cnr -o p2-9_2-diagram.pdf 194s Treating sample p2-9_2 as female 198s Wrote p2-9_2-diagram.pdf 198s cnvkit.py diagram -y build/p2-20_1.cnr -o p2-20_1-diagram.pdf 199s Treating sample p2-20_1 as female 203s Wrote p2-20_1-diagram.pdf 204s cnvkit.py diagram -y build/p2-20_2.cnr -o p2-20_2-diagram.pdf 204s Treating sample p2-20_2 as female 209s Wrote p2-20_2-diagram.pdf 209s cnvkit.py diagram -y --segment=build/p2-5_5.cns -o p2-5_5-cbs-diagram.pdf 210s Treating sample p2-5_5 as female 210s Wrote p2-5_5-cbs-diagram.pdf 210s cnvkit.py diagram -y --segment=build/p2-9_2.cns -o p2-9_2-cbs-diagram.pdf 211s Treating sample p2-9_2 as female 211s Wrote p2-9_2-cbs-diagram.pdf 211s cnvkit.py diagram -y --segment=build/p2-20_1.cns -o p2-20_1-cbs-diagram.pdf 212s Treating sample p2-20_1 as female 212s Wrote p2-20_1-cbs-diagram.pdf 212s cnvkit.py diagram -y --segment=build/p2-20_2.cns -o p2-20_2-cbs-diagram.pdf 213s Treating sample p2-20_2 as female 213s Wrote p2-20_2-cbs-diagram.pdf 214s 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 214s Treating sample p2-5_5 as female 217s Wrote p2-5_5-both-diagram.pdf 217s 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 218s Treating sample p2-9_2 as female 221s Wrote p2-9_2-both-diagram.pdf 221s 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 222s Treating sample p2-20_1 as female 225s Wrote p2-20_1-both-diagram.pdf 225s 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 226s Treating sample p2-20_2 as female 228s Wrote p2-20_2-both-diagram.pdf 229s 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 229s /usr/bin/pdfunite 229s 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 230s Treating sample p2-5_5 as female 230s Treating sample p2-9_2 as female 230s Treating sample p2-20_1 as female 230s Treating sample p2-20_2 as female 230s Treating sample p2-20_3 as female 230s Treating sample p2-20_4 as female 230s Treating sample p2-20_5 as female 230s Wrote heatmap-picard.pdf 231s cnvkit.py breaks build/p2-9_2.cnr build/p2-9_2.cns -o p2-9_2-breaks.txt 231s Found 14 gene breakpoints 232s Wrote p2-9_2-breaks.txt 232s cnvkit.py genemetrics -y -m 2 -s build/p2-9_2.cns build/p2-9_2.cnr -o p2-9_2-genemetrics.txt 233s Treating sample p2-9_2 as female 233s Found 323 gene-level gains and losses 233s Wrote p2-9_2-genemetrics.txt 233s 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 234s Wrote gender-picard.txt 235s 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 237s Wrote p2-all.cdt 237s 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 239s Wrote p2-all-jtv.txt 240s 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 240s Wrote p2-all.seg 241s cnvkit.py export nexus-basic build/p2-9_2.cnr -o p2-9_2.nexus 242s Wrote p2-9_2.nexus 242s cnvkit.py export nexus-ogt build/p2-9_2.cnr formats/na12878_na12882_mix.vcf -o p2-9_2.nexus-ogt 243s Selected test sample NA12882 and control sample NA12878 243s Loaded 3654 records; skipped: 514 somatic, 394 depth 243s Kept 2631 heterozygous of 3654 VCF records 244s Placed 705 variants into 18763 bins 244s Wrote p2-9_2.nexus-ogt 244s cnvkit.py call build/p2-5_5.cns -y -m clonal --purity 0.65 -o build/p2-5_5.call.cns 245s Treating sample p2-5_5 as female 245s Rescaling sample with purity 0.65, ploidy 2 245s Wrote build/p2-5_5.call.cns with 71 regions 245s cnvkit.py call build/p2-9_2.cns -y -m clonal --purity 0.65 -o build/p2-9_2.call.cns 246s Treating sample p2-9_2 as female 246s Rescaling sample with purity 0.65, ploidy 2 246s Wrote build/p2-9_2.call.cns with 103 regions 246s cnvkit.py call build/p2-20_1.cns -y -m clonal --purity 0.65 -o build/p2-20_1.call.cns 247s Treating sample p2-20_1 as female 247s Rescaling sample with purity 0.65, ploidy 2 247s Wrote build/p2-20_1.call.cns with 121 regions 247s cnvkit.py call build/p2-20_2.cns -y -m clonal --purity 0.65 -o build/p2-20_2.call.cns 248s Treating sample p2-20_2 as female 248s Rescaling sample with purity 0.65, ploidy 2 248s Wrote build/p2-20_2.call.cns with 117 regions 248s cnvkit.py call build/p2-20_3.cns -y -m clonal --purity 0.65 -o build/p2-20_3.call.cns 249s Treating sample p2-20_3 as female 249s Rescaling sample with purity 0.65, ploidy 2 249s Wrote build/p2-20_3.call.cns with 64 regions 249s cnvkit.py call build/p2-20_4.cns -y -m clonal --purity 0.65 -o build/p2-20_4.call.cns 250s Treating sample p2-20_4 as female 250s Rescaling sample with purity 0.65, ploidy 2 250s Wrote build/p2-20_4.call.cns with 143 regions 250s cnvkit.py call build/p2-20_5.cns -y -m clonal --purity 0.65 -o build/p2-20_5.call.cns 251s Treating sample p2-20_5 as female 251s Rescaling sample with purity 0.65, ploidy 2 251s Wrote build/p2-20_5.call.cns with 120 regions 251s 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 252s Treating sample p2-5_5.call as female 252s Treating sample p2-9_2.call as female 252s Treating sample p2-20_1.call as female 252s Treating sample p2-20_2.call as female 252s Treating sample p2-20_3.call as female 252s Treating sample p2-20_4.call as female 252s Treating sample p2-20_5.call as female 252s Wrote p2-all.bed 252s 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 253s Treating sample p2-9_2.call as female 253s Wrote p2-9_2.vcf 253s cnvkit.py export theta build/p2-9_2.cns -r build/reference-picard.cnn -o p2-9_2.theta2.input 254s Wrote p2-9_2.theta2.input 255s cnvkit.py segmetrics -s build/p2-9_2.cns build/p2-9_2.cnr -o p2-9_2-segmetrics.cns \ 255s --mean --median --mode --t-test \ 255s --stdev --mad --mse --iqr --bivar \ 255s --ci --pi --sem --smooth-bootstrap 256s Wrote p2-9_2-segmetrics.cns with 103 regions 256s cnvkit.py segmetrics -s build/p2-5_5.cns build/p2-5_5.cnr -o p2-5_5-segmetrics.cns \ 256s --ci -b 50 -a 0.5 257s Wrote p2-5_5-segmetrics.cns with 71 regions 257s cnvkit.py metrics build/p2-5_5.cnr -s build/p2-5_5.cns -o p2-5_5-metrics.tsv 258s Wrote p2-5_5-metrics.tsv 258s cnvkit.py metrics build/p2-9_2.cnr -s build/p2-9_2.cns -o p2-9_2-metrics.tsv 259s Wrote p2-9_2-metrics.tsv 259s 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 260s Wrote p2-20-metrics.tsv 260s PASS 261s autopkgtest [11:55:56]: test run-unit-test: -----------------------] 261s autopkgtest [11:55:56]: test run-unit-test: - - - - - - - - - - results - - - - - - - - - - 261s run-unit-test PASS 261s autopkgtest [11:55:56]: test pybuild-autopkgtest: preparing testbed 283s Creating nova instance adt-resolute-amd64-cnvkit-20251117-115135-juju-7f2275-prod-proposed-migration-environment-15-235995ba-c25b-4ed2-b16a-d68505032711 from image adt/ubuntu-resolute-amd64-server-20251117.img (UUID 9762b0cc-7c5b-4854-acd5-cc74ad0de8c6)... 328s autopkgtest [11:57:03]: testbed dpkg architecture: amd64 329s autopkgtest [11:57:04]: testbed apt version: 3.1.11 329s autopkgtest [11:57:04]: @@@@@@@@@@@@@@@@@@@@ test bed setup 329s autopkgtest [11:57:04]: testbed release detected to be: resolute 330s autopkgtest [11:57:05]: updating testbed package index (apt update) 330s Get:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease [87.8 kB] 330s Hit:2 http://ftpmaster.internal/ubuntu resolute InRelease 330s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease 330s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease 330s Get:5 http://ftpmaster.internal/ubuntu resolute-proposed/universe Sources [868 kB] 330s Get:6 http://ftpmaster.internal/ubuntu resolute-proposed/main Sources [81.1 kB] 330s Get:7 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse Sources [22.9 kB] 330s Get:8 http://ftpmaster.internal/ubuntu resolute-proposed/restricted Sources [9848 B] 330s Get:9 http://ftpmaster.internal/ubuntu resolute-proposed/main amd64 Packages [159 kB] 330s Get:10 http://ftpmaster.internal/ubuntu resolute-proposed/main i386 Packages [118 kB] 330s Get:11 http://ftpmaster.internal/ubuntu resolute-proposed/main amd64 c-n-f Metadata [3096 B] 330s Get:12 http://ftpmaster.internal/ubuntu resolute-proposed/restricted i386 Packages [3744 B] 330s Get:13 http://ftpmaster.internal/ubuntu resolute-proposed/restricted amd64 Packages [64.6 kB] 330s Get:14 http://ftpmaster.internal/ubuntu resolute-proposed/restricted amd64 c-n-f Metadata [336 B] 330s Get:15 http://ftpmaster.internal/ubuntu resolute-proposed/universe amd64 Packages [607 kB] 330s Get:16 http://ftpmaster.internal/ubuntu resolute-proposed/universe i386 Packages [279 kB] 331s Get:17 http://ftpmaster.internal/ubuntu resolute-proposed/universe amd64 c-n-f Metadata [21.2 kB] 331s Get:18 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse i386 Packages [6516 B] 331s Get:19 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse amd64 Packages [13.4 kB] 331s Get:20 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse amd64 c-n-f Metadata [680 B] 332s Fetched 2346 kB in 1s (2445 kB/s) 333s Reading package lists... 333s Hit:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease 333s Hit:2 http://ftpmaster.internal/ubuntu resolute InRelease 333s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease 333s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease 334s Reading package lists... 334s Reading package lists... 334s Building dependency tree... 334s Reading state information... 334s Calculating upgrade... 334s The following packages will be upgraded: 334s libpython3-stdlib python3 python3-minimal usbutils 334s 4 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 334s Need to get 146 kB of archives. 334s After this operation, 0 B of additional disk space will be used. 334s Get:1 http://ftpmaster.internal/ubuntu resolute-proposed/main amd64 python3-minimal amd64 3.13.7-2 [27.8 kB] 334s Get:2 http://ftpmaster.internal/ubuntu resolute-proposed/main amd64 python3 amd64 3.13.7-2 [23.9 kB] 334s Get:3 http://ftpmaster.internal/ubuntu resolute-proposed/main amd64 libpython3-stdlib amd64 3.13.7-2 [10.6 kB] 334s Get:4 http://ftpmaster.internal/ubuntu resolute/main amd64 usbutils amd64 1:019-1 [83.9 kB] 334s dpkg-preconfigure: unable to re-open stdin: No such file or directory 334s Fetched 146 kB in 0s (0 B/s) 334s (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 ... 83372 files and directories currently installed.) 334s Preparing to unpack .../python3-minimal_3.13.7-2_amd64.deb ... 334s Unpacking python3-minimal (3.13.7-2) over (3.13.7-1) ... 335s Setting up python3-minimal (3.13.7-2) ... 335s (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 ... 83372 files and directories currently installed.) 335s Preparing to unpack .../python3_3.13.7-2_amd64.deb ... 335s running python pre-rtupdate hooks for python3.13... 335s Unpacking python3 (3.13.7-2) over (3.13.7-1) ... 335s Preparing to unpack .../libpython3-stdlib_3.13.7-2_amd64.deb ... 335s Unpacking libpython3-stdlib:amd64 (3.13.7-2) over (3.13.7-1) ... 335s Preparing to unpack .../usbutils_1%3a019-1_amd64.deb ... 335s Unpacking usbutils (1:019-1) over (1:018-2) ... 335s Setting up usbutils (1:019-1) ... 335s Setting up libpython3-stdlib:amd64 (3.13.7-2) ... 335s Setting up python3 (3.13.7-2) ... 335s running python rtupdate hooks for python3.13... 335s running python post-rtupdate hooks for python3.13... 335s Processing triggers for man-db (2.13.1-1) ... 336s autopkgtest [11:57:11]: upgrading testbed (apt dist-upgrade and autopurge) 336s Reading package lists... 336s Building dependency tree... 336s Reading state information... 336s Calculating upgrade... 336s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 336s Reading package lists... 336s Building dependency tree... 336s Reading state information... 336s Solving dependencies... 336s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 339s Reading package lists... 339s Building dependency tree... 339s Reading state information... 339s Solving dependencies... 339s The following NEW packages will be installed: 339s autoconf automake autopoint autotools-dev blt build-essential cnvkit cpp 339s cpp-15 cpp-15-x86-64-linux-gnu cpp-x86-64-linux-gnu cython3 debhelper 339s debugedit dh-autoreconf dh-python dh-strip-nondeterminism dwz fontconfig 339s fontconfig-config fonts-dejavu-core fonts-dejavu-mono fonts-lyx 339s fonts-urw-base35 g++ g++-15 g++-15-x86-64-linux-gnu g++-x86-64-linux-gnu gcc 339s gcc-15 gcc-15-x86-64-linux-gnu gcc-x86-64-linux-gnu gettext intltool-debian 339s libarchive-zip-perl libasan8 libblas3 libcairo2 libcc1-0 libdatrie1 339s libdebhelper-perl libdeflate0 libfile-stripnondeterminism-perl 339s libfontconfig1 libfontenc1 libgcc-15-dev libgfortran5 libgomp1 339s libgpgmepp6t64 libgraphite2-3 libharfbuzz0b libhts3t64 libhtscodecs2 339s libhwasan0 libice6 libimagequant0 libisl23 libitm1 libjbig0 libjpeg-turbo8 339s libjpeg8 liblapack3 liblcms2-2 liblerc4 liblsan0 libmpc3 libopenjp2-7 339s libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 libpaper-utils 339s libpaper2 libpixman-1-0 libpoppler147 libpython3.14-minimal 339s libpython3.14-stdlib libqhull-r8.0 libquadmath0 libraqm0 libsharpyuv0 libsm6 339s libstdc++-15-dev libtcl8.6 libthai-data libthai0 libtiff6 libtk8.6 libtool 339s libtsan2 libubsan1 libwebp7 libwebpdemux2 libwebpmux3 libxcb-render0 339s libxcb-shm0 libxft2 libxrender1 libxslt1.1 libxss1 libxt6t64 libzopfli1 m4 339s po-debconf poppler-utils pybuild-plugin-autopkgtest pybuild-plugin-pyproject 339s python-matplotlib-data python3-all python3-biopython python3-brotli 339s python3-build python3-cairo python3-charset-normalizer python3-contourpy 339s python3-cycler python3-decorator python3-fonttools python3-freetype 339s python3-fs python3-iniconfig python3-installer python3-joblib 339s python3-kiwisolver python3-lxml python3-lz4 python3-matplotlib 339s python3-mpmath python3-networkx python3-numpy python3-numpy-dev 339s python3-pandas python3-pandas-lib python3-pil python3-pil.imagetk 339s python3-platformdirs python3-pluggy python3-pomegranate python3-pyfaidx 339s python3-pyproject-hooks python3-pysam python3-pytest python3-pytz 339s python3-reportlab python3-rlpycairo python3-scipy python3-sklearn 339s python3-sklearn-lib python3-sympy python3-threadpoolctl python3-tk 339s python3-ufolib2 python3-wheel python3-zopfli python3.13-tk python3.14 339s python3.14-minimal python3.14-tk r-base-core r-bioc-biocgenerics 339s r-bioc-dnacopy sgml-base tk8.6-blt2.5 unicode-data unzip w3c-sgml-lib 339s x11-common xdg-utils xfonts-encodings xfonts-utils xml-core zip 339s 0 upgraded, 171 newly installed, 0 to remove and 0 not upgraded. 339s Need to get 273 MB of archives. 339s After this operation, 1146 MB of additional disk space will be used. 339s Get:1 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-numpy-dev amd64 1:2.2.4+ds-1ubuntu1 [147 kB] 339s Get:2 http://ftpmaster.internal/ubuntu resolute/main amd64 libblas3 amd64 3.12.1-7 [259 kB] 339s Get:3 http://ftpmaster.internal/ubuntu resolute/main amd64 libgfortran5 amd64 15.2.0-7ubuntu1 [939 kB] 339s Get:4 http://ftpmaster.internal/ubuntu resolute/main amd64 liblapack3 amd64 3.12.1-7 [2739 kB] 339s Get:5 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-numpy amd64 1:2.2.4+ds-1ubuntu1 [5377 kB] 339s Get:6 http://ftpmaster.internal/ubuntu resolute/main amd64 libpython3.14-minimal amd64 3.14.0-4 [906 kB] 339s Get:7 http://ftpmaster.internal/ubuntu resolute/main amd64 python3.14-minimal amd64 3.14.0-4 [2559 kB] 340s Get:8 http://ftpmaster.internal/ubuntu resolute/main amd64 m4 amd64 1.4.20-2 [217 kB] 340s Get:9 http://ftpmaster.internal/ubuntu resolute/main amd64 autoconf all 2.72-3.1ubuntu1 [384 kB] 340s Get:10 http://ftpmaster.internal/ubuntu resolute/main amd64 autotools-dev all 20240727.1 [43.4 kB] 340s Get:11 http://ftpmaster.internal/ubuntu resolute/main amd64 automake all 1:1.18.1-2 [581 kB] 340s Get:12 http://ftpmaster.internal/ubuntu resolute/main amd64 autopoint all 0.23.2-1 [620 kB] 340s Get:13 http://ftpmaster.internal/ubuntu resolute/main amd64 libtcl8.6 amd64 8.6.17+dfsg-1 [1036 kB] 340s Get:14 http://ftpmaster.internal/ubuntu resolute/main amd64 fonts-dejavu-mono all 2.37-8 [502 kB] 340s Get:15 http://ftpmaster.internal/ubuntu resolute/main amd64 fonts-dejavu-core all 2.37-8 [835 kB] 340s Get:16 http://ftpmaster.internal/ubuntu resolute/main amd64 libfontenc1 amd64 1:1.1.8-1build1 [14.0 kB] 340s Get:17 http://ftpmaster.internal/ubuntu resolute/main amd64 x11-common all 1:7.7+24ubuntu1 [22.4 kB] 340s Get:18 http://ftpmaster.internal/ubuntu resolute/main amd64 xfonts-encodings all 1:1.0.5-0ubuntu2 [578 kB] 340s Get:19 http://ftpmaster.internal/ubuntu resolute/main amd64 xfonts-utils amd64 1:7.7+7 [97.1 kB] 340s Get:20 http://ftpmaster.internal/ubuntu resolute/main amd64 fonts-urw-base35 all 20200910-8 [11.0 MB] 340s Get:21 http://ftpmaster.internal/ubuntu resolute/main amd64 fontconfig-config amd64 2.15.0-2.3ubuntu1 [38.0 kB] 340s Get:22 http://ftpmaster.internal/ubuntu resolute/main amd64 libfontconfig1 amd64 2.15.0-2.3ubuntu1 [141 kB] 340s Get:23 http://ftpmaster.internal/ubuntu resolute/main amd64 libxrender1 amd64 1:0.9.12-1 [19.8 kB] 340s Get:24 http://ftpmaster.internal/ubuntu resolute/main amd64 libxft2 amd64 2.3.6-1build1 [45.3 kB] 340s Get:25 http://ftpmaster.internal/ubuntu resolute/main amd64 libxss1 amd64 1:1.2.3-1build3 [7204 B] 340s Get:26 http://ftpmaster.internal/ubuntu resolute/main amd64 libtk8.6 amd64 8.6.17-1 [823 kB] 340s Get:27 http://ftpmaster.internal/ubuntu resolute/main amd64 tk8.6-blt2.5 amd64 2.5.3+dfsg-8 [694 kB] 340s Get:28 http://ftpmaster.internal/ubuntu resolute/main amd64 blt amd64 2.5.3+dfsg-8 [4824 B] 340s Get:29 http://ftpmaster.internal/ubuntu resolute/main amd64 libisl23 amd64 0.27-1 [685 kB] 340s Get:30 http://ftpmaster.internal/ubuntu resolute/main amd64 libmpc3 amd64 1.3.1-2 [54.8 kB] 340s Get:31 http://ftpmaster.internal/ubuntu resolute/main amd64 cpp-15-x86-64-linux-gnu amd64 15.2.0-7ubuntu1 [12.9 MB] 340s Get:32 http://ftpmaster.internal/ubuntu resolute/main amd64 cpp-15 amd64 15.2.0-7ubuntu1 [1026 B] 340s Get:33 http://ftpmaster.internal/ubuntu resolute/main amd64 cpp-x86-64-linux-gnu amd64 4:15.2.0-4ubuntu1 [5746 B] 340s Get:34 http://ftpmaster.internal/ubuntu resolute/main amd64 cpp amd64 4:15.2.0-4ubuntu1 [22.4 kB] 340s Get:35 http://ftpmaster.internal/ubuntu resolute/main amd64 libcc1-0 amd64 15.2.0-7ubuntu1 [47.4 kB] 340s Get:36 http://ftpmaster.internal/ubuntu resolute/main amd64 libgomp1 amd64 15.2.0-7ubuntu1 [151 kB] 340s Get:37 http://ftpmaster.internal/ubuntu resolute/main amd64 libitm1 amd64 15.2.0-7ubuntu1 [29.7 kB] 340s Get:38 http://ftpmaster.internal/ubuntu resolute/main amd64 libasan8 amd64 15.2.0-7ubuntu1 [3071 kB] 340s Get:39 http://ftpmaster.internal/ubuntu resolute/main amd64 liblsan0 amd64 15.2.0-7ubuntu1 [1360 kB] 340s Get:40 http://ftpmaster.internal/ubuntu resolute/main amd64 libtsan2 amd64 15.2.0-7ubuntu1 [2757 kB] 340s Get:41 http://ftpmaster.internal/ubuntu resolute/main amd64 libubsan1 amd64 15.2.0-7ubuntu1 [1210 kB] 340s Get:42 http://ftpmaster.internal/ubuntu resolute/main amd64 libhwasan0 amd64 15.2.0-7ubuntu1 [1685 kB] 340s Get:43 http://ftpmaster.internal/ubuntu resolute/main amd64 libquadmath0 amd64 15.2.0-7ubuntu1 [153 kB] 341s Get:44 http://ftpmaster.internal/ubuntu resolute/main amd64 libgcc-15-dev amd64 15.2.0-7ubuntu1 [2864 kB] 341s Get:45 http://ftpmaster.internal/ubuntu resolute/main amd64 gcc-15-x86-64-linux-gnu amd64 15.2.0-7ubuntu1 [25.4 MB] 341s Get:46 http://ftpmaster.internal/ubuntu resolute/main amd64 gcc-15 amd64 15.2.0-7ubuntu1 [524 kB] 341s Get:47 http://ftpmaster.internal/ubuntu resolute/main amd64 gcc-x86-64-linux-gnu amd64 4:15.2.0-4ubuntu1 [1208 B] 341s Get:48 http://ftpmaster.internal/ubuntu resolute/main amd64 gcc amd64 4:15.2.0-4ubuntu1 [5024 B] 341s Get:49 http://ftpmaster.internal/ubuntu resolute/main amd64 libstdc++-15-dev amd64 15.2.0-7ubuntu1 [2573 kB] 341s Get:50 http://ftpmaster.internal/ubuntu resolute/main amd64 g++-15-x86-64-linux-gnu amd64 15.2.0-7ubuntu1 [14.4 MB] 341s Get:51 http://ftpmaster.internal/ubuntu resolute/main amd64 g++-15 amd64 15.2.0-7ubuntu1 [23.7 kB] 341s Get:52 http://ftpmaster.internal/ubuntu resolute/main amd64 g++-x86-64-linux-gnu amd64 4:15.2.0-4ubuntu1 [966 B] 341s Get:53 http://ftpmaster.internal/ubuntu resolute/main amd64 g++ amd64 4:15.2.0-4ubuntu1 [1100 B] 341s Get:54 http://ftpmaster.internal/ubuntu resolute/main amd64 build-essential amd64 12.12ubuntu1 [5080 B] 341s Get:55 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-charset-normalizer amd64 3.4.3-1 [174 kB] 341s Get:56 http://ftpmaster.internal/ubuntu resolute/main amd64 python3.14-tk amd64 3.14.0-4 [108 kB] 341s Get:57 http://ftpmaster.internal/ubuntu resolute/main amd64 python3.13-tk amd64 3.13.9-1 [108 kB] 341s Get:58 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-tk amd64 3.13.9-1 [8946 B] 341s Get:59 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-pil.imagetk amd64 11.3.0-1ubuntu2 [9804 B] 341s Get:60 http://ftpmaster.internal/ubuntu resolute/main amd64 libimagequant0 amd64 2.18.0-1build1 [36.3 kB] 341s Get:61 http://ftpmaster.internal/ubuntu resolute/main amd64 libjpeg-turbo8 amd64 2.1.5-4ubuntu2 [152 kB] 341s Get:62 http://ftpmaster.internal/ubuntu resolute/main amd64 libjpeg8 amd64 8c-2ubuntu11 [2148 B] 341s Get:63 http://ftpmaster.internal/ubuntu resolute/main amd64 liblcms2-2 amd64 2.17-1 [170 kB] 341s Get:64 http://ftpmaster.internal/ubuntu resolute/main amd64 libopenjp2-7 amd64 2.5.3-2.1 [188 kB] 341s Get:65 http://ftpmaster.internal/ubuntu resolute/main amd64 libgraphite2-3 amd64 1.3.14-2ubuntu1 [73.1 kB] 341s Get:66 http://ftpmaster.internal/ubuntu resolute/main amd64 libharfbuzz0b amd64 12.1.0-1 [535 kB] 341s Get:67 http://ftpmaster.internal/ubuntu resolute/main amd64 libraqm0 amd64 0.10.3-1 [15.4 kB] 341s Get:68 http://ftpmaster.internal/ubuntu resolute/main amd64 libdeflate0 amd64 1.23-2 [49.9 kB] 341s Get:69 http://ftpmaster.internal/ubuntu resolute/main amd64 libjbig0 amd64 2.1-6.1ubuntu2 [29.7 kB] 341s Get:70 http://ftpmaster.internal/ubuntu resolute/main amd64 liblerc4 amd64 4.0.0+ds-5ubuntu1 [271 kB] 341s Get:71 http://ftpmaster.internal/ubuntu resolute/main amd64 libsharpyuv0 amd64 1.5.0-0.1 [25.9 kB] 341s Get:72 http://ftpmaster.internal/ubuntu resolute/main amd64 libwebp7 amd64 1.5.0-0.1 [378 kB] 341s Get:73 http://ftpmaster.internal/ubuntu resolute/main amd64 libtiff6 amd64 4.7.0-3ubuntu3 [209 kB] 341s Get:74 http://ftpmaster.internal/ubuntu resolute/main amd64 libwebpdemux2 amd64 1.5.0-0.1 [13.0 kB] 341s Get:75 http://ftpmaster.internal/ubuntu resolute/main amd64 libwebpmux3 amd64 1.5.0-0.1 [27.6 kB] 341s Get:76 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-pil amd64 11.3.0-1ubuntu2 [504 kB] 341s Get:77 http://ftpmaster.internal/ubuntu resolute/main amd64 libpixman-1-0 amd64 0.46.4-1 [287 kB] 341s Get:78 http://ftpmaster.internal/ubuntu resolute/main amd64 libxcb-render0 amd64 1.17.0-2build1 [17.4 kB] 341s Get:79 http://ftpmaster.internal/ubuntu resolute/main amd64 libxcb-shm0 amd64 1.17.0-2build1 [6120 B] 341s Get:80 http://ftpmaster.internal/ubuntu resolute/main amd64 libcairo2 amd64 1.18.4-1build1 [611 kB] 341s Get:81 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-cairo amd64 1.27.0-2build1 [140 kB] 341s Get:82 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-freetype all 2.5.1-2 [92.2 kB] 341s Get:83 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-rlpycairo all 0.3.0-4 [9332 B] 341s Get:84 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-reportlab all 4.4.4-2 [1147 kB] 341s Get:85 http://ftpmaster.internal/ubuntu resolute/main amd64 sgml-base all 1.31+nmu1 [11.0 kB] 341s Get:86 http://ftpmaster.internal/ubuntu resolute/main amd64 xml-core all 0.19 [20.3 kB] 341s Get:87 http://ftpmaster.internal/ubuntu resolute/universe amd64 w3c-sgml-lib all 1.3-3 [280 kB] 341s Get:88 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-biopython amd64 1.85+dfsg-4 [1719 kB] 341s Get:89 http://ftpmaster.internal/ubuntu resolute/universe amd64 fonts-lyx all 2.4.4-2 [171 kB] 341s Get:90 http://ftpmaster.internal/ubuntu resolute/universe amd64 python-matplotlib-data all 3.10.7+dfsg1-1 [2930 kB] 341s Get:91 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-contourpy amd64 1.3.1-2 [255 kB] 341s Get:92 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-cycler all 0.12.1-2 [9850 B] 341s Get:93 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-brotli amd64 1.1.0-2build6 [340 kB] 341s Get:94 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-platformdirs all 4.3.7-1 [16.9 kB] 341s Get:95 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-fs all 2.4.16-9ubuntu1 [91.5 kB] 341s Get:96 http://ftpmaster.internal/ubuntu resolute/main amd64 libxslt1.1 amd64 1.1.43-0.3 [172 kB] 341s Get:97 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-lxml amd64 6.0.2-1 [2333 kB] 341s Get:98 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-lz4 amd64 4.4.4+dfsg-3 [27.5 kB] 341s Get:99 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-decorator all 5.2.1-2 [28.1 kB] 341s Get:100 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-scipy amd64 1.15.3-1ubuntu1 [20.3 MB] 342s Get:101 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-mpmath all 1.3.0-2 [423 kB] 342s Get:102 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-sympy all 1.14.0-2 [4306 kB] 342s Get:103 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-ufolib2 all 0.17.1+dfsg1-1 [33.5 kB] 342s Get:104 http://ftpmaster.internal/ubuntu resolute/main amd64 libpython3.14-stdlib amd64 3.14.0-4 [2397 kB] 342s Get:105 http://ftpmaster.internal/ubuntu resolute/main amd64 python3.14 amd64 3.14.0-4 [805 kB] 342s Get:106 http://ftpmaster.internal/ubuntu resolute-proposed/main amd64 python3-all amd64 3.13.7-2 [890 B] 342s Get:107 http://ftpmaster.internal/ubuntu resolute/universe amd64 libzopfli1 amd64 1.0.3-3 [141 kB] 342s Get:108 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-zopfli amd64 0.4.0-1 [11.1 kB] 342s Get:109 http://ftpmaster.internal/ubuntu resolute/universe amd64 unicode-data all 16.0.0-1 [9513 kB] 342s Get:110 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-fonttools amd64 4.57.0-2build1 [1731 kB] 342s Get:111 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-kiwisolver amd64 1.4.10~rc0-1 [65.5 kB] 342s Get:112 http://ftpmaster.internal/ubuntu resolute/universe amd64 libqhull-r8.0 amd64 2020.2-7 [197 kB] 342s Get:113 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-matplotlib amd64 3.10.7+dfsg1-1 [17.2 MB] 343s Get:114 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-pytz all 2025.2-4 [32.3 kB] 343s Get:115 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-pandas-lib amd64 2.3.3+dfsg-1ubuntu1 [7668 kB] 343s Get:116 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-pandas all 2.3.3+dfsg-1ubuntu1 [2948 kB] 343s Get:117 http://ftpmaster.internal/ubuntu resolute/universe amd64 cython3 amd64 3.1.6+dfsg-1ubuntu1 [3428 kB] 343s Get:118 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-joblib all 1.4.2-4 [205 kB] 343s Get:119 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-networkx all 3.2.1-4ubuntu1 [11.5 MB] 343s Get:120 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-pomegranate amd64 0.15.0-2 [4637 kB] 343s Get:121 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-pyfaidx all 0.8.1.3-2 [29.7 kB] 343s Get:122 http://ftpmaster.internal/ubuntu resolute/universe amd64 libhtscodecs2 amd64 1.6.1-2 [132 kB] 343s Get:123 http://ftpmaster.internal/ubuntu resolute/universe amd64 libhts3t64 amd64 1.22.1+ds2-1 [461 kB] 343s Get:124 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-pysam amd64 0.23.3+ds-2 [4536 kB] 343s Get:125 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-sklearn-lib amd64 1.7.2+dfsg-3ubuntu1 [6361 kB] 343s Get:126 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-threadpoolctl all 3.1.0-1 [21.3 kB] 343s Get:127 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-sklearn all 1.7.2+dfsg-3ubuntu1 [2616 kB] 343s Get:128 http://ftpmaster.internal/ubuntu resolute/main amd64 zip amd64 3.0-15ubuntu2 [178 kB] 343s Get:129 http://ftpmaster.internal/ubuntu resolute/main amd64 unzip amd64 6.0-28ubuntu7 [180 kB] 343s Get:130 http://ftpmaster.internal/ubuntu resolute/main amd64 libpaper2 amd64 2.2.5-0.3 [17.4 kB] 343s Get:131 http://ftpmaster.internal/ubuntu resolute/main amd64 libpaper-utils amd64 2.2.5-0.3 [15.5 kB] 343s Get:132 http://ftpmaster.internal/ubuntu resolute/main amd64 xdg-utils all 1.2.1-2ubuntu1 [66.0 kB] 343s Get:133 http://ftpmaster.internal/ubuntu resolute/main amd64 fontconfig amd64 2.15.0-2.3ubuntu1 [180 kB] 343s Get:134 http://ftpmaster.internal/ubuntu resolute/main amd64 libthai-data all 0.1.29-2build1 [158 kB] 343s Get:135 http://ftpmaster.internal/ubuntu resolute/main amd64 libdatrie1 amd64 0.2.13-4 [19.3 kB] 343s Get:136 http://ftpmaster.internal/ubuntu resolute/main amd64 libthai0 amd64 0.1.29-2build1 [18.9 kB] 343s Get:137 http://ftpmaster.internal/ubuntu resolute/main amd64 libpango-1.0-0 amd64 1.56.3-2 [239 kB] 343s Get:138 http://ftpmaster.internal/ubuntu resolute/main amd64 libpangoft2-1.0-0 amd64 1.56.3-2 [52.5 kB] 343s Get:139 http://ftpmaster.internal/ubuntu resolute/main amd64 libpangocairo-1.0-0 amd64 1.56.3-2 [29.0 kB] 343s Get:140 http://ftpmaster.internal/ubuntu resolute/main amd64 libice6 amd64 2:1.1.1-1 [44.1 kB] 343s Get:141 http://ftpmaster.internal/ubuntu resolute/main amd64 libsm6 amd64 2:1.2.6-1 [16.4 kB] 343s Get:142 http://ftpmaster.internal/ubuntu resolute/main amd64 libxt6t64 amd64 1:1.2.1-1.3 [173 kB] 343s Get:143 http://ftpmaster.internal/ubuntu resolute/universe amd64 r-base-core amd64 4.5.2-1 [28.8 MB] 344s Get:144 http://ftpmaster.internal/ubuntu resolute/universe amd64 r-bioc-biocgenerics all 0.52.0-2 [624 kB] 344s Get:145 http://ftpmaster.internal/ubuntu resolute/universe amd64 r-bioc-dnacopy amd64 1.80.0-2 [500 kB] 344s Get:146 http://ftpmaster.internal/ubuntu resolute/universe amd64 cnvkit all 0.9.12-1 [20.6 MB] 344s Get:147 http://ftpmaster.internal/ubuntu resolute/main amd64 libdebhelper-perl all 13.24.2ubuntu1 [95.7 kB] 344s Get:148 http://ftpmaster.internal/ubuntu resolute/main amd64 libtool all 2.5.4-7 [169 kB] 344s Get:149 http://ftpmaster.internal/ubuntu resolute/main amd64 dh-autoreconf all 21 [12.5 kB] 344s Get:150 http://ftpmaster.internal/ubuntu resolute/main amd64 libarchive-zip-perl all 1.68-1 [90.2 kB] 344s Get:151 http://ftpmaster.internal/ubuntu resolute/main amd64 libfile-stripnondeterminism-perl all 1.15.0-1 [20.5 kB] 344s Get:152 http://ftpmaster.internal/ubuntu resolute/main amd64 dh-strip-nondeterminism all 1.15.0-1 [5090 B] 344s Get:153 http://ftpmaster.internal/ubuntu resolute/main amd64 debugedit amd64 1:5.2-3 [49.9 kB] 344s Get:154 http://ftpmaster.internal/ubuntu resolute/main amd64 dwz amd64 0.16-2 [115 kB] 344s Get:155 http://ftpmaster.internal/ubuntu resolute/main amd64 gettext amd64 0.23.2-1 [1019 kB] 344s Get:156 http://ftpmaster.internal/ubuntu resolute/main amd64 intltool-debian all 0.35.0+20060710.6 [23.2 kB] 344s Get:157 http://ftpmaster.internal/ubuntu resolute/main amd64 po-debconf all 1.0.21+nmu1 [233 kB] 344s Get:158 http://ftpmaster.internal/ubuntu resolute/main amd64 debhelper all 13.24.2ubuntu1 [896 kB] 344s Get:159 http://ftpmaster.internal/ubuntu resolute/universe amd64 dh-python all 6.20250414 [119 kB] 344s Get:160 http://ftpmaster.internal/ubuntu resolute/main amd64 libgpgmepp6t64 amd64 1.24.2-3ubuntu2 [124 kB] 344s Get:161 http://ftpmaster.internal/ubuntu resolute/main amd64 libpoppler147 amd64 25.03.0-11.1 [1224 kB] 344s Get:162 http://ftpmaster.internal/ubuntu resolute/main amd64 poppler-utils amd64 25.03.0-11.1 [229 kB] 344s Get:163 http://ftpmaster.internal/ubuntu resolute/universe amd64 pybuild-plugin-autopkgtest all 6.20250414 [1746 B] 344s Get:164 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-pyproject-hooks all 1.2.0-1 [10.2 kB] 344s Get:165 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-wheel all 0.46.1-2 [22.1 kB] 344s Get:166 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-build all 1.2.2-4 [31.0 kB] 344s Get:167 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-installer all 0.7.0+dfsg1-3 [17.4 kB] 344s Get:168 http://ftpmaster.internal/ubuntu resolute/universe amd64 pybuild-plugin-pyproject all 6.20250414 [1728 B] 344s Get:169 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-iniconfig all 2.1.0-1 [6840 B] 344s Get:170 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-pluggy all 1.6.0-1 [21.0 kB] 344s Get:171 http://ftpmaster.internal/ubuntu resolute/universe amd64 python3-pytest all 8.3.5-2 [252 kB] 344s Preconfiguring packages ... 345s Fetched 273 MB in 5s (50.5 MB/s) 345s Selecting previously unselected package python3-numpy-dev:amd64. 345s (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 ... 83372 files and directories currently installed.) 345s Preparing to unpack .../000-python3-numpy-dev_1%3a2.2.4+ds-1ubuntu1_amd64.deb ... 345s Unpacking python3-numpy-dev:amd64 (1:2.2.4+ds-1ubuntu1) ... 345s Selecting previously unselected package libblas3:amd64. 345s Preparing to unpack .../001-libblas3_3.12.1-7_amd64.deb ... 345s Unpacking libblas3:amd64 (3.12.1-7) ... 345s Selecting previously unselected package libgfortran5:amd64. 345s Preparing to unpack .../002-libgfortran5_15.2.0-7ubuntu1_amd64.deb ... 345s Unpacking libgfortran5:amd64 (15.2.0-7ubuntu1) ... 345s Selecting previously unselected package liblapack3:amd64. 345s Preparing to unpack .../003-liblapack3_3.12.1-7_amd64.deb ... 345s Unpacking liblapack3:amd64 (3.12.1-7) ... 345s Selecting previously unselected package python3-numpy. 345s Preparing to unpack .../004-python3-numpy_1%3a2.2.4+ds-1ubuntu1_amd64.deb ... 345s Unpacking python3-numpy (1:2.2.4+ds-1ubuntu1) ... 345s Selecting previously unselected package libpython3.14-minimal:amd64. 345s Preparing to unpack .../005-libpython3.14-minimal_3.14.0-4_amd64.deb ... 345s Unpacking libpython3.14-minimal:amd64 (3.14.0-4) ... 345s Selecting previously unselected package python3.14-minimal. 345s Preparing to unpack .../006-python3.14-minimal_3.14.0-4_amd64.deb ... 345s Unpacking python3.14-minimal (3.14.0-4) ... 345s Selecting previously unselected package m4. 345s Preparing to unpack .../007-m4_1.4.20-2_amd64.deb ... 345s Unpacking m4 (1.4.20-2) ... 345s Selecting previously unselected package autoconf. 345s Preparing to unpack .../008-autoconf_2.72-3.1ubuntu1_all.deb ... 345s Unpacking autoconf (2.72-3.1ubuntu1) ... 345s Selecting previously unselected package autotools-dev. 345s Preparing to unpack .../009-autotools-dev_20240727.1_all.deb ... 345s Unpacking autotools-dev (20240727.1) ... 345s Selecting previously unselected package automake. 345s Preparing to unpack .../010-automake_1%3a1.18.1-2_all.deb ... 345s Unpacking automake (1:1.18.1-2) ... 345s Selecting previously unselected package autopoint. 345s Preparing to unpack .../011-autopoint_0.23.2-1_all.deb ... 345s Unpacking autopoint (0.23.2-1) ... 345s Selecting previously unselected package libtcl8.6:amd64. 345s Preparing to unpack .../012-libtcl8.6_8.6.17+dfsg-1_amd64.deb ... 345s Unpacking libtcl8.6:amd64 (8.6.17+dfsg-1) ... 345s Selecting previously unselected package fonts-dejavu-mono. 345s Preparing to unpack .../013-fonts-dejavu-mono_2.37-8_all.deb ... 345s Unpacking fonts-dejavu-mono (2.37-8) ... 345s Selecting previously unselected package fonts-dejavu-core. 345s Preparing to unpack .../014-fonts-dejavu-core_2.37-8_all.deb ... 345s Unpacking fonts-dejavu-core (2.37-8) ... 345s Selecting previously unselected package libfontenc1:amd64. 345s Preparing to unpack .../015-libfontenc1_1%3a1.1.8-1build1_amd64.deb ... 345s Unpacking libfontenc1:amd64 (1:1.1.8-1build1) ... 345s Selecting previously unselected package x11-common. 345s Preparing to unpack .../016-x11-common_1%3a7.7+24ubuntu1_all.deb ... 345s Unpacking x11-common (1:7.7+24ubuntu1) ... 345s Selecting previously unselected package xfonts-encodings. 345s Preparing to unpack .../017-xfonts-encodings_1%3a1.0.5-0ubuntu2_all.deb ... 345s Unpacking xfonts-encodings (1:1.0.5-0ubuntu2) ... 345s Selecting previously unselected package xfonts-utils. 345s Preparing to unpack .../018-xfonts-utils_1%3a7.7+7_amd64.deb ... 345s Unpacking xfonts-utils (1:7.7+7) ... 345s Selecting previously unselected package fonts-urw-base35. 345s Preparing to unpack .../019-fonts-urw-base35_20200910-8_all.deb ... 345s Unpacking fonts-urw-base35 (20200910-8) ... 345s Selecting previously unselected package fontconfig-config. 345s Preparing to unpack .../020-fontconfig-config_2.15.0-2.3ubuntu1_amd64.deb ... 345s Unpacking fontconfig-config (2.15.0-2.3ubuntu1) ... 345s Selecting previously unselected package libfontconfig1:amd64. 345s Preparing to unpack .../021-libfontconfig1_2.15.0-2.3ubuntu1_amd64.deb ... 345s Unpacking libfontconfig1:amd64 (2.15.0-2.3ubuntu1) ... 345s Selecting previously unselected package libxrender1:amd64. 345s Preparing to unpack .../022-libxrender1_1%3a0.9.12-1_amd64.deb ... 345s Unpacking libxrender1:amd64 (1:0.9.12-1) ... 345s Selecting previously unselected package libxft2:amd64. 345s Preparing to unpack .../023-libxft2_2.3.6-1build1_amd64.deb ... 345s Unpacking libxft2:amd64 (2.3.6-1build1) ... 345s Selecting previously unselected package libxss1:amd64. 345s Preparing to unpack .../024-libxss1_1%3a1.2.3-1build3_amd64.deb ... 345s Unpacking libxss1:amd64 (1:1.2.3-1build3) ... 345s Selecting previously unselected package libtk8.6:amd64. 345s Preparing to unpack .../025-libtk8.6_8.6.17-1_amd64.deb ... 345s Unpacking libtk8.6:amd64 (8.6.17-1) ... 345s Selecting previously unselected package tk8.6-blt2.5. 345s Preparing to unpack .../026-tk8.6-blt2.5_2.5.3+dfsg-8_amd64.deb ... 345s Unpacking tk8.6-blt2.5 (2.5.3+dfsg-8) ... 345s Selecting previously unselected package blt. 345s Preparing to unpack .../027-blt_2.5.3+dfsg-8_amd64.deb ... 345s Unpacking blt (2.5.3+dfsg-8) ... 345s Selecting previously unselected package libisl23:amd64. 345s Preparing to unpack .../028-libisl23_0.27-1_amd64.deb ... 345s Unpacking libisl23:amd64 (0.27-1) ... 345s Selecting previously unselected package libmpc3:amd64. 345s Preparing to unpack .../029-libmpc3_1.3.1-2_amd64.deb ... 345s Unpacking libmpc3:amd64 (1.3.1-2) ... 345s Selecting previously unselected package cpp-15-x86-64-linux-gnu. 345s Preparing to unpack .../030-cpp-15-x86-64-linux-gnu_15.2.0-7ubuntu1_amd64.deb ... 345s Unpacking cpp-15-x86-64-linux-gnu (15.2.0-7ubuntu1) ... 345s Selecting previously unselected package cpp-15. 345s Preparing to unpack .../031-cpp-15_15.2.0-7ubuntu1_amd64.deb ... 345s Unpacking cpp-15 (15.2.0-7ubuntu1) ... 345s Selecting previously unselected package cpp-x86-64-linux-gnu. 345s Preparing to unpack .../032-cpp-x86-64-linux-gnu_4%3a15.2.0-4ubuntu1_amd64.deb ... 345s Unpacking cpp-x86-64-linux-gnu (4:15.2.0-4ubuntu1) ... 346s Selecting previously unselected package cpp. 346s Preparing to unpack .../033-cpp_4%3a15.2.0-4ubuntu1_amd64.deb ... 346s Unpacking cpp (4:15.2.0-4ubuntu1) ... 346s Selecting previously unselected package libcc1-0:amd64. 346s Preparing to unpack .../034-libcc1-0_15.2.0-7ubuntu1_amd64.deb ... 346s Unpacking libcc1-0:amd64 (15.2.0-7ubuntu1) ... 346s Selecting previously unselected package libgomp1:amd64. 346s Preparing to unpack .../035-libgomp1_15.2.0-7ubuntu1_amd64.deb ... 346s Unpacking libgomp1:amd64 (15.2.0-7ubuntu1) ... 346s Selecting previously unselected package libitm1:amd64. 346s Preparing to unpack .../036-libitm1_15.2.0-7ubuntu1_amd64.deb ... 346s Unpacking libitm1:amd64 (15.2.0-7ubuntu1) ... 346s Selecting previously unselected package libasan8:amd64. 346s Preparing to unpack .../037-libasan8_15.2.0-7ubuntu1_amd64.deb ... 346s Unpacking libasan8:amd64 (15.2.0-7ubuntu1) ... 346s Selecting previously unselected package liblsan0:amd64. 346s Preparing to unpack .../038-liblsan0_15.2.0-7ubuntu1_amd64.deb ... 346s Unpacking liblsan0:amd64 (15.2.0-7ubuntu1) ... 346s Selecting previously unselected package libtsan2:amd64. 346s Preparing to unpack .../039-libtsan2_15.2.0-7ubuntu1_amd64.deb ... 346s Unpacking libtsan2:amd64 (15.2.0-7ubuntu1) ... 346s Selecting previously unselected package libubsan1:amd64. 346s Preparing to unpack .../040-libubsan1_15.2.0-7ubuntu1_amd64.deb ... 346s Unpacking libubsan1:amd64 (15.2.0-7ubuntu1) ... 346s Selecting previously unselected package libhwasan0:amd64. 346s Preparing to unpack .../041-libhwasan0_15.2.0-7ubuntu1_amd64.deb ... 346s Unpacking libhwasan0:amd64 (15.2.0-7ubuntu1) ... 346s Selecting previously unselected package libquadmath0:amd64. 346s Preparing to unpack .../042-libquadmath0_15.2.0-7ubuntu1_amd64.deb ... 346s Unpacking libquadmath0:amd64 (15.2.0-7ubuntu1) ... 346s Selecting previously unselected package libgcc-15-dev:amd64. 346s Preparing to unpack .../043-libgcc-15-dev_15.2.0-7ubuntu1_amd64.deb ... 346s Unpacking libgcc-15-dev:amd64 (15.2.0-7ubuntu1) ... 346s Selecting previously unselected package gcc-15-x86-64-linux-gnu. 346s Preparing to unpack .../044-gcc-15-x86-64-linux-gnu_15.2.0-7ubuntu1_amd64.deb ... 346s Unpacking gcc-15-x86-64-linux-gnu (15.2.0-7ubuntu1) ... 346s Selecting previously unselected package gcc-15. 346s Preparing to unpack .../045-gcc-15_15.2.0-7ubuntu1_amd64.deb ... 346s Unpacking gcc-15 (15.2.0-7ubuntu1) ... 346s Selecting previously unselected package gcc-x86-64-linux-gnu. 346s Preparing to unpack .../046-gcc-x86-64-linux-gnu_4%3a15.2.0-4ubuntu1_amd64.deb ... 346s Unpacking gcc-x86-64-linux-gnu (4:15.2.0-4ubuntu1) ... 346s Selecting previously unselected package gcc. 346s Preparing to unpack .../047-gcc_4%3a15.2.0-4ubuntu1_amd64.deb ... 346s Unpacking gcc (4:15.2.0-4ubuntu1) ... 346s Selecting previously unselected package libstdc++-15-dev:amd64. 346s Preparing to unpack .../048-libstdc++-15-dev_15.2.0-7ubuntu1_amd64.deb ... 346s Unpacking libstdc++-15-dev:amd64 (15.2.0-7ubuntu1) ... 346s Selecting previously unselected package g++-15-x86-64-linux-gnu. 346s Preparing to unpack .../049-g++-15-x86-64-linux-gnu_15.2.0-7ubuntu1_amd64.deb ... 346s Unpacking g++-15-x86-64-linux-gnu (15.2.0-7ubuntu1) ... 346s Selecting previously unselected package g++-15. 346s Preparing to unpack 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346s Unpacking python3.14-tk (3.14.0-4) ... 346s Selecting previously unselected package python3.13-tk. 346s Preparing to unpack .../056-python3.13-tk_3.13.9-1_amd64.deb ... 346s Unpacking python3.13-tk (3.13.9-1) ... 346s Selecting previously unselected package python3-tk:amd64. 346s Preparing to unpack .../057-python3-tk_3.13.9-1_amd64.deb ... 346s Unpacking python3-tk:amd64 (3.13.9-1) ... 346s Selecting previously unselected package python3-pil.imagetk:amd64. 346s Preparing to unpack .../058-python3-pil.imagetk_11.3.0-1ubuntu2_amd64.deb ... 346s Unpacking python3-pil.imagetk:amd64 (11.3.0-1ubuntu2) ... 346s Selecting previously unselected package libimagequant0:amd64. 346s Preparing to unpack .../059-libimagequant0_2.18.0-1build1_amd64.deb ... 346s Unpacking libimagequant0:amd64 (2.18.0-1build1) ... 346s Selecting previously unselected package libjpeg-turbo8:amd64. 346s Preparing to unpack .../060-libjpeg-turbo8_2.1.5-4ubuntu2_amd64.deb ... 346s Unpacking libjpeg-turbo8:amd64 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libraqm0:amd64. 346s Preparing to unpack .../066-libraqm0_0.10.3-1_amd64.deb ... 346s Unpacking libraqm0:amd64 (0.10.3-1) ... 346s Selecting previously unselected package libdeflate0:amd64. 346s Preparing to unpack .../067-libdeflate0_1.23-2_amd64.deb ... 346s Unpacking libdeflate0:amd64 (1.23-2) ... 346s Selecting previously unselected package libjbig0:amd64. 346s Preparing to unpack .../068-libjbig0_2.1-6.1ubuntu2_amd64.deb ... 346s Unpacking libjbig0:amd64 (2.1-6.1ubuntu2) ... 346s Selecting previously unselected package liblerc4:amd64. 346s Preparing to unpack .../069-liblerc4_4.0.0+ds-5ubuntu1_amd64.deb ... 346s Unpacking liblerc4:amd64 (4.0.0+ds-5ubuntu1) ... 346s Selecting previously unselected package libsharpyuv0:amd64. 346s Preparing to unpack .../070-libsharpyuv0_1.5.0-0.1_amd64.deb ... 346s Unpacking libsharpyuv0:amd64 (1.5.0-0.1) ... 346s Selecting previously unselected package libwebp7:amd64. 346s Preparing to unpack .../071-libwebp7_1.5.0-0.1_amd64.deb ... 346s Unpacking libwebp7:amd64 (1.5.0-0.1) ... 347s Selecting previously unselected package libtiff6:amd64. 347s Preparing to unpack .../072-libtiff6_4.7.0-3ubuntu3_amd64.deb ... 347s Unpacking libtiff6:amd64 (4.7.0-3ubuntu3) ... 347s Selecting previously unselected package libwebpdemux2:amd64. 347s Preparing to unpack .../073-libwebpdemux2_1.5.0-0.1_amd64.deb ... 347s Unpacking libwebpdemux2:amd64 (1.5.0-0.1) ... 347s Selecting previously unselected package libwebpmux3:amd64. 347s Preparing to unpack .../074-libwebpmux3_1.5.0-0.1_amd64.deb ... 347s Unpacking libwebpmux3:amd64 (1.5.0-0.1) ... 347s Selecting previously unselected package python3-pil:amd64. 347s Preparing to unpack .../075-python3-pil_11.3.0-1ubuntu2_amd64.deb ... 347s Unpacking python3-pil:amd64 (11.3.0-1ubuntu2) ... 347s Selecting previously unselected package libpixman-1-0:amd64. 347s Preparing to unpack .../076-libpixman-1-0_0.46.4-1_amd64.deb ... 347s Unpacking libpixman-1-0:amd64 (0.46.4-1) ... 347s Selecting previously unselected package libxcb-render0:amd64. 347s Preparing to unpack .../077-libxcb-render0_1.17.0-2build1_amd64.deb ... 347s Unpacking libxcb-render0:amd64 (1.17.0-2build1) ... 347s Selecting previously unselected package libxcb-shm0:amd64. 347s Preparing to unpack .../078-libxcb-shm0_1.17.0-2build1_amd64.deb ... 347s Unpacking libxcb-shm0:amd64 (1.17.0-2build1) ... 347s Selecting previously unselected package libcairo2:amd64. 347s Preparing to unpack .../079-libcairo2_1.18.4-1build1_amd64.deb ... 347s Unpacking libcairo2:amd64 (1.18.4-1build1) ... 347s Selecting previously unselected package python3-cairo. 347s Preparing to unpack .../080-python3-cairo_1.27.0-2build1_amd64.deb ... 347s Unpacking python3-cairo (1.27.0-2build1) ... 347s Selecting previously unselected package python3-freetype. 347s Preparing to unpack .../081-python3-freetype_2.5.1-2_all.deb ... 347s Unpacking python3-freetype (2.5.1-2) ... 347s Selecting previously unselected package python3-rlpycairo. 347s Preparing to unpack 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347s Unpacking python3-platformdirs (4.3.7-1) ... 347s Selecting previously unselected package python3-fs. 347s Preparing to unpack .../094-python3-fs_2.4.16-9ubuntu1_all.deb ... 347s Unpacking python3-fs (2.4.16-9ubuntu1) ... 347s Selecting previously unselected package libxslt1.1:amd64. 347s Preparing to unpack .../095-libxslt1.1_1.1.43-0.3_amd64.deb ... 347s Unpacking libxslt1.1:amd64 (1.1.43-0.3) ... 347s Selecting previously unselected package python3-lxml:amd64. 347s Preparing to unpack .../096-python3-lxml_6.0.2-1_amd64.deb ... 347s Unpacking python3-lxml:amd64 (6.0.2-1) ... 347s Selecting previously unselected package python3-lz4. 347s Preparing to unpack .../097-python3-lz4_4.4.4+dfsg-3_amd64.deb ... 347s Unpacking python3-lz4 (4.4.4+dfsg-3) ... 347s Selecting previously unselected package python3-decorator. 347s Preparing to unpack .../098-python3-decorator_5.2.1-2_all.deb ... 347s Unpacking python3-decorator (5.2.1-2) ... 347s Selecting previously unselected package python3-scipy. 347s Preparing to unpack .../099-python3-scipy_1.15.3-1ubuntu1_amd64.deb ... 347s Unpacking python3-scipy (1.15.3-1ubuntu1) ... 347s Selecting previously unselected package python3-mpmath. 347s Preparing to unpack .../100-python3-mpmath_1.3.0-2_all.deb ... 347s Unpacking python3-mpmath (1.3.0-2) ... 347s Selecting previously unselected package python3-sympy. 347s Preparing to unpack .../101-python3-sympy_1.14.0-2_all.deb ... 347s Unpacking python3-sympy (1.14.0-2) ... 347s Selecting previously unselected package python3-ufolib2. 347s Preparing to unpack .../102-python3-ufolib2_0.17.1+dfsg1-1_all.deb ... 347s Unpacking python3-ufolib2 (0.17.1+dfsg1-1) ... 347s Selecting previously unselected package libpython3.14-stdlib:amd64. 347s Preparing to unpack .../103-libpython3.14-stdlib_3.14.0-4_amd64.deb ... 347s Unpacking libpython3.14-stdlib:amd64 (3.14.0-4) ... 347s Selecting previously unselected package python3.14. 347s Preparing to unpack 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python3-kiwisolver. 348s Preparing to unpack .../110-python3-kiwisolver_1.4.10~rc0-1_amd64.deb ... 348s Unpacking python3-kiwisolver (1.4.10~rc0-1) ... 348s Selecting previously unselected package libqhull-r8.0:amd64. 348s Preparing to unpack .../111-libqhull-r8.0_2020.2-7_amd64.deb ... 348s Unpacking libqhull-r8.0:amd64 (2020.2-7) ... 348s Selecting previously unselected package python3-matplotlib. 348s Preparing to unpack .../112-python3-matplotlib_3.10.7+dfsg1-1_amd64.deb ... 348s Unpacking python3-matplotlib (3.10.7+dfsg1-1) ... 348s Selecting previously unselected package python3-pytz. 348s Preparing to unpack .../113-python3-pytz_2025.2-4_all.deb ... 348s Unpacking python3-pytz (2025.2-4) ... 348s Selecting previously unselected package python3-pandas-lib:amd64. 348s Preparing to unpack .../114-python3-pandas-lib_2.3.3+dfsg-1ubuntu1_amd64.deb ... 348s Unpacking python3-pandas-lib:amd64 (2.3.3+dfsg-1ubuntu1) ... 348s Selecting previously unselected package python3-pandas. 348s 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349s Unpacking python3-pyfaidx (0.8.1.3-2) ... 349s Selecting previously unselected package libhtscodecs2:amd64. 349s Preparing to unpack .../121-libhtscodecs2_1.6.1-2_amd64.deb ... 349s Unpacking libhtscodecs2:amd64 (1.6.1-2) ... 349s Selecting previously unselected package libhts3t64:amd64. 349s Preparing to unpack .../122-libhts3t64_1.22.1+ds2-1_amd64.deb ... 349s Unpacking libhts3t64:amd64 (1.22.1+ds2-1) ... 349s Selecting previously unselected package python3-pysam. 349s Preparing to unpack .../123-python3-pysam_0.23.3+ds-2_amd64.deb ... 349s Unpacking python3-pysam (0.23.3+ds-2) ... 349s Selecting previously unselected package python3-sklearn-lib:amd64. 349s Preparing to unpack .../124-python3-sklearn-lib_1.7.2+dfsg-3ubuntu1_amd64.deb ... 349s Unpacking python3-sklearn-lib:amd64 (1.7.2+dfsg-3ubuntu1) ... 349s Selecting previously unselected package python3-threadpoolctl. 349s Preparing to unpack .../125-python3-threadpoolctl_3.1.0-1_all.deb ... 349s Unpacking python3-threadpoolctl (3.1.0-1) ... 349s Selecting previously unselected package python3-sklearn. 349s Preparing to unpack .../126-python3-sklearn_1.7.2+dfsg-3ubuntu1_all.deb ... 349s Unpacking python3-sklearn (1.7.2+dfsg-3ubuntu1) ... 349s Selecting previously unselected package zip. 349s Preparing to unpack .../127-zip_3.0-15ubuntu2_amd64.deb ... 349s Unpacking zip (3.0-15ubuntu2) ... 349s Selecting previously unselected package unzip. 349s Preparing to unpack .../128-unzip_6.0-28ubuntu7_amd64.deb ... 349s Unpacking unzip (6.0-28ubuntu7) ... 349s Selecting previously unselected package libpaper2:amd64. 349s Preparing to unpack .../129-libpaper2_2.2.5-0.3_amd64.deb ... 349s Unpacking libpaper2:amd64 (2.2.5-0.3) ... 349s Selecting previously unselected package libpaper-utils. 349s Preparing to unpack .../130-libpaper-utils_2.2.5-0.3_amd64.deb ... 349s Unpacking libpaper-utils (2.2.5-0.3) ... 349s Selecting previously unselected package xdg-utils. 349s Preparing to unpack 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... 349s Selecting previously unselected package libpangoft2-1.0-0:amd64. 349s Preparing to unpack .../137-libpangoft2-1.0-0_1.56.3-2_amd64.deb ... 349s Unpacking libpangoft2-1.0-0:amd64 (1.56.3-2) ... 349s Selecting previously unselected package libpangocairo-1.0-0:amd64. 349s Preparing to unpack .../138-libpangocairo-1.0-0_1.56.3-2_amd64.deb ... 349s Unpacking libpangocairo-1.0-0:amd64 (1.56.3-2) ... 349s Selecting previously unselected package libice6:amd64. 349s Preparing to unpack .../139-libice6_2%3a1.1.1-1_amd64.deb ... 349s Unpacking libice6:amd64 (2:1.1.1-1) ... 349s Selecting previously unselected package libsm6:amd64. 349s Preparing to unpack .../140-libsm6_2%3a1.2.6-1_amd64.deb ... 349s Unpacking libsm6:amd64 (2:1.2.6-1) ... 349s Selecting previously unselected package libxt6t64:amd64. 349s Preparing to unpack .../141-libxt6t64_1%3a1.2.1-1.3_amd64.deb ... 349s Unpacking libxt6t64:amd64 (1:1.2.1-1.3) ... 349s Selecting previously unselected package r-base-core. 349s 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Setting up python3-biopython (1.85+dfsg-4) ... 404s Setting up cnvkit (0.9.12-1) ... 410s autopkgtest [11:58:25]: test pybuild-autopkgtest: pybuild-autopkgtest 410s autopkgtest [11:58:25]: test pybuild-autopkgtest: [----------------------- 410s pybuild-autopkgtest 411s I: pybuild base:311: cd /tmp/autopkgtest.ZVFRmj/autopkgtest_tmp/build; python3.14 -m pytest test 411s ============================= test session starts ============================== 411s platform linux -- Python 3.14.0, pytest-8.3.5, pluggy-1.6.0 411s rootdir: /tmp/autopkgtest.ZVFRmj/autopkgtest_tmp/build 411s configfile: pyproject.toml 411s plugins: typeguard-4.4.2 411s collected 0 items / 5 errors 411s 411s ==================================== ERRORS ==================================== 411s _____________________ ERROR collecting test/test_cnvlib.py _____________________ 411s ImportError while importing test module '/tmp/autopkgtest.ZVFRmj/autopkgtest_tmp/build/test/test_cnvlib.py'. 411s Hint: make sure your test modules/packages have valid Python names. 411s Traceback: 411s /usr/lib/python3/dist-packages/numpy/_core/__init__.py:23: in 411s from . import multiarray 411s /usr/lib/python3/dist-packages/numpy/_core/multiarray.py:10: in 411s from . import overrides 411s /usr/lib/python3/dist-packages/numpy/_core/overrides.py:7: in 411s from numpy._core._multiarray_umath import ( 411s E ModuleNotFoundError: No module named 'numpy._core._multiarray_umath' 411s 411s During handling of the above exception, another exception occurred: 411s /usr/lib/python3/dist-packages/numpy/__init__.py:114: in 411s from numpy.__config__ import show_config 411s /usr/lib/python3/dist-packages/numpy/__config__.py:4: in 411s from numpy._core._multiarray_umath import ( 411s /usr/lib/python3/dist-packages/numpy/_core/__init__.py:49: in 411s raise ImportError(msg) 411s E ImportError: 411s E 411s E IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! 411s E 411s E Importing the numpy C-extensions failed. This error can happen for 411s E many reasons, often due to issues with your setup or how NumPy was 411s E installed. 411s E 411s E We have compiled some common reasons and troubleshooting tips at: 411s E 411s E https://numpy.org/devdocs/user/troubleshooting-importerror.html 411s E 411s E Please note and check the following: 411s E 411s E * The Python version is: Python3.14 from "/usr/bin/python3.14" 411s E * The NumPy version is: "2.2.4" 411s E 411s E and make sure that they are the versions you expect. 411s E Please carefully study the documentation linked above for further help. 411s E 411s E Original error was: No module named 'numpy._core._multiarray_umath' 411s 411s The above exception was the direct cause of the following exception: 411s /usr/lib/python3.14/importlib/__init__.py:88: in import_module 411s return _bootstrap._gcd_import(name[level:], package, level) 411s test/test_cnvlib.py:14: in 411s import numpy as np 411s /usr/lib/python3/dist-packages/numpy/__init__.py:119: in 411s raise ImportError(msg) from e 411s E ImportError: Error importing numpy: you should not try to import numpy from 411s E its source directory; please exit the numpy source tree, and relaunch 411s E your python interpreter from there. 411s ____________________ ERROR collecting test/test_commands.py ____________________ 411s ImportError while importing test module '/tmp/autopkgtest.ZVFRmj/autopkgtest_tmp/build/test/test_commands.py'. 411s Hint: make sure your test modules/packages have valid Python names. 411s Traceback: 411s /usr/lib/python3/dist-packages/numpy/_core/__init__.py:23: in 411s from . import multiarray 411s /usr/lib/python3/dist-packages/numpy/_core/multiarray.py:10: in 411s from . import overrides 411s /usr/lib/python3/dist-packages/numpy/_core/overrides.py:7: in 411s from numpy._core._multiarray_umath import ( 411s E ModuleNotFoundError: No module named 'numpy._core._multiarray_umath' 411s 411s During handling of the above exception, another exception occurred: 411s /usr/lib/python3/dist-packages/numpy/__init__.py:114: in 411s from numpy.__config__ import show_config 411s /usr/lib/python3/dist-packages/numpy/__config__.py:4: in 411s from numpy._core._multiarray_umath import ( 411s /usr/lib/python3/dist-packages/numpy/_core/__init__.py:49: in 411s raise ImportError(msg) 411s E ImportError: 411s E 411s E IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! 411s E 411s E Importing the numpy C-extensions failed. This error can happen for 411s E many reasons, often due to issues with your setup or how NumPy was 411s E installed. 411s E 411s E We have compiled some common reasons and troubleshooting tips at: 411s E 411s E https://numpy.org/devdocs/user/troubleshooting-importerror.html 411s E 411s E Please note and check the following: 411s E 411s E * The Python version is: Python3.14 from "/usr/bin/python3.14" 411s E * The NumPy version is: "2.2.4" 411s E 411s E and make sure that they are the versions you expect. 411s E Please carefully study the documentation linked above for further help. 411s E 411s E Original error was: No module named 'numpy._core._multiarray_umath' 411s 411s The above exception was the direct cause of the following exception: 411s /usr/lib/python3.14/importlib/__init__.py:88: in import_module 411s return _bootstrap._gcd_import(name[level:], package, level) 411s test/test_commands.py:18: in 411s import numpy as np 411s /usr/lib/python3/dist-packages/numpy/__init__.py:119: in 411s raise ImportError(msg) from e 411s E ImportError: Error importing numpy: you should not try to import numpy from 411s E its source directory; please exit the numpy source tree, and relaunch 411s E your python interpreter from there. 411s _____________________ ERROR collecting test/test_genome.py _____________________ 411s ImportError while importing test module '/tmp/autopkgtest.ZVFRmj/autopkgtest_tmp/build/test/test_genome.py'. 411s Hint: make sure your test modules/packages have valid Python names. 411s Traceback: 411s /usr/lib/python3/dist-packages/numpy/_core/__init__.py:23: in 411s from . import multiarray 411s /usr/lib/python3/dist-packages/numpy/_core/multiarray.py:10: in 411s from . import overrides 411s /usr/lib/python3/dist-packages/numpy/_core/overrides.py:7: in 411s from numpy._core._multiarray_umath import ( 411s E ModuleNotFoundError: No module named 'numpy._core._multiarray_umath' 411s 411s During handling of the above exception, another exception occurred: 411s /usr/lib/python3/dist-packages/numpy/__init__.py:114: in 411s from numpy.__config__ import show_config 411s /usr/lib/python3/dist-packages/numpy/__config__.py:4: in 411s from numpy._core._multiarray_umath import ( 411s /usr/lib/python3/dist-packages/numpy/_core/__init__.py:49: in 411s raise ImportError(msg) 411s E ImportError: 411s E 411s E IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! 411s E 411s E Importing the numpy C-extensions failed. This error can happen for 411s E many reasons, often due to issues with your setup or how NumPy was 411s E installed. 411s E 411s E We have compiled some common reasons and troubleshooting tips at: 411s E 411s E https://numpy.org/devdocs/user/troubleshooting-importerror.html 411s E 411s E Please note and check the following: 411s E 411s E * The Python version is: Python3.14 from "/usr/bin/python3.14" 411s E * The NumPy version is: "2.2.4" 411s E 411s E and make sure that they are the versions you expect. 411s E Please carefully study the documentation linked above for further help. 411s E 411s E Original error was: No module named 'numpy._core._multiarray_umath' 411s 411s The above exception was the direct cause of the following exception: 411s /usr/lib/python3.14/importlib/__init__.py:88: in import_module 411s return _bootstrap._gcd_import(name[level:], package, level) 411s test/test_genome.py:12: in 411s import numpy as np 411s /usr/lib/python3/dist-packages/numpy/__init__.py:119: in 411s raise ImportError(msg) from e 411s E ImportError: Error importing numpy: you should not try to import numpy from 411s E its source directory; please exit the numpy source tree, and relaunch 411s E your python interpreter from there. 411s _______________________ ERROR collecting test/test_io.py _______________________ 411s ImportError while importing test module '/tmp/autopkgtest.ZVFRmj/autopkgtest_tmp/build/test/test_io.py'. 411s Hint: make sure your test modules/packages have valid Python names. 411s Traceback: 411s /usr/lib/python3.14/importlib/__init__.py:88: in import_module 411s return _bootstrap._gcd_import(name[level:], package, level) 411s test/test_io.py:11: in 411s from skgenome import tabio 411s /usr/lib/python3/dist-packages/skgenome/__init__.py:1: in 411s from . import tabio 411s /usr/lib/python3/dist-packages/skgenome/tabio/__init__.py:10: in 411s import pandas as pd 411s /usr/lib/python3/dist-packages/pandas/__init__.py:19: in 411s raise ImportError( 411s E ImportError: Unable to import required dependencies: 411s E numpy: Error importing numpy: you should not try to import numpy from 411s E its source directory; please exit the numpy source tree, and relaunch 411s E your python interpreter from there. 411s _______________________ ERROR collecting test/test_r.py ________________________ 411s ImportError while importing test module '/tmp/autopkgtest.ZVFRmj/autopkgtest_tmp/build/test/test_r.py'. 411s Hint: make sure your test modules/packages have valid Python names. 411s Traceback: 411s /usr/lib/python3.14/importlib/__init__.py:88: in import_module 411s return _bootstrap._gcd_import(name[level:], package, level) 411s test/test_r.py:14: in 411s import cnvlib 411s /usr/lib/python3/dist-packages/cnvlib/__init__.py:1: in 411s from skgenome.tabio import write 411s /usr/lib/python3/dist-packages/skgenome/__init__.py:1: in 411s from . import tabio 411s /usr/lib/python3/dist-packages/skgenome/tabio/__init__.py:10: in 411s import pandas as pd 411s /usr/lib/python3/dist-packages/pandas/__init__.py:19: in 411s raise ImportError( 411s E ImportError: Unable to import required dependencies: 411s E numpy: Error importing numpy: you should not try to import numpy from 411s E its source directory; please exit the numpy source tree, and relaunch 411s E your python interpreter from there. 411s =========================== short test summary info ============================ 411s ERROR test/test_cnvlib.py 411s ERROR test/test_commands.py 411s ERROR test/test_genome.py 411s ERROR test/test_io.py 411s ERROR test/test_r.py 411s !!!!!!!!!!!!!!!!!!! Interrupted: 5 errors during collection !!!!!!!!!!!!!!!!!!!! 411s ============================== 5 errors in 0.21s =============================== 411s E: pybuild pybuild:389: test: plugin pyproject failed with: exit code=2: cd /tmp/autopkgtest.ZVFRmj/autopkgtest_tmp/build; python3.14 -m pytest test 411s I: pybuild base:311: cd /tmp/autopkgtest.ZVFRmj/autopkgtest_tmp/build; python3.13 -m pytest test 412s ============================= test session starts ============================== 412s platform linux -- Python 3.13.9, pytest-8.3.5, pluggy-1.6.0 412s rootdir: /tmp/autopkgtest.ZVFRmj/autopkgtest_tmp/build 412s configfile: pyproject.toml 412s plugins: typeguard-4.4.2 412s collected 70 items 412s 415s test/test_cnvlib.py ........... [ 15%] 472s test/test_commands.py ............................. [ 57%] 474s test/test_genome.py ................... [ 84%] 475s test/test_io.py .......... [ 98%] 481s test/test_r.py . [100%] 481s 481s =============================== warnings summary =============================== 481s test/test_commands.py: 81 warnings 481s test/test_r.py: 24 warnings 481s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:317: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 481s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 481s A typical example is when you are setting values in a column of a DataFrame, like: 481s 481s df["col"][row_indexer] = value 481s 481s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 481s 481s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 481s 481s segments.start.iat[0] = bins_start 481s 481s test/test_commands.py: 81 warnings 481s test/test_r.py: 24 warnings 481s /usr/lib/python3/dist-packages/cnvlib/segmentation/__init__.py:318: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0! 481s You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy. 481s A typical example is when you are setting values in a column of a DataFrame, like: 481s 481s df["col"][row_indexer] = value 481s 481s Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`. 481s 481s See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy 481s 481s segments.end.iat[-1] = bins_end 481s 481s -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html 481s ================= 70 passed, 210 warnings in 69.46s (0:01:09) ================== 481s pybuild-autopkgtest: error: pybuild --autopkgtest --test-pytest -i python{version} -p "3.14 3.13" returned exit code 13 481s make: *** [/tmp/Nqyh8dyQ7w/run:4: pybuild-autopkgtest] Error 25 481s pybuild-autopkgtest: error: /tmp/Nqyh8dyQ7w/run pybuild-autopkgtest returned exit code 2 481s autopkgtest [11:59:36]: test pybuild-autopkgtest: -----------------------] 482s autopkgtest [11:59:37]: test pybuild-autopkgtest: - - - - - - - - - - results - - - - - - - - - - 482s pybuild-autopkgtest FAIL non-zero exit status 25 482s autopkgtest [11:59:37]: @@@@@@@@@@@@@@@@@@@@ summary 482s run-unit-test PASS 482s pybuild-autopkgtest FAIL non-zero exit status 25