I: pbuilder: network access will be disabled during build
I: Current time: Fri Feb 14 11:56:16 -12 2025
I: pbuilder-time-stamp: 1739577376
I: Building the build Environment
I: extracting base tarball [/var/cache/pbuilder/unstable-reproducible-base.tgz]
I: copying local configuration
W: --override-config is not set; not updating apt.conf Read the manpage for details.
I: mounting /proc filesystem
I: mounting /sys filesystem
I: creating /{dev,run}/shm
I: mounting /dev/pts filesystem
I: redirecting /dev/ptmx to /dev/pts/ptmx
I: policy-rc.d already exists
I: Copying source file
I: copying [einsteinpy_0.4.0-3.dsc]
I: copying [./einsteinpy_0.4.0.orig.tar.gz]
I: copying [./einsteinpy_0.4.0-3.debian.tar.xz]
I: Extracting source
dpkg-source: warning: cannot verify inline signature for ./einsteinpy_0.4.0-3.dsc: unsupported subcommand
dpkg-source: info: extracting einsteinpy in einsteinpy-0.4.0
dpkg-source: info: unpacking einsteinpy_0.4.0.orig.tar.gz
dpkg-source: info: unpacking einsteinpy_0.4.0-3.debian.tar.xz
dpkg-source: info: using patch list from debian/patches/series
dpkg-source: info: applying Fix-numpy-1.24-compatibility.patch
dpkg-source: info: applying Fixes-NameError-for-numpy-in-tensor_lambdify.patch
I: using fakeroot in build.
I: Installing the build-deps
I: user script /srv/workspace/pbuilder/30296/tmp/hooks/D02_print_environment starting
I: set
  BUILDDIR='/build/reproducible-path'
  BUILDUSERGECOS='first user,first room,first work-phone,first home-phone,first other'
  BUILDUSERNAME='pbuilder1'
  BUILD_ARCH='armhf'
  DEBIAN_FRONTEND='noninteractive'
  DEB_BUILD_OPTIONS='buildinfo=+all reproducible=+all parallel=3 '
  DISTRIBUTION='unstable'
  HOME='/root'
  HOST_ARCH='armhf'
  IFS=' 	
  '
  INVOCATION_ID='fbf00f161477434898d4017811ea9d8e'
  LANG='C'
  LANGUAGE='en_US:en'
  LC_ALL='C'
  MAIL='/var/mail/root'
  OPTIND='1'
  PATH='/usr/sbin:/usr/bin:/sbin:/bin:/usr/games'
  PBCURRENTCOMMANDLINEOPERATION='build'
  PBUILDER_OPERATION='build'
  PBUILDER_PKGDATADIR='/usr/share/pbuilder'
  PBUILDER_PKGLIBDIR='/usr/lib/pbuilder'
  PBUILDER_SYSCONFDIR='/etc'
  PPID='30296'
  PS1='# '
  PS2='> '
  PS4='+ '
  PWD='/'
  SHELL='/bin/bash'
  SHLVL='2'
  SUDO_COMMAND='/usr/bin/timeout -k 18.1h 18h /usr/bin/ionice -c 3 /usr/bin/nice /usr/sbin/pbuilder --build --configfile /srv/reproducible-results/rbuild-debian/r-b-build.KaiZbcI5/pbuilderrc_9Tdf --distribution unstable --hookdir /etc/pbuilder/first-build-hooks --debbuildopts -b --basetgz /var/cache/pbuilder/unstable-reproducible-base.tgz --buildresult /srv/reproducible-results/rbuild-debian/r-b-build.KaiZbcI5/b1 --logfile b1/build.log einsteinpy_0.4.0-3.dsc'
  SUDO_GID='113'
  SUDO_UID='107'
  SUDO_USER='jenkins'
  TERM='unknown'
  TZ='/usr/share/zoneinfo/Etc/GMT+12'
  USER='root'
  _='/usr/bin/systemd-run'
  http_proxy='http://10.0.0.15:3142/'
I: uname -a
  Linux cbxi4a 6.1.0-23-armmp #1 SMP Debian 6.1.99-1 (2024-07-15) armv7l GNU/Linux
I: ls -l /bin
  lrwxrwxrwx 1 root root 7 Nov 22 14:40 /bin -> usr/bin
I: user script /srv/workspace/pbuilder/30296/tmp/hooks/D02_print_environment finished
 -> Attempting to satisfy build-dependencies
 -> Creating pbuilder-satisfydepends-dummy package
Package: pbuilder-satisfydepends-dummy
Version: 0.invalid.0
Architecture: armhf
Maintainer: Debian Pbuilder Team <pbuilder-maint@lists.alioth.debian.org>
Description: Dummy package to satisfy dependencies with aptitude - created by pbuilder
 This package was created automatically by pbuilder to satisfy the
 build-dependencies of the package being currently built.
Depends: debhelper-compat (= 13), dh-python, python3-all, python3-astropy, python3-matplotlib, python3-numpy, python3-plotly, python3-pytest, python3-scipy, python3-setuptools, python3-sympy
dpkg-deb: building package 'pbuilder-satisfydepends-dummy' in '/tmp/satisfydepends-aptitude/pbuilder-satisfydepends-dummy.deb'.
Selecting previously unselected package pbuilder-satisfydepends-dummy.
(Reading database ... 19533 files and directories currently installed.)
Preparing to unpack .../pbuilder-satisfydepends-dummy.deb ...
Unpacking pbuilder-satisfydepends-dummy (0.invalid.0) ...
dpkg: pbuilder-satisfydepends-dummy: dependency problems, but configuring anyway as you requested:
 pbuilder-satisfydepends-dummy depends on debhelper-compat (= 13); however:
  Package debhelper-compat is not installed.
 pbuilder-satisfydepends-dummy depends on dh-python; however:
  Package dh-python is not installed.
 pbuilder-satisfydepends-dummy depends on python3-all; however:
  Package python3-all is not installed.
 pbuilder-satisfydepends-dummy depends on python3-astropy; however:
  Package python3-astropy is not installed.
 pbuilder-satisfydepends-dummy depends on python3-matplotlib; however:
  Package python3-matplotlib is not installed.
 pbuilder-satisfydepends-dummy depends on python3-numpy; however:
  Package python3-numpy is not installed.
 pbuilder-satisfydepends-dummy depends on python3-plotly; however:
  Package python3-plotly is not installed.
 pbuilder-satisfydepends-dummy depends on python3-pytest; however:
  Package python3-pytest is not installed.
 pbuilder-satisfydepends-dummy depends on python3-scipy; however:
  Package python3-scipy is not installed.
 pbuilder-satisfydepends-dummy depends on python3-setuptools; however:
  Package python3-setuptools is not installed.
 pbuilder-satisfydepends-dummy depends on python3-sympy; however:
  Package python3-sympy is not installed.

Setting up pbuilder-satisfydepends-dummy (0.invalid.0) ...
Reading package lists...
Building dependency tree...
Reading state information...
Initializing package states...
Writing extended state information...
Building tag database...
pbuilder-satisfydepends-dummy is already installed at the requested version (0.invalid.0)
pbuilder-satisfydepends-dummy is already installed at the requested version (0.invalid.0)
The following NEW packages will be installed:
  autoconf{a} automake{a} autopoint{a} autotools-dev{a} blt{a} bsdextrautils{a} ca-certificates{a} debhelper{a} dh-autoreconf{a} dh-python{a} dh-strip-nondeterminism{a} dwz{a} file{a} fontconfig-config{a} fonts-dejavu-core{a} fonts-dejavu-mono{a} fonts-lyx{a} gettext{a} gettext-base{a} groff-base{a} intltool-debian{a} libarchive-zip-perl{a} libblas3{a} libbrotli1{a} libcom-err2{a} libdebhelper-perl{a} libdeflate0{a} libelf1t64{a} liberfa1{a} libexpat1{a} libffi8{a} libfile-stripnondeterminism-perl{a} libfontconfig1{a} libfreetype6{a} libfribidi0{a} libgcrypt20{a} libgfortran5{a} libglib2.0-0t64{a} libgpg-error0{a} libgraphite2-3{a} libgssapi-krb5-2{a} libharfbuzz0b{a} libicu72{a} libimagequant0{a} libjbig0{a} libjpeg62-turbo{a} libjs-jquery{a} libjs-jquery-hotkeys{a} libjs-jquery-isonscreen{a} libjs-jquery-metadata{a} libjs-jquery-tablesorter{a} libjs-jquery-throttle-debounce{a} libjs-jquery-ui{a} libjs-sphinxdoc{a} libjs-underscore{a} libk5crypto3{a} libkeyutils1{a} libkrb5-3{a} libkrb5support0{a} liblapack3{a} liblbfgsb0{a} liblcms2-2{a} liblerc4{a} libmagic-mgc{a} libmagic1t64{a} libnsl2{a} libopenjp2-7{a} libpipeline1{a} libpng16-16t64{a} libpython3-stdlib{a} libpython3.12-minimal{a} libpython3.12-stdlib{a} libpython3.13-minimal{a} libpython3.13-stdlib{a} libqhull-r8.0{a} libraqm0{a} libreadline8t64{a} libsharpyuv0{a} libtcl8.6{a} libtiff6{a} libtirpc-common{a} libtirpc3t64{a} libtk8.6{a} libtool{a} libuchardet0{a} libunistring5{a} libwcs8{a} libwebp7{a} libwebpdemux2{a} libwebpmux3{a} libx11-6{a} libx11-data{a} libxau6{a} libxcb1{a} libxdmcp6{a} libxext6{a} libxft2{a} libxml2{a} libxrender1{a} libxslt1.1{a} libxss1{a} libyaml-0-2{a} m4{a} man-db{a} media-types{a} netbase{a} openssl{a} po-debconf{a} python-matplotlib-data{a} python3{a} python3-all{a} python3-astropy{a} python3-astropy-iers-data{a} python3-attr{a} python3-autocommand{a} python3-brotli{a} python3-certifi{a} python3-chardet{a} python3-charset-normalizer{a} python3-configobj{a} python3-contourpy{a} python3-coverage{a} python3-cycler{a} python3-dateutil{a} python3-decorator{a} python3-erfa{a} python3-fastjsonschema{a} python3-fonttools{a} python3-fs{a} python3-hypothesis{a} python3-idna{a} python3-inflect{a} python3-iniconfig{a} python3-jaraco.context{a} python3-jaraco.functools{a} python3-jaraco.text{a} python3-jsonschema{a} python3-jsonschema-specifications{a} python3-jupyter-core{a} python3-kiwisolver{a} python3-lxml{a} python3-lz4{a} python3-matplotlib{a} python3-minimal{a} python3-more-itertools{a} python3-mpmath{a} python3-nbformat{a} python3-numpy{a} python3-numpy-dev{a} python3-packaging{a} python3-pil{a} python3-pil.imagetk{a} python3-pkg-resources{a} python3-platformdirs{a} python3-plotly{a} python3-pluggy{a} python3-ply{a} python3-pyparsing{a} python3-pytest{a} python3-pytest-arraydiff{a} python3-pytest-astropy{a} python3-pytest-astropy-header{a} python3-pytest-cov{a} python3-pytest-doctestplus{a} python3-pytest-filter-subpackage{a} python3-pytest-mock{a} python3-pytest-remotedata{a} python3-pytz{a} python3-referencing{a} python3-requests{a} python3-rpds-py{a} python3-scipy{a} python3-setuptools{a} python3-sortedcontainers{a} python3-sympy{a} python3-tenacity{a} python3-tk{a} python3-traitlets{a} python3-typeguard{a} python3-typing-extensions{a} python3-tz{a} python3-ufolib2{a} python3-urllib3{a} python3-yaml{a} python3-zipp{a} python3.12{a} python3.12-minimal{a} python3.12-tk{a} python3.13{a} python3.13-minimal{a} python3.13-tk{a} readline-common{a} sensible-utils{a} tk8.6-blt2.5{a} tzdata{a} unicode-data{a} x11-common{a} 
The following packages are RECOMMENDED but will NOT be installed:
  curl isympy-common javascript-common krb5-locales libarchive-cpio-perl libglib2.0-data libgpg-error-l10n libjs-jquery-datatables libltdl-dev libmail-sendmail-perl lynx python3-bs4 python3-click python3-cssselect python3-fqdn python3-html5lib python3-isoduration python3-json-pointer python3-olefile python3-pooch python3-pygments python3-rfc3339-validator python3-rfc3986-validator python3-rfc3987 python3-uritemplate python3-webcolors shared-mime-info wget xdg-user-dirs 
0 packages upgraded, 197 newly installed, 0 to remove and 0 not upgraded.
Need to get 109 MB of archives. After unpacking 508 MB will be used.
Writing extended state information...
Get: 1 http://deb.debian.org/debian unstable/main armhf libpython3.13-minimal armhf 3.13.2-1 [845 kB]
Get: 2 http://deb.debian.org/debian unstable/main armhf libexpat1 armhf 2.6.4-1 [83.5 kB]
Get: 3 http://deb.debian.org/debian unstable/main armhf python3.13-minimal armhf 3.13.2-1 [1842 kB]
Get: 4 http://deb.debian.org/debian unstable/main armhf python3-minimal armhf 3.13.1-2 [27.0 kB]
Get: 5 http://deb.debian.org/debian unstable/main armhf media-types all 11.0.0 [27.6 kB]
Get: 6 http://deb.debian.org/debian unstable/main armhf netbase all 6.4 [12.8 kB]
Get: 7 http://deb.debian.org/debian unstable/main armhf tzdata all 2025a-2 [259 kB]
Get: 8 http://deb.debian.org/debian unstable/main armhf libffi8 armhf 3.4.7-1 [20.3 kB]
Get: 9 http://deb.debian.org/debian unstable/main armhf readline-common all 8.2-6 [69.4 kB]
Get: 10 http://deb.debian.org/debian unstable/main armhf libreadline8t64 armhf 8.2-6 [146 kB]
Get: 11 http://deb.debian.org/debian unstable/main armhf libpython3.13-stdlib armhf 3.13.2-1 [1853 kB]
Get: 12 http://deb.debian.org/debian unstable/main armhf python3.13 armhf 3.13.2-1 [745 kB]
Get: 13 http://deb.debian.org/debian unstable/main armhf libpython3-stdlib armhf 3.13.1-2 [9952 B]
Get: 14 http://deb.debian.org/debian unstable/main armhf python3 armhf 3.13.1-2 [28.0 kB]
Get: 15 http://deb.debian.org/debian unstable/main armhf libpython3.12-minimal armhf 3.12.9-1 [803 kB]
Get: 16 http://deb.debian.org/debian unstable/main armhf python3.12-minimal armhf 3.12.9-1 [1810 kB]
Get: 17 http://deb.debian.org/debian unstable/main armhf sensible-utils all 0.0.24 [24.8 kB]
Get: 18 http://deb.debian.org/debian unstable/main armhf openssl armhf 3.4.1-1 [1393 kB]
Get: 19 http://deb.debian.org/debian unstable/main armhf ca-certificates all 20241223 [164 kB]
Get: 20 http://deb.debian.org/debian unstable/main armhf libmagic-mgc armhf 1:5.45-3+b1 [314 kB]
Get: 21 http://deb.debian.org/debian unstable/main armhf libmagic1t64 armhf 1:5.45-3+b1 [98.5 kB]
Get: 22 http://deb.debian.org/debian unstable/main armhf file armhf 1:5.45-3+b1 [42.3 kB]
Get: 23 http://deb.debian.org/debian unstable/main armhf gettext-base armhf 0.23.1-1 [238 kB]
Get: 24 http://deb.debian.org/debian unstable/main armhf libuchardet0 armhf 0.0.8-1+b2 [65.6 kB]
Get: 25 http://deb.debian.org/debian unstable/main armhf groff-base armhf 1.23.0-7 [1095 kB]
Get: 26 http://deb.debian.org/debian unstable/main armhf bsdextrautils armhf 2.40.4-3 [84.6 kB]
Get: 27 http://deb.debian.org/debian unstable/main armhf libpipeline1 armhf 1.5.8-1 [35.0 kB]
Get: 28 http://deb.debian.org/debian unstable/main armhf man-db armhf 2.13.0-1 [1382 kB]
Get: 29 http://deb.debian.org/debian unstable/main armhf m4 armhf 1.4.19-5 [272 kB]
Get: 30 http://deb.debian.org/debian unstable/main armhf autoconf all 2.72-3 [493 kB]
Get: 31 http://deb.debian.org/debian unstable/main armhf autotools-dev all 20220109.1 [51.6 kB]
Get: 32 http://deb.debian.org/debian unstable/main armhf automake all 1:1.17-3 [862 kB]
Get: 33 http://deb.debian.org/debian unstable/main armhf autopoint all 0.23.1-1 [770 kB]
Get: 34 http://deb.debian.org/debian unstable/main armhf libtcl8.6 armhf 8.6.16+dfsg-1 [934 kB]
Get: 35 http://deb.debian.org/debian unstable/main armhf libbrotli1 armhf 1.1.0-2+b6 [282 kB]
Get: 36 http://deb.debian.org/debian unstable/main armhf libpng16-16t64 armhf 1.6.46-4 [267 kB]
Get: 37 http://deb.debian.org/debian unstable/main armhf libfreetype6 armhf 2.13.3+dfsg-1 [385 kB]
Get: 38 http://deb.debian.org/debian unstable/main armhf fonts-dejavu-mono all 2.37-8 [489 kB]
Get: 39 http://deb.debian.org/debian unstable/main armhf fonts-dejavu-core all 2.37-8 [840 kB]
Get: 40 http://deb.debian.org/debian unstable/main armhf fontconfig-config armhf 2.15.0-2 [317 kB]
Get: 41 http://deb.debian.org/debian unstable/main armhf libfontconfig1 armhf 2.15.0-2 [371 kB]
Get: 42 http://deb.debian.org/debian unstable/main armhf libxau6 armhf 1:1.0.11-1 [19.7 kB]
Get: 43 http://deb.debian.org/debian unstable/main armhf libxdmcp6 armhf 1:1.1.5-1 [26.4 kB]
Get: 44 http://deb.debian.org/debian unstable/main armhf libxcb1 armhf 1.17.0-2+b1 [140 kB]
Get: 45 http://deb.debian.org/debian unstable/main armhf libx11-data all 2:1.8.10-2 [337 kB]
Get: 46 http://deb.debian.org/debian unstable/main armhf libx11-6 armhf 2:1.8.10-2 [750 kB]
Get: 47 http://deb.debian.org/debian unstable/main armhf libxrender1 armhf 1:0.9.10-1.1+b4 [25.0 kB]
Get: 48 http://deb.debian.org/debian unstable/main armhf libxft2 armhf 2.3.6-1+b4 [46.4 kB]
Get: 49 http://deb.debian.org/debian unstable/main armhf libxext6 armhf 2:1.3.4-1+b3 [45.2 kB]
Get: 50 http://deb.debian.org/debian unstable/main armhf x11-common all 1:7.7+24 [217 kB]
Get: 51 http://deb.debian.org/debian unstable/main armhf libxss1 armhf 1:1.2.3-1+b3 [16.4 kB]
Get: 52 http://deb.debian.org/debian unstable/main armhf libtk8.6 armhf 8.6.16-1 [698 kB]
Get: 53 http://deb.debian.org/debian unstable/main armhf tk8.6-blt2.5 armhf 2.5.3+dfsg-7 [481 kB]
Get: 54 http://deb.debian.org/debian unstable/main armhf blt armhf 2.5.3+dfsg-7 [6024 B]
Get: 55 http://deb.debian.org/debian unstable/main armhf libdebhelper-perl all 13.24.1 [90.9 kB]
Get: 56 http://deb.debian.org/debian unstable/main armhf libtool all 2.5.4-3 [539 kB]
Get: 57 http://deb.debian.org/debian unstable/main armhf dh-autoreconf all 20 [17.1 kB]
Get: 58 http://deb.debian.org/debian unstable/main armhf libarchive-zip-perl all 1.68-1 [104 kB]
Get: 59 http://deb.debian.org/debian unstable/main armhf libfile-stripnondeterminism-perl all 1.14.1-2 [19.7 kB]
Get: 60 http://deb.debian.org/debian unstable/main armhf dh-strip-nondeterminism all 1.14.1-2 [8620 B]
Get: 61 http://deb.debian.org/debian unstable/main armhf libelf1t64 armhf 0.192-4 [184 kB]
Get: 62 http://deb.debian.org/debian unstable/main armhf dwz armhf 0.15-1+b2 [106 kB]
Get: 63 http://deb.debian.org/debian unstable/main armhf libunistring5 armhf 1.3-1 [444 kB]
Get: 64 http://deb.debian.org/debian unstable/main armhf libicu72 armhf 72.1-6 [9086 kB]
Get: 65 http://deb.debian.org/debian unstable/main armhf libxml2 armhf 2.12.7+dfsg+really2.9.14-0.2+b1 [605 kB]
Get: 66 http://deb.debian.org/debian unstable/main armhf gettext armhf 0.23.1-1 [1560 kB]
Get: 67 http://deb.debian.org/debian unstable/main armhf intltool-debian all 0.35.0+20060710.6 [22.9 kB]
Get: 68 http://deb.debian.org/debian unstable/main armhf po-debconf all 1.0.21+nmu1 [248 kB]
Get: 69 http://deb.debian.org/debian unstable/main armhf debhelper all 13.24.1 [920 kB]
Get: 70 http://deb.debian.org/debian unstable/main armhf python3-autocommand all 2.2.2-3 [13.6 kB]
Get: 71 http://deb.debian.org/debian unstable/main armhf python3-more-itertools all 10.6.0-1 [65.3 kB]
Get: 72 http://deb.debian.org/debian unstable/main armhf python3-typing-extensions all 4.12.2-2 [73.0 kB]
Get: 73 http://deb.debian.org/debian unstable/main armhf python3-typeguard all 4.4.1-1 [37.0 kB]
Get: 74 http://deb.debian.org/debian unstable/main armhf python3-inflect all 7.3.1-2 [32.4 kB]
Get: 75 http://deb.debian.org/debian unstable/main armhf python3-jaraco.context all 6.0.0-1 [7984 B]
Get: 76 http://deb.debian.org/debian unstable/main armhf python3-jaraco.functools all 4.1.0-1 [12.0 kB]
Get: 77 http://deb.debian.org/debian unstable/main armhf python3-pkg-resources all 75.6.0-1 [222 kB]
Get: 78 http://deb.debian.org/debian unstable/main armhf python3-jaraco.text all 4.0.0-1 [11.4 kB]
Get: 79 http://deb.debian.org/debian unstable/main armhf python3-zipp all 3.21.0-1 [10.6 kB]
Get: 80 http://deb.debian.org/debian unstable/main armhf python3-setuptools all 75.6.0-1 [720 kB]
Get: 81 http://deb.debian.org/debian unstable/main armhf dh-python all 6.20250108 [113 kB]
Get: 82 http://deb.debian.org/debian unstable/main armhf fonts-lyx all 2.4.3-1 [190 kB]
Get: 83 http://deb.debian.org/debian unstable/main armhf libblas3 armhf 3.12.1-2 [116 kB]
Get: 84 http://deb.debian.org/debian unstable/main armhf libcom-err2 armhf 1.47.2-1 [23.3 kB]
Get: 85 http://deb.debian.org/debian unstable/main armhf libdeflate0 armhf 1.23-1+b1 [36.7 kB]
Get: 86 http://deb.debian.org/debian unstable/main armhf liberfa1 armhf 2.0.1-1+b2 [119 kB]
Get: 87 http://deb.debian.org/debian unstable/main armhf libfribidi0 armhf 1.0.16-1 [24.6 kB]
Get: 88 http://deb.debian.org/debian unstable/main armhf libgpg-error0 armhf 1.51-3 [71.9 kB]
Get: 89 http://deb.debian.org/debian unstable/main armhf libgcrypt20 armhf 1.11.0-7 [727 kB]
Get: 90 http://deb.debian.org/debian unstable/main armhf libgfortran5 armhf 14.2.0-17 [263 kB]
Get: 91 http://deb.debian.org/debian unstable/main armhf libglib2.0-0t64 armhf 2.83.3-2 [1337 kB]
Get: 92 http://deb.debian.org/debian unstable/main armhf libgraphite2-3 armhf 1.3.14-2+b1 [63.1 kB]
Get: 93 http://deb.debian.org/debian unstable/main armhf libkrb5support0 armhf 1.21.3-4 [30.3 kB]
Get: 94 http://deb.debian.org/debian unstable/main armhf libk5crypto3 armhf 1.21.3-4 [75.8 kB]
Get: 95 http://deb.debian.org/debian unstable/main armhf libkeyutils1 armhf 1.6.3-4 [8096 B]
Get: 96 http://deb.debian.org/debian unstable/main armhf libkrb5-3 armhf 1.21.3-4 [285 kB]
Get: 97 http://deb.debian.org/debian unstable/main armhf libgssapi-krb5-2 armhf 1.21.3-4 [116 kB]
Get: 98 http://deb.debian.org/debian unstable/main armhf libharfbuzz0b armhf 10.2.0-1 [419 kB]
Get: 99 http://deb.debian.org/debian unstable/main armhf libimagequant0 armhf 2.18.0-1+b2 [30.5 kB]
Get: 100 http://deb.debian.org/debian unstable/main armhf libjbig0 armhf 2.1-6.1+b2 [27.3 kB]
Get: 101 http://deb.debian.org/debian unstable/main armhf libjpeg62-turbo armhf 1:2.1.5-3+b1 [145 kB]
Get: 102 http://deb.debian.org/debian unstable/main armhf libjs-jquery all 3.6.1+dfsg+~3.5.14-1 [326 kB]
Get: 103 http://deb.debian.org/debian unstable/main armhf libjs-jquery-hotkeys all 0.2.0-1 [12.6 kB]
Get: 104 http://deb.debian.org/debian unstable/main armhf libjs-jquery-isonscreen all 1.2.0-1.1 [3196 B]
Get: 105 http://deb.debian.org/debian unstable/main armhf libjs-jquery-metadata all 12-4 [6532 B]
Get: 106 http://deb.debian.org/debian unstable/main armhf libjs-jquery-tablesorter all 1:2.31.3+dfsg1-4 [184 kB]
Get: 107 http://deb.debian.org/debian unstable/main armhf libjs-jquery-throttle-debounce all 1.1+dfsg.1-2 [12.2 kB]
Get: 108 http://deb.debian.org/debian unstable/main armhf libjs-jquery-ui all 1.13.2+dfsg-1 [250 kB]
Get: 109 http://deb.debian.org/debian unstable/main armhf libjs-underscore all 1.13.4~dfsg+~1.11.4-3 [116 kB]
Get: 110 http://deb.debian.org/debian unstable/main armhf libjs-sphinxdoc all 8.1.3-4 [30.4 kB]
Get: 111 http://deb.debian.org/debian unstable/main armhf liblapack3 armhf 3.12.1-2 [1835 kB]
Get: 112 http://deb.debian.org/debian unstable/main armhf liblbfgsb0 armhf 3.0+dfsg.4-1+b2 [25.6 kB]
Get: 113 http://deb.debian.org/debian unstable/main armhf liblcms2-2 armhf 2.16-2 [131 kB]
Get: 114 http://deb.debian.org/debian unstable/main armhf liblerc4 armhf 4.0.0+ds-5 [146 kB]
Get: 115 http://deb.debian.org/debian unstable/main armhf libtirpc-common all 1.3.4+ds-1.3 [10.9 kB]
Get: 116 http://deb.debian.org/debian unstable/main armhf libtirpc3t64 armhf 1.3.4+ds-1.3+b1 [71.3 kB]
Get: 117 http://deb.debian.org/debian unstable/main armhf libnsl2 armhf 1.3.0-3+b3 [35.0 kB]
Get: 118 http://deb.debian.org/debian unstable/main armhf libopenjp2-7 armhf 2.5.3-1 [184 kB]
Get: 119 http://deb.debian.org/debian unstable/main armhf libpython3.12-stdlib armhf 3.12.9-1 [1833 kB]
Get: 120 http://deb.debian.org/debian unstable/main armhf libqhull-r8.0 armhf 2020.2-6+b2 [220 kB]
Get: 121 http://deb.debian.org/debian unstable/main armhf libraqm0 armhf 0.10.2-1 [11.6 kB]
Get: 122 http://deb.debian.org/debian unstable/main armhf libsharpyuv0 armhf 1.5.0-0.1 [114 kB]
Get: 123 http://deb.debian.org/debian unstable/main armhf libwebp7 armhf 1.5.0-0.1 [273 kB]
Get: 124 http://deb.debian.org/debian unstable/main armhf libtiff6 armhf 4.5.1+git230720-5 [302 kB]
Get: 125 http://deb.debian.org/debian unstable/main armhf libwcs8 armhf 8.4+ds-1 [251 kB]
Get: 126 http://deb.debian.org/debian unstable/main armhf libwebpdemux2 armhf 1.5.0-0.1 [112 kB]
Get: 127 http://deb.debian.org/debian unstable/main armhf libwebpmux3 armhf 1.5.0-0.1 [123 kB]
Get: 128 http://deb.debian.org/debian unstable/main armhf libxslt1.1 armhf 1.1.35-1.1+b1 [212 kB]
Get: 129 http://deb.debian.org/debian unstable/main armhf libyaml-0-2 armhf 0.2.5-2 [45.4 kB]
Get: 130 http://deb.debian.org/debian unstable/main armhf python-matplotlib-data all 3.8.3-3 [2731 kB]
Get: 131 http://deb.debian.org/debian unstable/main armhf python3.12 armhf 3.12.9-1 [681 kB]
Get: 132 http://deb.debian.org/debian unstable/main armhf python3-all armhf 3.13.1-2 [1056 B]
Get: 133 http://deb.debian.org/debian unstable/main armhf python3-configobj all 5.0.9-1 [34.0 kB]
Get: 134 http://deb.debian.org/debian unstable/main armhf python3-iniconfig all 1.1.1-2 [6396 B]
Get: 135 http://deb.debian.org/debian unstable/main armhf python3-packaging all 24.2-1 [55.3 kB]
Get: 136 http://deb.debian.org/debian unstable/main armhf python3-pluggy all 1.5.0-1 [26.9 kB]
Get: 137 http://deb.debian.org/debian unstable/main armhf python3-pytest all 8.3.4-1 [250 kB]
Get: 138 http://deb.debian.org/debian unstable/main armhf python3-attr all 25.1.0-1 [68.7 kB]
Get: 139 http://deb.debian.org/debian unstable/main armhf python3-dateutil all 2.9.0-3 [79.3 kB]
Get: 140 http://deb.debian.org/debian unstable/main armhf python3-sortedcontainers all 2.4.0-2 [31.9 kB]
Get: 141 http://deb.debian.org/debian unstable/main armhf python3-hypothesis all 6.125.2-1 [328 kB]
Get: 142 http://deb.debian.org/debian unstable/main armhf python3-numpy-dev armhf 1:2.2.2+ds-2 [132 kB]
Get: 143 http://deb.debian.org/debian unstable/main armhf python3-numpy armhf 1:2.2.2+ds-2 [3882 kB]
Get: 144 http://deb.debian.org/debian unstable/main armhf python3-pytest-arraydiff all 0.6.1-3 [10.5 kB]
Get: 145 http://deb.debian.org/debian unstable/main armhf python3-pytest-astropy-header all 0.2.2-2 [9000 B]
Get: 146 http://deb.debian.org/debian unstable/main armhf python3-coverage armhf 7.6.0+dfsg1-2+b1 [176 kB]
Get: 147 http://deb.debian.org/debian unstable/main armhf python3-pytest-cov all 5.0.0-1 [26.8 kB]
Get: 148 http://deb.debian.org/debian unstable/main armhf python3-pytest-doctestplus all 1.4.0-1 [25.7 kB]
Get: 149 http://deb.debian.org/debian unstable/main armhf python3-pytest-filter-subpackage all 0.2.0-1 [6312 B]
Get: 150 http://deb.debian.org/debian unstable/main armhf python3-pytest-mock all 3.14.0-2 [18.3 kB]
Get: 151 http://deb.debian.org/debian unstable/main armhf python3-pytest-remotedata all 0.4.1-1 [9292 B]
Get: 152 http://deb.debian.org/debian unstable/main armhf python3-pytest-astropy all 0.11.0-1 [6716 B]
Get: 153 http://deb.debian.org/debian unstable/main armhf python3-ply all 3.11-7 [62.6 kB]
Get: 154 http://deb.debian.org/debian unstable/main armhf python3-astropy-iers-data all 0.2025.1.6.0.33.42-1 [1091 kB]
Get: 155 http://deb.debian.org/debian unstable/main armhf python3-erfa armhf 2.0.1.5+ds-1+b1 [305 kB]
Get: 156 http://deb.debian.org/debian unstable/main armhf python3-yaml armhf 6.0.2-1+b1 [158 kB]
Get: 157 http://deb.debian.org/debian unstable/main armhf python3-astropy armhf 7.0.1-1 [3807 kB]
Get: 158 http://deb.debian.org/debian unstable/main armhf python3-brotli armhf 1.1.0-2+b6 [302 kB]
Get: 159 http://deb.debian.org/debian unstable/main armhf python3-certifi all 2025.1.31+ds-1 [9652 B]
Get: 160 http://deb.debian.org/debian unstable/main armhf python3-chardet all 5.2.0+dfsg-2 [108 kB]
Get: 161 http://deb.debian.org/debian unstable/main armhf python3-charset-normalizer armhf 3.4.0-1+b1 [126 kB]
Get: 162 http://deb.debian.org/debian unstable/main armhf python3-contourpy armhf 1.3.1-1 [217 kB]
Get: 163 http://deb.debian.org/debian unstable/main armhf python3-cycler all 0.12.1-1 [9496 B]
Get: 164 http://deb.debian.org/debian unstable/main armhf python3-decorator all 5.1.1-5 [15.1 kB]
Get: 165 http://deb.debian.org/debian unstable/main armhf python3-fastjsonschema all 2.21.1-1 [22.4 kB]
Get: 166 http://deb.debian.org/debian unstable/main armhf python3-pytz all 2025.1-3 [150 kB]
Get: 167 http://deb.debian.org/debian unstable/main armhf python3-tz all 2025.1-3 [4004 B]
Get: 168 http://deb.debian.org/debian unstable/main armhf python3-platformdirs all 4.3.6-1 [16.6 kB]
Get: 169 http://deb.debian.org/debian unstable/main armhf python3-fs all 2.4.16-6 [95.1 kB]
Get: 170 http://deb.debian.org/debian unstable/main armhf python3-lxml armhf 5.3.1-1 [1474 kB]
Get: 171 http://deb.debian.org/debian unstable/main armhf python3-lz4 armhf 4.4.0+dfsg-1 [23.9 kB]
Get: 172 http://deb.debian.org/debian unstable/main armhf python3-scipy armhf 1.14.1-4 [18.5 MB]
Get: 173 http://deb.debian.org/debian unstable/main armhf python3-mpmath all 1.3.0-1 [419 kB]
Get: 174 http://deb.debian.org/debian unstable/main armhf python3-sympy all 1.13.3-2 [4148 kB]
Get: 175 http://deb.debian.org/debian unstable/main armhf python3-ufolib2 all 0.17.0+dfsg1-1 [33.0 kB]
Get: 176 http://deb.debian.org/debian unstable/main armhf unicode-data all 15.1.0-1 [8547 kB]
Get: 177 http://deb.debian.org/debian unstable/main armhf python3-fonttools armhf 4.55.3-2 [1529 kB]
Get: 178 http://deb.debian.org/debian unstable/main armhf python3-idna all 3.10-1 [42.0 kB]
Get: 179 http://deb.debian.org/debian unstable/main armhf python3-rpds-py armhf 0.21.0-2 [255 kB]
Get: 180 http://deb.debian.org/debian unstable/main armhf python3-referencing all 0.35.1-2 [23.5 kB]
Get: 181 http://deb.debian.org/debian unstable/main armhf python3-jsonschema-specifications all 2023.12.1-2 [9020 B]
Get: 182 http://deb.debian.org/debian unstable/main armhf python3-jsonschema all 4.19.2-6 [71.4 kB]
Get: 183 http://deb.debian.org/debian unstable/main armhf python3-traitlets all 5.14.3+really5.14.3-1 [70.9 kB]
Get: 184 http://deb.debian.org/debian unstable/main armhf python3-jupyter-core all 5.7.2-5 [35.3 kB]
Get: 185 http://deb.debian.org/debian unstable/main armhf python3-kiwisolver armhf 1.4.7-3 [64.9 kB]
Get: 186 http://deb.debian.org/debian unstable/main armhf python3-pil armhf 11.1.0-5 [496 kB]
Get: 187 http://deb.debian.org/debian unstable/main armhf python3.12-tk armhf 3.12.9-1 [109 kB]
Get: 188 http://deb.debian.org/debian unstable/main armhf python3.13-tk armhf 3.13.2-1 [100 kB]
Get: 189 http://deb.debian.org/debian unstable/main armhf python3-tk armhf 3.13.1-1 [9464 B]
Get: 190 http://deb.debian.org/debian unstable/main armhf python3-pil.imagetk armhf 11.1.0-5 [81.1 kB]
Get: 191 http://deb.debian.org/debian unstable/main armhf python3-pyparsing all 3.1.2-1 [146 kB]
Get: 192 http://deb.debian.org/debian unstable/main armhf python3-matplotlib armhf 3.8.3-3+b2 [5583 kB]
Get: 193 http://deb.debian.org/debian unstable/main armhf python3-nbformat all 5.9.1-1 [45.1 kB]
Get: 194 http://deb.debian.org/debian unstable/main armhf python3-urllib3 all 2.3.0-1 [114 kB]
Get: 195 http://deb.debian.org/debian unstable/main armhf python3-requests all 2.32.3+dfsg-1 [71.9 kB]
Get: 196 http://deb.debian.org/debian unstable/main armhf python3-tenacity all 8.4.2+really8.4.1-2 [46.0 kB]
Get: 197 http://deb.debian.org/debian unstable/main armhf python3-plotly all 5.20.0+dfsg-1 [2726 kB]
Fetched 109 MB in 10s (10.7 MB/s)
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Adding 'diversion of /lib/arm-linux-gnueabihf/libhistory.so.8 to /lib/arm-linux-gnueabihf/libhistory.so.8.usr-is-merged by libreadline8t64'
Adding 'diversion of /lib/arm-linux-gnueabihf/libhistory.so.8.2 to /lib/arm-linux-gnueabihf/libhistory.so.8.2.usr-is-merged by libreadline8t64'
Adding 'diversion of /lib/arm-linux-gnueabihf/libreadline.so.8 to /lib/arm-linux-gnueabihf/libreadline.so.8.usr-is-merged by libreadline8t64'
Adding 'diversion of /lib/arm-linux-gnueabihf/libreadline.so.8.2 to /lib/arm-linux-gnueabihf/libreadline.so.8.2.usr-is-merged by libreadline8t64'
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Current default time zone: 'Etc/UTC'
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Not building database; man-db/auto-update is not 'true'.
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done.
Reading package lists...
Building dependency tree...
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 -> Finished parsing the build-deps
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fakeroot is already the newest version (1.37-1).
0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
I: Building the package
I: Running cd /build/reproducible-path/einsteinpy-0.4.0/ && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games" HOME="/nonexistent/first-build" dpkg-buildpackage -us -uc -b && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games" HOME="/nonexistent/first-build" dpkg-genchanges -S  > ../einsteinpy_0.4.0-3_source.changes
dpkg-buildpackage: info: source package einsteinpy
dpkg-buildpackage: info: source version 0.4.0-3
dpkg-buildpackage: info: source distribution unstable
dpkg-buildpackage: info: source changed by Ole Streicher <olebole@debian.org>
 dpkg-source --before-build .
dpkg-buildpackage: info: host architecture armhf
 debian/rules clean
dh clean --with python3 --buildsystem=pybuild
   dh_auto_clean -O--buildsystem=pybuild
I: pybuild base:311: python3.12 setup.py clean 
/usr/lib/python3/dist-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'setup_cfg'
  warnings.warn(msg)
running clean
removing '/build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build' (and everything under it)
'build/bdist.linux-armv7l' does not exist -- can't clean it
'build/scripts-3.12' does not exist -- can't clean it
I: pybuild base:311: python3.13 setup.py clean 
/usr/lib/python3/dist-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'setup_cfg'
  warnings.warn(msg)
running clean
removing '/build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build' (and everything under it)
'build/bdist.linux-armv7l' does not exist -- can't clean it
'build/scripts-3.13' does not exist -- can't clean it
   dh_autoreconf_clean -O--buildsystem=pybuild
   dh_clean -O--buildsystem=pybuild
 debian/rules binary
dh binary --with python3 --buildsystem=pybuild
   dh_update_autotools_config -O--buildsystem=pybuild
   dh_autoreconf -O--buildsystem=pybuild
   dh_auto_configure -O--buildsystem=pybuild
I: pybuild base:311: python3.12 setup.py config 
/usr/lib/python3/dist-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'setup_cfg'
  warnings.warn(msg)
running config
I: pybuild base:311: python3.13 setup.py config 
/usr/lib/python3/dist-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'setup_cfg'
  warnings.warn(msg)
running config
   dh_auto_build -O--buildsystem=pybuild
I: pybuild base:311: /usr/bin/python3.12 setup.py build 
/usr/lib/python3/dist-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'setup_cfg'
  warnings.warn(msg)
running build
running build_py
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy
copying src/einsteinpy/examples.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy
copying src/einsteinpy/ijit.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy
copying src/einsteinpy/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy
copying src/einsteinpy/bodies.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy
copying src/einsteinpy/constant.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy
copying src/einsteinpy/units.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/utils
copying src/einsteinpy/utils/dual.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/utils
copying src/einsteinpy/utils/scalar_factor.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/utils
copying src/einsteinpy/utils/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/utils
copying src/einsteinpy/utils/exceptions.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/utils
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/metric
copying src/einsteinpy/metric/kerr.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/metric
copying src/einsteinpy/metric/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/metric
copying src/einsteinpy/metric/base_metric.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/metric
copying src/einsteinpy/metric/kerrnewman.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/metric
copying src/einsteinpy/metric/schwarzschild.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/metric
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic
copying src/einsteinpy/symbolic/weyl.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic
copying src/einsteinpy/symbolic/christoffel.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic
copying src/einsteinpy/symbolic/riemann.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic
copying src/einsteinpy/symbolic/metric.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic
copying src/einsteinpy/symbolic/vector.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic
copying src/einsteinpy/symbolic/constants.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic
copying src/einsteinpy/symbolic/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic
copying src/einsteinpy/symbolic/einstein.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic
copying src/einsteinpy/symbolic/stress_energy_momentum.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic
copying src/einsteinpy/symbolic/schouten.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic
copying src/einsteinpy/symbolic/ricci.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic
copying src/einsteinpy/symbolic/helpers.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic
copying src/einsteinpy/symbolic/tensor.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/rays
copying src/einsteinpy/rays/shadow.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/rays
copying src/einsteinpy/rays/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/rays
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/geodesic
copying src/einsteinpy/geodesic/geodesic.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/geodesic
copying src/einsteinpy/geodesic/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/geodesic
copying src/einsteinpy/geodesic/utils.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/geodesic
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/plotting
copying src/einsteinpy/plotting/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/plotting
copying src/einsteinpy/plotting/fractal.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/plotting
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/hypersurface
copying src/einsteinpy/hypersurface/schwarzschildembedding.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/hypersurface
copying src/einsteinpy/hypersurface/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/hypersurface
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/coordinates
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copying src/einsteinpy/coordinates/conversion.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/coordinates
copying src/einsteinpy/coordinates/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/coordinates
copying src/einsteinpy/coordinates/core.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/coordinates
copying src/einsteinpy/coordinates/utils.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/coordinates
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/integrators
copying src/einsteinpy/integrators/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/integrators
copying src/einsteinpy/integrators/utils.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/integrators
copying src/einsteinpy/integrators/runge_kutta.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/integrators
copying src/einsteinpy/integrators/fantasy.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/integrators
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/godel.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/janis_newman_winicour.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/bessel_gravitational_wave.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/find.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/ernst.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/davidson.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/barriola_vilenkin.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/bertotti_kasner.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/alcubierre_warp.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/vacuum_solutions.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/cmetric.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/minkowski.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/de_sitter.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/symbolic/predefined
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/plotting/rays
copying src/einsteinpy/plotting/rays/shadow.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/plotting/rays
copying src/einsteinpy/plotting/rays/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/plotting/rays
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/plotting/geodesic
copying src/einsteinpy/plotting/geodesic/static.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/plotting/geodesic
copying src/einsteinpy/plotting/geodesic/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/plotting/geodesic
copying src/einsteinpy/plotting/geodesic/core.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/plotting/geodesic
copying src/einsteinpy/plotting/geodesic/interactive.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/plotting/geodesic
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/plotting/hypersurface
copying src/einsteinpy/plotting/hypersurface/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/plotting/hypersurface
copying src/einsteinpy/plotting/hypersurface/core.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/plotting/hypersurface
running egg_info
creating src/einsteinpy.egg-info
writing src/einsteinpy.egg-info/PKG-INFO
writing dependency_links to src/einsteinpy.egg-info/dependency_links.txt
writing requirements to src/einsteinpy.egg-info/requires.txt
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writing manifest file 'src/einsteinpy.egg-info/SOURCES.txt'
reading manifest file 'src/einsteinpy.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching '*.py' under directory 'src/einsteinpy/tests'
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adding license file 'COPYING'
adding license file 'AUTHORS'
writing manifest file 'src/einsteinpy.egg-info/SOURCES.txt'
I: pybuild base:311: /usr/bin/python3 setup.py build 
/usr/lib/python3/dist-packages/setuptools/_distutils/dist.py:261: UserWarning: Unknown distribution option: 'setup_cfg'
  warnings.warn(msg)
running build
running build_py
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy
copying src/einsteinpy/examples.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy
copying src/einsteinpy/ijit.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy
copying src/einsteinpy/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy
copying src/einsteinpy/bodies.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy
copying src/einsteinpy/constant.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy
copying src/einsteinpy/units.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/utils
copying src/einsteinpy/utils/dual.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/utils
copying src/einsteinpy/utils/scalar_factor.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/utils
copying src/einsteinpy/utils/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/utils
copying src/einsteinpy/utils/exceptions.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/utils
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/metric
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copying src/einsteinpy/metric/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/metric
copying src/einsteinpy/metric/base_metric.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/metric
copying src/einsteinpy/metric/kerrnewman.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/metric
copying src/einsteinpy/metric/schwarzschild.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/metric
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic
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copying src/einsteinpy/symbolic/christoffel.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic
copying src/einsteinpy/symbolic/riemann.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic
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copying src/einsteinpy/symbolic/constants.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic
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copying src/einsteinpy/symbolic/einstein.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic
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copying src/einsteinpy/geodesic/utils.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/geodesic
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creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/hypersurface
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copying src/einsteinpy/coordinates/conversion.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/coordinates
copying src/einsteinpy/coordinates/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/coordinates
copying src/einsteinpy/coordinates/core.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/coordinates
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copying src/einsteinpy/integrators/runge_kutta.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/integrators
copying src/einsteinpy/integrators/fantasy.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/integrators
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/godel.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/janis_newman_winicour.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/bessel_gravitational_wave.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/find.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/ernst.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/davidson.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/barriola_vilenkin.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/bertotti_kasner.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/alcubierre_warp.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/vacuum_solutions.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/cmetric.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/minkowski.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic/predefined
copying src/einsteinpy/symbolic/predefined/de_sitter.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/symbolic/predefined
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/plotting/rays
copying src/einsteinpy/plotting/rays/shadow.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/plotting/rays
copying src/einsteinpy/plotting/rays/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/plotting/rays
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/plotting/geodesic
copying src/einsteinpy/plotting/geodesic/static.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/plotting/geodesic
copying src/einsteinpy/plotting/geodesic/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/plotting/geodesic
copying src/einsteinpy/plotting/geodesic/core.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/plotting/geodesic
copying src/einsteinpy/plotting/geodesic/interactive.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/plotting/geodesic
creating /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/plotting/hypersurface
copying src/einsteinpy/plotting/hypersurface/__init__.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/plotting/hypersurface
copying src/einsteinpy/plotting/hypersurface/core.py -> /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/plotting/hypersurface
running egg_info
writing src/einsteinpy.egg-info/PKG-INFO
writing dependency_links to src/einsteinpy.egg-info/dependency_links.txt
writing requirements to src/einsteinpy.egg-info/requires.txt
writing top-level names to src/einsteinpy.egg-info/top_level.txt
reading manifest file 'src/einsteinpy.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no files found matching '*.py' under directory 'src/einsteinpy/tests'
warning: no files found matching '*.html' under directory 'src/einsteinpy/tests'
no previously-included directories found matching 'docs/source/examples/.ipynb_checkpoints'
warning: no previously-included files matching '*.py[cod]' found anywhere in distribution
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adding license file 'COPYING'
adding license file 'AUTHORS'
writing manifest file 'src/einsteinpy.egg-info/SOURCES.txt'
   dh_auto_test -O--buildsystem=pybuild
I: pybuild base:311: cd /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build; python3.12 -m pytest tests
============================= test session starts ==============================
platform linux -- Python 3.12.9, pytest-8.3.4, pluggy-1.5.0
rootdir: /build/reproducible-path/einsteinpy-0.4.0
configfile: setup.cfg
plugins: arraydiff-0.6.1, hypothesis-6.125.2, cov-5.0.0, mock-3.14.0, filter-subpackage-0.2.0, astropy-0.11.0, remotedata-0.4.1, doctestplus-1.4.0, typeguard-4.4.1, astropy-header-0.2.2
collected 296 items

tests/test_bodies.py .........................                           [  8%]
tests/test_constant.py ...                                               [  9%]
tests/test_coordinates/test_conversions.py ..........                    [ 12%]
tests/test_coordinates/test_coord_transform.py ................          [ 18%]
tests/test_coordinates/test_diff_transform.py .................          [ 23%]
tests/test_coordinates/test_utils.py ....                                [ 25%]
tests/test_examples.py .                                                 [ 25%]
tests/test_geodesic/test_geodesic.py .........                           [ 28%]
tests/test_geodesic/test_geodesic_utils.py ......                        [ 30%]
tests/test_hypersurface/test_schwarzschildembedding.py ....              [ 32%]
tests/test_ijit/test_ijit_without_numba.py ..                            [ 32%]
tests/test_integrators/test_geod_int.py ............                     [ 36%]
tests/test_integrators/test_runge_kutta.py .                             [ 37%]
tests/test_metric/test_base_metric.py ...............                    [ 42%]
tests/test_metric/test_kerr.py ...                                       [ 43%]
tests/test_metric/test_kerrnewman.py ........                            [ 45%]
tests/test_metric/test_schwarzschild.py .                                [ 46%]
tests/test_plotting/test_fractal.py ..                                   [ 46%]
tests/test_plotting/test_geodesic/test_interactive.py ........           [ 49%]
tests/test_plotting/test_geodesic/test_static.py ............            [ 53%]
tests/test_plotting/test_hypersurface.py ..                              [ 54%]
tests/test_plotting/test_rays/test_shadow.py EE                          [ 55%]
tests/test_symbolic/test_christoffel.py ......                           [ 57%]
tests/test_symbolic/test_constants.py ..                                 [ 57%]
tests/test_symbolic/test_einstein.py .....                               [ 59%]
tests/test_symbolic/test_helpers.py .x.......                            [ 62%]
tests/test_symbolic/test_metric.py .......                               [ 64%]
tests/test_symbolic/test_predefined/test_all.py ........................ [ 72%]
.                                                                        [ 73%]
tests/test_symbolic/test_predefined/test_find.py ..                      [ 73%]
tests/test_symbolic/test_ricci.py .......                                [ 76%]
tests/test_symbolic/test_riemann.py ....                                 [ 77%]
tests/test_symbolic/test_schouten.py ....                                [ 79%]
tests/test_symbolic/test_stress_energy_momentum.py ...                   [ 80%]
tests/test_symbolic/test_tensor.py .........xxxxxx...x.....              [ 88%]
tests/test_symbolic/test_vector.py .x..                                  [ 89%]
tests/test_symbolic/test_weyl.py ......                                  [ 91%]
tests/test_units.py .                                                    [ 91%]
tests/test_utils/test_dual.py ...................                        [ 98%]
tests/test_utils/test_exceptions.py .                                    [ 98%]
tests/test_utils/test_scalar_factor.py ....                              [100%]

==================================== ERRORS ====================================
____________ ERROR at setup of test_plotter_has_correct_attributes _____________

    @pytest.fixture()
    def dummy_data():
        mass = 1 * u.kg
        fov = 30 * u.km
>       shadow = Shadow(mass=mass, fov=fov, n_rays=1000)

tests/test_plotting/test_rays/test_shadow.py:15: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3/dist-packages/astropy/units/decorators.py:316: in wrapper
    return_ = wrapped_function(*func_args, **func_kwargs)
einsteinpy/rays/shadow.py:29: in __init__
    root = newton(self._root_equation, 0.1, args=(i,))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

func = <bound method Shadow._root_equation of <einsteinpy.rays.shadow.Shadow object at 0xae9dddb0>>
x0 = np.float64(0.1), fprime = None, args = (np.float64(5.196152422706632),)
tol = 1.48e-08, maxiter = 50, fprime2 = None, x1 = None, rtol = 0.0
full_output = False, disp = True

    def newton(func, x0, fprime=None, args=(), tol=1.48e-8, maxiter=50,
               fprime2=None, x1=None, rtol=0.0,
               full_output=False, disp=True):
        """
        Find a root of a real or complex function using the Newton-Raphson
        (or secant or Halley's) method.
    
        Find a root of the scalar-valued function `func` given a nearby scalar
        starting point `x0`.
        The Newton-Raphson method is used if the derivative `fprime` of `func`
        is provided, otherwise the secant method is used. If the second order
        derivative `fprime2` of `func` is also provided, then Halley's method is
        used.
    
        If `x0` is a sequence with more than one item, `newton` returns an array:
        the roots of the function from each (scalar) starting point in `x0`.
        In this case, `func` must be vectorized to return a sequence or array of
        the same shape as its first argument. If `fprime` (`fprime2`) is given,
        then its return must also have the same shape: each element is the first
        (second) derivative of `func` with respect to its only variable evaluated
        at each element of its first argument.
    
        `newton` is for finding roots of a scalar-valued functions of a single
        variable. For problems involving several variables, see `root`.
    
        Parameters
        ----------
        func : callable
            The function whose root is wanted. It must be a function of a
            single variable of the form ``f(x,a,b,c...)``, where ``a,b,c...``
            are extra arguments that can be passed in the `args` parameter.
        x0 : float, sequence, or ndarray
            An initial estimate of the root that should be somewhere near the
            actual root. If not scalar, then `func` must be vectorized and return
            a sequence or array of the same shape as its first argument.
        fprime : callable, optional
            The derivative of the function when available and convenient. If it
            is None (default), then the secant method is used.
        args : tuple, optional
            Extra arguments to be used in the function call.
        tol : float, optional
            The allowable error of the root's value. If `func` is complex-valued,
            a larger `tol` is recommended as both the real and imaginary parts
            of `x` contribute to ``|x - x0|``.
        maxiter : int, optional
            Maximum number of iterations.
        fprime2 : callable, optional
            The second order derivative of the function when available and
            convenient. If it is None (default), then the normal Newton-Raphson
            or the secant method is used. If it is not None, then Halley's method
            is used.
        x1 : float, optional
            Another estimate of the root that should be somewhere near the
            actual root. Used if `fprime` is not provided.
        rtol : float, optional
            Tolerance (relative) for termination.
        full_output : bool, optional
            If `full_output` is False (default), the root is returned.
            If True and `x0` is scalar, the return value is ``(x, r)``, where ``x``
            is the root and ``r`` is a `RootResults` object.
            If True and `x0` is non-scalar, the return value is ``(x, converged,
            zero_der)`` (see Returns section for details).
        disp : bool, optional
            If True, raise a RuntimeError if the algorithm didn't converge, with
            the error message containing the number of iterations and current
            function value. Otherwise, the convergence status is recorded in a
            `RootResults` return object.
            Ignored if `x0` is not scalar.
            *Note: this has little to do with displaying, however,
            the `disp` keyword cannot be renamed for backwards compatibility.*
    
        Returns
        -------
        root : float, sequence, or ndarray
            Estimated location where function is zero.
        r : `RootResults`, optional
            Present if ``full_output=True`` and `x0` is scalar.
            Object containing information about the convergence. In particular,
            ``r.converged`` is True if the routine converged.
        converged : ndarray of bool, optional
            Present if ``full_output=True`` and `x0` is non-scalar.
            For vector functions, indicates which elements converged successfully.
        zero_der : ndarray of bool, optional
            Present if ``full_output=True`` and `x0` is non-scalar.
            For vector functions, indicates which elements had a zero derivative.
    
        See Also
        --------
        root_scalar : interface to root solvers for scalar functions
        root : interface to root solvers for multi-input, multi-output functions
    
        Notes
        -----
        The convergence rate of the Newton-Raphson method is quadratic,
        the Halley method is cubic, and the secant method is
        sub-quadratic. This means that if the function is well-behaved
        the actual error in the estimated root after the nth iteration
        is approximately the square (cube for Halley) of the error
        after the (n-1)th step. However, the stopping criterion used
        here is the step size and there is no guarantee that a root
        has been found. Consequently, the result should be verified.
        Safer algorithms are brentq, brenth, ridder, and bisect,
        but they all require that the root first be bracketed in an
        interval where the function changes sign. The brentq algorithm
        is recommended for general use in one dimensional problems
        when such an interval has been found.
    
        When `newton` is used with arrays, it is best suited for the following
        types of problems:
    
        * The initial guesses, `x0`, are all relatively the same distance from
          the roots.
        * Some or all of the extra arguments, `args`, are also arrays so that a
          class of similar problems can be solved together.
        * The size of the initial guesses, `x0`, is larger than O(100) elements.
          Otherwise, a naive loop may perform as well or better than a vector.
    
        Examples
        --------
        >>> import numpy as np
        >>> import matplotlib.pyplot as plt
        >>> from scipy import optimize
    
        >>> def f(x):
        ...     return (x**3 - 1)  # only one real root at x = 1
    
        ``fprime`` is not provided, use the secant method:
    
        >>> root = optimize.newton(f, 1.5)
        >>> root
        1.0000000000000016
        >>> root = optimize.newton(f, 1.5, fprime2=lambda x: 6 * x)
        >>> root
        1.0000000000000016
    
        Only ``fprime`` is provided, use the Newton-Raphson method:
    
        >>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2)
        >>> root
        1.0
    
        Both ``fprime2`` and ``fprime`` are provided, use Halley's method:
    
        >>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2,
        ...                        fprime2=lambda x: 6 * x)
        >>> root
        1.0
    
        When we want to find roots for a set of related starting values and/or
        function parameters, we can provide both of those as an array of inputs:
    
        >>> f = lambda x, a: x**3 - a
        >>> fder = lambda x, a: 3 * x**2
        >>> rng = np.random.default_rng()
        >>> x = rng.standard_normal(100)
        >>> a = np.arange(-50, 50)
        >>> vec_res = optimize.newton(f, x, fprime=fder, args=(a, ), maxiter=200)
    
        The above is the equivalent of solving for each value in ``(x, a)``
        separately in a for-loop, just faster:
    
        >>> loop_res = [optimize.newton(f, x0, fprime=fder, args=(a0,),
        ...                             maxiter=200)
        ...             for x0, a0 in zip(x, a)]
        >>> np.allclose(vec_res, loop_res)
        True
    
        Plot the results found for all values of ``a``:
    
        >>> analytical_result = np.sign(a) * np.abs(a)**(1/3)
        >>> fig, ax = plt.subplots()
        >>> ax.plot(a, analytical_result, 'o')
        >>> ax.plot(a, vec_res, '.')
        >>> ax.set_xlabel('$a$')
        >>> ax.set_ylabel('$x$ where $f(x, a)=0$')
        >>> plt.show()
    
        """
        if tol <= 0:
            raise ValueError("tol too small (%g <= 0)" % tol)
        maxiter = operator.index(maxiter)
        if maxiter < 1:
            raise ValueError("maxiter must be greater than 0")
        if np.size(x0) > 1:
            return _array_newton(func, x0, fprime, args, tol, maxiter, fprime2,
                                 full_output)
    
        # Convert to float (don't use float(x0); this works also for complex x0)
        # Use np.asarray because we want x0 to be a numpy object, not a Python
        # object. e.g. np.complex(1+1j) > 0 is possible, but (1 + 1j) > 0 raises
        # a TypeError
        x0 = np.asarray(x0)[()] * 1.0
        p0 = x0
        funcalls = 0
        if fprime is not None:
            # Newton-Raphson method
            method = "newton"
            for itr in range(maxiter):
                # first evaluate fval
                fval = func(p0, *args)
                funcalls += 1
                # If fval is 0, a root has been found, then terminate
                if fval == 0:
                    return _results_select(
                        full_output, (p0, funcalls, itr, _ECONVERGED), method)
                fder = fprime(p0, *args)
                funcalls += 1
                if fder == 0:
                    msg = "Derivative was zero."
                    if disp:
                        msg += (
                            " Failed to converge after %d iterations, value is %s."
                            % (itr + 1, p0))
                        raise RuntimeError(msg)
                    warnings.warn(msg, RuntimeWarning, stacklevel=2)
                    return _results_select(
                        full_output, (p0, funcalls, itr + 1, _ECONVERR), method)
                newton_step = fval / fder
                if fprime2:
                    fder2 = fprime2(p0, *args)
                    funcalls += 1
                    method = "halley"
                    # Halley's method:
                    #   newton_step /= (1.0 - 0.5 * newton_step * fder2 / fder)
                    # Only do it if denominator stays close enough to 1
                    # Rationale: If 1-adj < 0, then Halley sends x in the
                    # opposite direction to Newton. Doesn't happen if x is close
                    # enough to root.
                    adj = newton_step * fder2 / fder / 2
                    if np.abs(adj) < 1:
                        newton_step /= 1.0 - adj
                p = p0 - newton_step
                if np.isclose(p, p0, rtol=rtol, atol=tol):
                    return _results_select(
                        full_output, (p, funcalls, itr + 1, _ECONVERGED), method)
                p0 = p
        else:
            # Secant method
            method = "secant"
            if x1 is not None:
                if x1 == x0:
                    raise ValueError("x1 and x0 must be different")
                p1 = x1
            else:
                eps = 1e-4
                p1 = x0 * (1 + eps)
                p1 += (eps if p1 >= 0 else -eps)
            q0 = func(p0, *args)
            funcalls += 1
            q1 = func(p1, *args)
            funcalls += 1
            if abs(q1) < abs(q0):
                p0, p1, q0, q1 = p1, p0, q1, q0
            for itr in range(maxiter):
                if q1 == q0:
                    if p1 != p0:
                        msg = "Tolerance of %s reached." % (p1 - p0)
                        if disp:
                            msg += (
                                " Failed to converge after %d iterations, value is %s."
                                % (itr + 1, p1))
                            raise RuntimeError(msg)
                        warnings.warn(msg, RuntimeWarning, stacklevel=2)
                    p = (p1 + p0) / 2.0
                    return _results_select(
                        full_output, (p, funcalls, itr + 1, _ECONVERR), method)
                else:
                    if abs(q1) > abs(q0):
                        p = (-q0 / q1 * p1 + p0) / (1 - q0 / q1)
                    else:
                        p = (-q1 / q0 * p0 + p1) / (1 - q1 / q0)
                if np.isclose(p, p1, rtol=rtol, atol=tol):
                    return _results_select(
                        full_output, (p, funcalls, itr + 1, _ECONVERGED), method)
                p0, q0 = p1, q1
                p1 = p
                q1 = func(p1, *args)
                funcalls += 1
    
        if disp:
            msg = ("Failed to converge after %d iterations, value is %s."
                   % (itr + 1, p))
>           raise RuntimeError(msg)
E           RuntimeError: Failed to converge after 50 iterations, value is nan.

/usr/lib/python3/dist-packages/scipy/optimize/_zeros_py.py:391: RuntimeError
__________________ ERROR at setup of test_plot_calls_plt_plot __________________

    @pytest.fixture()
    def dummy_data():
        mass = 1 * u.kg
        fov = 30 * u.km
>       shadow = Shadow(mass=mass, fov=fov, n_rays=1000)

tests/test_plotting/test_rays/test_shadow.py:15: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3/dist-packages/astropy/units/decorators.py:316: in wrapper
    return_ = wrapped_function(*func_args, **func_kwargs)
einsteinpy/rays/shadow.py:29: in __init__
    root = newton(self._root_equation, 0.1, args=(i,))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

func = <bound method Shadow._root_equation of <einsteinpy.rays.shadow.Shadow object at 0xaf06c2b8>>
x0 = np.float64(0.1), fprime = None, args = (np.float64(5.196152422706632),)
tol = 1.48e-08, maxiter = 50, fprime2 = None, x1 = None, rtol = 0.0
full_output = False, disp = True

    def newton(func, x0, fprime=None, args=(), tol=1.48e-8, maxiter=50,
               fprime2=None, x1=None, rtol=0.0,
               full_output=False, disp=True):
        """
        Find a root of a real or complex function using the Newton-Raphson
        (or secant or Halley's) method.
    
        Find a root of the scalar-valued function `func` given a nearby scalar
        starting point `x0`.
        The Newton-Raphson method is used if the derivative `fprime` of `func`
        is provided, otherwise the secant method is used. If the second order
        derivative `fprime2` of `func` is also provided, then Halley's method is
        used.
    
        If `x0` is a sequence with more than one item, `newton` returns an array:
        the roots of the function from each (scalar) starting point in `x0`.
        In this case, `func` must be vectorized to return a sequence or array of
        the same shape as its first argument. If `fprime` (`fprime2`) is given,
        then its return must also have the same shape: each element is the first
        (second) derivative of `func` with respect to its only variable evaluated
        at each element of its first argument.
    
        `newton` is for finding roots of a scalar-valued functions of a single
        variable. For problems involving several variables, see `root`.
    
        Parameters
        ----------
        func : callable
            The function whose root is wanted. It must be a function of a
            single variable of the form ``f(x,a,b,c...)``, where ``a,b,c...``
            are extra arguments that can be passed in the `args` parameter.
        x0 : float, sequence, or ndarray
            An initial estimate of the root that should be somewhere near the
            actual root. If not scalar, then `func` must be vectorized and return
            a sequence or array of the same shape as its first argument.
        fprime : callable, optional
            The derivative of the function when available and convenient. If it
            is None (default), then the secant method is used.
        args : tuple, optional
            Extra arguments to be used in the function call.
        tol : float, optional
            The allowable error of the root's value. If `func` is complex-valued,
            a larger `tol` is recommended as both the real and imaginary parts
            of `x` contribute to ``|x - x0|``.
        maxiter : int, optional
            Maximum number of iterations.
        fprime2 : callable, optional
            The second order derivative of the function when available and
            convenient. If it is None (default), then the normal Newton-Raphson
            or the secant method is used. If it is not None, then Halley's method
            is used.
        x1 : float, optional
            Another estimate of the root that should be somewhere near the
            actual root. Used if `fprime` is not provided.
        rtol : float, optional
            Tolerance (relative) for termination.
        full_output : bool, optional
            If `full_output` is False (default), the root is returned.
            If True and `x0` is scalar, the return value is ``(x, r)``, where ``x``
            is the root and ``r`` is a `RootResults` object.
            If True and `x0` is non-scalar, the return value is ``(x, converged,
            zero_der)`` (see Returns section for details).
        disp : bool, optional
            If True, raise a RuntimeError if the algorithm didn't converge, with
            the error message containing the number of iterations and current
            function value. Otherwise, the convergence status is recorded in a
            `RootResults` return object.
            Ignored if `x0` is not scalar.
            *Note: this has little to do with displaying, however,
            the `disp` keyword cannot be renamed for backwards compatibility.*
    
        Returns
        -------
        root : float, sequence, or ndarray
            Estimated location where function is zero.
        r : `RootResults`, optional
            Present if ``full_output=True`` and `x0` is scalar.
            Object containing information about the convergence. In particular,
            ``r.converged`` is True if the routine converged.
        converged : ndarray of bool, optional
            Present if ``full_output=True`` and `x0` is non-scalar.
            For vector functions, indicates which elements converged successfully.
        zero_der : ndarray of bool, optional
            Present if ``full_output=True`` and `x0` is non-scalar.
            For vector functions, indicates which elements had a zero derivative.
    
        See Also
        --------
        root_scalar : interface to root solvers for scalar functions
        root : interface to root solvers for multi-input, multi-output functions
    
        Notes
        -----
        The convergence rate of the Newton-Raphson method is quadratic,
        the Halley method is cubic, and the secant method is
        sub-quadratic. This means that if the function is well-behaved
        the actual error in the estimated root after the nth iteration
        is approximately the square (cube for Halley) of the error
        after the (n-1)th step. However, the stopping criterion used
        here is the step size and there is no guarantee that a root
        has been found. Consequently, the result should be verified.
        Safer algorithms are brentq, brenth, ridder, and bisect,
        but they all require that the root first be bracketed in an
        interval where the function changes sign. The brentq algorithm
        is recommended for general use in one dimensional problems
        when such an interval has been found.
    
        When `newton` is used with arrays, it is best suited for the following
        types of problems:
    
        * The initial guesses, `x0`, are all relatively the same distance from
          the roots.
        * Some or all of the extra arguments, `args`, are also arrays so that a
          class of similar problems can be solved together.
        * The size of the initial guesses, `x0`, is larger than O(100) elements.
          Otherwise, a naive loop may perform as well or better than a vector.
    
        Examples
        --------
        >>> import numpy as np
        >>> import matplotlib.pyplot as plt
        >>> from scipy import optimize
    
        >>> def f(x):
        ...     return (x**3 - 1)  # only one real root at x = 1
    
        ``fprime`` is not provided, use the secant method:
    
        >>> root = optimize.newton(f, 1.5)
        >>> root
        1.0000000000000016
        >>> root = optimize.newton(f, 1.5, fprime2=lambda x: 6 * x)
        >>> root
        1.0000000000000016
    
        Only ``fprime`` is provided, use the Newton-Raphson method:
    
        >>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2)
        >>> root
        1.0
    
        Both ``fprime2`` and ``fprime`` are provided, use Halley's method:
    
        >>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2,
        ...                        fprime2=lambda x: 6 * x)
        >>> root
        1.0
    
        When we want to find roots for a set of related starting values and/or
        function parameters, we can provide both of those as an array of inputs:
    
        >>> f = lambda x, a: x**3 - a
        >>> fder = lambda x, a: 3 * x**2
        >>> rng = np.random.default_rng()
        >>> x = rng.standard_normal(100)
        >>> a = np.arange(-50, 50)
        >>> vec_res = optimize.newton(f, x, fprime=fder, args=(a, ), maxiter=200)
    
        The above is the equivalent of solving for each value in ``(x, a)``
        separately in a for-loop, just faster:
    
        >>> loop_res = [optimize.newton(f, x0, fprime=fder, args=(a0,),
        ...                             maxiter=200)
        ...             for x0, a0 in zip(x, a)]
        >>> np.allclose(vec_res, loop_res)
        True
    
        Plot the results found for all values of ``a``:
    
        >>> analytical_result = np.sign(a) * np.abs(a)**(1/3)
        >>> fig, ax = plt.subplots()
        >>> ax.plot(a, analytical_result, 'o')
        >>> ax.plot(a, vec_res, '.')
        >>> ax.set_xlabel('$a$')
        >>> ax.set_ylabel('$x$ where $f(x, a)=0$')
        >>> plt.show()
    
        """
        if tol <= 0:
            raise ValueError("tol too small (%g <= 0)" % tol)
        maxiter = operator.index(maxiter)
        if maxiter < 1:
            raise ValueError("maxiter must be greater than 0")
        if np.size(x0) > 1:
            return _array_newton(func, x0, fprime, args, tol, maxiter, fprime2,
                                 full_output)
    
        # Convert to float (don't use float(x0); this works also for complex x0)
        # Use np.asarray because we want x0 to be a numpy object, not a Python
        # object. e.g. np.complex(1+1j) > 0 is possible, but (1 + 1j) > 0 raises
        # a TypeError
        x0 = np.asarray(x0)[()] * 1.0
        p0 = x0
        funcalls = 0
        if fprime is not None:
            # Newton-Raphson method
            method = "newton"
            for itr in range(maxiter):
                # first evaluate fval
                fval = func(p0, *args)
                funcalls += 1
                # If fval is 0, a root has been found, then terminate
                if fval == 0:
                    return _results_select(
                        full_output, (p0, funcalls, itr, _ECONVERGED), method)
                fder = fprime(p0, *args)
                funcalls += 1
                if fder == 0:
                    msg = "Derivative was zero."
                    if disp:
                        msg += (
                            " Failed to converge after %d iterations, value is %s."
                            % (itr + 1, p0))
                        raise RuntimeError(msg)
                    warnings.warn(msg, RuntimeWarning, stacklevel=2)
                    return _results_select(
                        full_output, (p0, funcalls, itr + 1, _ECONVERR), method)
                newton_step = fval / fder
                if fprime2:
                    fder2 = fprime2(p0, *args)
                    funcalls += 1
                    method = "halley"
                    # Halley's method:
                    #   newton_step /= (1.0 - 0.5 * newton_step * fder2 / fder)
                    # Only do it if denominator stays close enough to 1
                    # Rationale: If 1-adj < 0, then Halley sends x in the
                    # opposite direction to Newton. Doesn't happen if x is close
                    # enough to root.
                    adj = newton_step * fder2 / fder / 2
                    if np.abs(adj) < 1:
                        newton_step /= 1.0 - adj
                p = p0 - newton_step
                if np.isclose(p, p0, rtol=rtol, atol=tol):
                    return _results_select(
                        full_output, (p, funcalls, itr + 1, _ECONVERGED), method)
                p0 = p
        else:
            # Secant method
            method = "secant"
            if x1 is not None:
                if x1 == x0:
                    raise ValueError("x1 and x0 must be different")
                p1 = x1
            else:
                eps = 1e-4
                p1 = x0 * (1 + eps)
                p1 += (eps if p1 >= 0 else -eps)
            q0 = func(p0, *args)
            funcalls += 1
            q1 = func(p1, *args)
            funcalls += 1
            if abs(q1) < abs(q0):
                p0, p1, q0, q1 = p1, p0, q1, q0
            for itr in range(maxiter):
                if q1 == q0:
                    if p1 != p0:
                        msg = "Tolerance of %s reached." % (p1 - p0)
                        if disp:
                            msg += (
                                " Failed to converge after %d iterations, value is %s."
                                % (itr + 1, p1))
                            raise RuntimeError(msg)
                        warnings.warn(msg, RuntimeWarning, stacklevel=2)
                    p = (p1 + p0) / 2.0
                    return _results_select(
                        full_output, (p, funcalls, itr + 1, _ECONVERR), method)
                else:
                    if abs(q1) > abs(q0):
                        p = (-q0 / q1 * p1 + p0) / (1 - q0 / q1)
                    else:
                        p = (-q1 / q0 * p0 + p1) / (1 - q1 / q0)
                if np.isclose(p, p1, rtol=rtol, atol=tol):
                    return _results_select(
                        full_output, (p, funcalls, itr + 1, _ECONVERGED), method)
                p0, q0 = p1, q1
                p1 = p
                q1 = func(p1, *args)
                funcalls += 1
    
        if disp:
            msg = ("Failed to converge after %d iterations, value is %s."
                   % (itr + 1, p))
>           raise RuntimeError(msg)
E           RuntimeError: Failed to converge after 50 iterations, value is nan.

/usr/lib/python3/dist-packages/scipy/optimize/_zeros_py.py:391: RuntimeError
=============================== warnings summary ===============================
einsteinpy/ijit.py:30
.pybuild/cpython3_3.12_einsteinpy/build/tests/test_ijit/test_ijit_without_numba.py::test_decorator
  /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build/einsteinpy/ijit.py:30: UserWarning:
  
  Could not import numba package. All einsteinpy functions will work properly but the CPU intensive algorithms will be slow. Consider installing numba to boost performance.

.pybuild/cpython3_3.12_einsteinpy/build/tests/test_symbolic/test_christoffel.py::test_properties
  /usr/lib/python3/dist-packages/_pytest/python.py:163: PytestReturnNotNoneWarning:
  
  Expected None, but .pybuild/cpython3_3.12_einsteinpy/build/tests/test_symbolic/test_christoffel.py::test_properties returned True, which will be an error in a future version of pytest.  Did you mean to use `assert` instead of `return`?

.pybuild/cpython3_3.12_einsteinpy/build/tests/test_symbolic/test_schouten.py::test_SchoutenTensor_with_arbritary_config
  /usr/lib/python3/dist-packages/_pytest/python.py:163: PytestReturnNotNoneWarning:
  
  Expected None, but .pybuild/cpython3_3.12_einsteinpy/build/tests/test_symbolic/test_schouten.py::test_SchoutenTensor_with_arbritary_config returned True, which will be an error in a future version of pytest.  Did you mean to use `assert` instead of `return`?

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
ERROR tests/test_plotting/test_rays/test_shadow.py::test_plotter_has_correct_attributes
ERROR tests/test_plotting/test_rays/test_shadow.py::test_plot_calls_plt_plot
====== 285 passed, 9 xfailed, 4 warnings, 2 errors in 1807.68s (0:30:07) =======
E: pybuild pybuild:389: test: plugin distutils failed with: exit code=1: cd /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.12_einsteinpy/build; python3.12 -m pytest tests
I: pybuild base:311: cd /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build; python3.13 -m pytest tests
============================= test session starts ==============================
platform linux -- Python 3.13.2, pytest-8.3.4, pluggy-1.5.0
rootdir: /build/reproducible-path/einsteinpy-0.4.0
configfile: setup.cfg
plugins: arraydiff-0.6.1, hypothesis-6.125.2, cov-5.0.0, mock-3.14.0, filter-subpackage-0.2.0, astropy-0.11.0, remotedata-0.4.1, doctestplus-1.4.0, typeguard-4.4.1, astropy-header-0.2.2
collected 296 items

tests/test_bodies.py .........................                           [  8%]
tests/test_constant.py ...                                               [  9%]
tests/test_coordinates/test_conversions.py ..........                    [ 12%]
tests/test_coordinates/test_coord_transform.py ................          [ 18%]
tests/test_coordinates/test_diff_transform.py .................          [ 23%]
tests/test_coordinates/test_utils.py ....                                [ 25%]
tests/test_examples.py .                                                 [ 25%]
tests/test_geodesic/test_geodesic.py .........                           [ 28%]
tests/test_geodesic/test_geodesic_utils.py ......                        [ 30%]
tests/test_hypersurface/test_schwarzschildembedding.py ....              [ 32%]
tests/test_ijit/test_ijit_without_numba.py ..                            [ 32%]
tests/test_integrators/test_geod_int.py ............                     [ 36%]
tests/test_integrators/test_runge_kutta.py .                             [ 37%]
tests/test_metric/test_base_metric.py ...............                    [ 42%]
tests/test_metric/test_kerr.py ...                                       [ 43%]
tests/test_metric/test_kerrnewman.py ........                            [ 45%]
tests/test_metric/test_schwarzschild.py .                                [ 46%]
tests/test_plotting/test_fractal.py ..                                   [ 46%]
tests/test_plotting/test_geodesic/test_interactive.py ........           [ 49%]
tests/test_plotting/test_geodesic/test_static.py ............            [ 53%]
tests/test_plotting/test_hypersurface.py ..                              [ 54%]
tests/test_plotting/test_rays/test_shadow.py EE                          [ 55%]
tests/test_symbolic/test_christoffel.py ......                           [ 57%]
tests/test_symbolic/test_constants.py ..                                 [ 57%]
tests/test_symbolic/test_einstein.py .....                               [ 59%]
tests/test_symbolic/test_helpers.py .x.......                            [ 62%]
tests/test_symbolic/test_metric.py .......                               [ 64%]
tests/test_symbolic/test_predefined/test_all.py ........................ [ 72%]
.                                                                        [ 73%]
tests/test_symbolic/test_predefined/test_find.py ..                      [ 73%]
tests/test_symbolic/test_ricci.py .......                                [ 76%]
tests/test_symbolic/test_riemann.py ....                                 [ 77%]
tests/test_symbolic/test_schouten.py ....                                [ 79%]
tests/test_symbolic/test_stress_energy_momentum.py ...                   [ 80%]
tests/test_symbolic/test_tensor.py .........xxxxxx...x.....              [ 88%]
tests/test_symbolic/test_vector.py .x..                                  [ 89%]
tests/test_symbolic/test_weyl.py ......                                  [ 91%]
tests/test_units.py .                                                    [ 91%]
tests/test_utils/test_dual.py ...................                        [ 98%]
tests/test_utils/test_exceptions.py .                                    [ 98%]
tests/test_utils/test_scalar_factor.py ....                              [100%]

==================================== ERRORS ====================================
____________ ERROR at setup of test_plotter_has_correct_attributes _____________

    @pytest.fixture()
    def dummy_data():
        mass = 1 * u.kg
        fov = 30 * u.km
>       shadow = Shadow(mass=mass, fov=fov, n_rays=1000)

tests/test_plotting/test_rays/test_shadow.py:15: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3/dist-packages/astropy/units/decorators.py:316: in wrapper
    return_ = wrapped_function(*func_args, **func_kwargs)
einsteinpy/rays/shadow.py:29: in __init__
    root = newton(self._root_equation, 0.1, args=(i,))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

func = <bound method Shadow._root_equation of <einsteinpy.rays.shadow.Shadow object at 0xaf45e760>>
x0 = np.float64(0.1), fprime = None, args = (np.float64(5.196152422706632),)
tol = 1.48e-08, maxiter = 50, fprime2 = None, x1 = None, rtol = 0.0
full_output = False, disp = True

    def newton(func, x0, fprime=None, args=(), tol=1.48e-8, maxiter=50,
               fprime2=None, x1=None, rtol=0.0,
               full_output=False, disp=True):
        """
        Find a root of a real or complex function using the Newton-Raphson
        (or secant or Halley's) method.
    
        Find a root of the scalar-valued function `func` given a nearby scalar
        starting point `x0`.
        The Newton-Raphson method is used if the derivative `fprime` of `func`
        is provided, otherwise the secant method is used. If the second order
        derivative `fprime2` of `func` is also provided, then Halley's method is
        used.
    
        If `x0` is a sequence with more than one item, `newton` returns an array:
        the roots of the function from each (scalar) starting point in `x0`.
        In this case, `func` must be vectorized to return a sequence or array of
        the same shape as its first argument. If `fprime` (`fprime2`) is given,
        then its return must also have the same shape: each element is the first
        (second) derivative of `func` with respect to its only variable evaluated
        at each element of its first argument.
    
        `newton` is for finding roots of a scalar-valued functions of a single
        variable. For problems involving several variables, see `root`.
    
        Parameters
        ----------
        func : callable
            The function whose root is wanted. It must be a function of a
            single variable of the form ``f(x,a,b,c...)``, where ``a,b,c...``
            are extra arguments that can be passed in the `args` parameter.
        x0 : float, sequence, or ndarray
            An initial estimate of the root that should be somewhere near the
            actual root. If not scalar, then `func` must be vectorized and return
            a sequence or array of the same shape as its first argument.
        fprime : callable, optional
            The derivative of the function when available and convenient. If it
            is None (default), then the secant method is used.
        args : tuple, optional
            Extra arguments to be used in the function call.
        tol : float, optional
            The allowable error of the root's value. If `func` is complex-valued,
            a larger `tol` is recommended as both the real and imaginary parts
            of `x` contribute to ``|x - x0|``.
        maxiter : int, optional
            Maximum number of iterations.
        fprime2 : callable, optional
            The second order derivative of the function when available and
            convenient. If it is None (default), then the normal Newton-Raphson
            or the secant method is used. If it is not None, then Halley's method
            is used.
        x1 : float, optional
            Another estimate of the root that should be somewhere near the
            actual root. Used if `fprime` is not provided.
        rtol : float, optional
            Tolerance (relative) for termination.
        full_output : bool, optional
            If `full_output` is False (default), the root is returned.
            If True and `x0` is scalar, the return value is ``(x, r)``, where ``x``
            is the root and ``r`` is a `RootResults` object.
            If True and `x0` is non-scalar, the return value is ``(x, converged,
            zero_der)`` (see Returns section for details).
        disp : bool, optional
            If True, raise a RuntimeError if the algorithm didn't converge, with
            the error message containing the number of iterations and current
            function value. Otherwise, the convergence status is recorded in a
            `RootResults` return object.
            Ignored if `x0` is not scalar.
            *Note: this has little to do with displaying, however,
            the `disp` keyword cannot be renamed for backwards compatibility.*
    
        Returns
        -------
        root : float, sequence, or ndarray
            Estimated location where function is zero.
        r : `RootResults`, optional
            Present if ``full_output=True`` and `x0` is scalar.
            Object containing information about the convergence. In particular,
            ``r.converged`` is True if the routine converged.
        converged : ndarray of bool, optional
            Present if ``full_output=True`` and `x0` is non-scalar.
            For vector functions, indicates which elements converged successfully.
        zero_der : ndarray of bool, optional
            Present if ``full_output=True`` and `x0` is non-scalar.
            For vector functions, indicates which elements had a zero derivative.
    
        See Also
        --------
        root_scalar : interface to root solvers for scalar functions
        root : interface to root solvers for multi-input, multi-output functions
    
        Notes
        -----
        The convergence rate of the Newton-Raphson method is quadratic,
        the Halley method is cubic, and the secant method is
        sub-quadratic. This means that if the function is well-behaved
        the actual error in the estimated root after the nth iteration
        is approximately the square (cube for Halley) of the error
        after the (n-1)th step. However, the stopping criterion used
        here is the step size and there is no guarantee that a root
        has been found. Consequently, the result should be verified.
        Safer algorithms are brentq, brenth, ridder, and bisect,
        but they all require that the root first be bracketed in an
        interval where the function changes sign. The brentq algorithm
        is recommended for general use in one dimensional problems
        when such an interval has been found.
    
        When `newton` is used with arrays, it is best suited for the following
        types of problems:
    
        * The initial guesses, `x0`, are all relatively the same distance from
          the roots.
        * Some or all of the extra arguments, `args`, are also arrays so that a
          class of similar problems can be solved together.
        * The size of the initial guesses, `x0`, is larger than O(100) elements.
          Otherwise, a naive loop may perform as well or better than a vector.
    
        Examples
        --------
        >>> import numpy as np
        >>> import matplotlib.pyplot as plt
        >>> from scipy import optimize
    
        >>> def f(x):
        ...     return (x**3 - 1)  # only one real root at x = 1
    
        ``fprime`` is not provided, use the secant method:
    
        >>> root = optimize.newton(f, 1.5)
        >>> root
        1.0000000000000016
        >>> root = optimize.newton(f, 1.5, fprime2=lambda x: 6 * x)
        >>> root
        1.0000000000000016
    
        Only ``fprime`` is provided, use the Newton-Raphson method:
    
        >>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2)
        >>> root
        1.0
    
        Both ``fprime2`` and ``fprime`` are provided, use Halley's method:
    
        >>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2,
        ...                        fprime2=lambda x: 6 * x)
        >>> root
        1.0
    
        When we want to find roots for a set of related starting values and/or
        function parameters, we can provide both of those as an array of inputs:
    
        >>> f = lambda x, a: x**3 - a
        >>> fder = lambda x, a: 3 * x**2
        >>> rng = np.random.default_rng()
        >>> x = rng.standard_normal(100)
        >>> a = np.arange(-50, 50)
        >>> vec_res = optimize.newton(f, x, fprime=fder, args=(a, ), maxiter=200)
    
        The above is the equivalent of solving for each value in ``(x, a)``
        separately in a for-loop, just faster:
    
        >>> loop_res = [optimize.newton(f, x0, fprime=fder, args=(a0,),
        ...                             maxiter=200)
        ...             for x0, a0 in zip(x, a)]
        >>> np.allclose(vec_res, loop_res)
        True
    
        Plot the results found for all values of ``a``:
    
        >>> analytical_result = np.sign(a) * np.abs(a)**(1/3)
        >>> fig, ax = plt.subplots()
        >>> ax.plot(a, analytical_result, 'o')
        >>> ax.plot(a, vec_res, '.')
        >>> ax.set_xlabel('$a$')
        >>> ax.set_ylabel('$x$ where $f(x, a)=0$')
        >>> plt.show()
    
        """
        if tol <= 0:
            raise ValueError("tol too small (%g <= 0)" % tol)
        maxiter = operator.index(maxiter)
        if maxiter < 1:
            raise ValueError("maxiter must be greater than 0")
        if np.size(x0) > 1:
            return _array_newton(func, x0, fprime, args, tol, maxiter, fprime2,
                                 full_output)
    
        # Convert to float (don't use float(x0); this works also for complex x0)
        # Use np.asarray because we want x0 to be a numpy object, not a Python
        # object. e.g. np.complex(1+1j) > 0 is possible, but (1 + 1j) > 0 raises
        # a TypeError
        x0 = np.asarray(x0)[()] * 1.0
        p0 = x0
        funcalls = 0
        if fprime is not None:
            # Newton-Raphson method
            method = "newton"
            for itr in range(maxiter):
                # first evaluate fval
                fval = func(p0, *args)
                funcalls += 1
                # If fval is 0, a root has been found, then terminate
                if fval == 0:
                    return _results_select(
                        full_output, (p0, funcalls, itr, _ECONVERGED), method)
                fder = fprime(p0, *args)
                funcalls += 1
                if fder == 0:
                    msg = "Derivative was zero."
                    if disp:
                        msg += (
                            " Failed to converge after %d iterations, value is %s."
                            % (itr + 1, p0))
                        raise RuntimeError(msg)
                    warnings.warn(msg, RuntimeWarning, stacklevel=2)
                    return _results_select(
                        full_output, (p0, funcalls, itr + 1, _ECONVERR), method)
                newton_step = fval / fder
                if fprime2:
                    fder2 = fprime2(p0, *args)
                    funcalls += 1
                    method = "halley"
                    # Halley's method:
                    #   newton_step /= (1.0 - 0.5 * newton_step * fder2 / fder)
                    # Only do it if denominator stays close enough to 1
                    # Rationale: If 1-adj < 0, then Halley sends x in the
                    # opposite direction to Newton. Doesn't happen if x is close
                    # enough to root.
                    adj = newton_step * fder2 / fder / 2
                    if np.abs(adj) < 1:
                        newton_step /= 1.0 - adj
                p = p0 - newton_step
                if np.isclose(p, p0, rtol=rtol, atol=tol):
                    return _results_select(
                        full_output, (p, funcalls, itr + 1, _ECONVERGED), method)
                p0 = p
        else:
            # Secant method
            method = "secant"
            if x1 is not None:
                if x1 == x0:
                    raise ValueError("x1 and x0 must be different")
                p1 = x1
            else:
                eps = 1e-4
                p1 = x0 * (1 + eps)
                p1 += (eps if p1 >= 0 else -eps)
            q0 = func(p0, *args)
            funcalls += 1
            q1 = func(p1, *args)
            funcalls += 1
            if abs(q1) < abs(q0):
                p0, p1, q0, q1 = p1, p0, q1, q0
            for itr in range(maxiter):
                if q1 == q0:
                    if p1 != p0:
                        msg = "Tolerance of %s reached." % (p1 - p0)
                        if disp:
                            msg += (
                                " Failed to converge after %d iterations, value is %s."
                                % (itr + 1, p1))
                            raise RuntimeError(msg)
                        warnings.warn(msg, RuntimeWarning, stacklevel=2)
                    p = (p1 + p0) / 2.0
                    return _results_select(
                        full_output, (p, funcalls, itr + 1, _ECONVERR), method)
                else:
                    if abs(q1) > abs(q0):
                        p = (-q0 / q1 * p1 + p0) / (1 - q0 / q1)
                    else:
                        p = (-q1 / q0 * p0 + p1) / (1 - q1 / q0)
                if np.isclose(p, p1, rtol=rtol, atol=tol):
                    return _results_select(
                        full_output, (p, funcalls, itr + 1, _ECONVERGED), method)
                p0, q0 = p1, q1
                p1 = p
                q1 = func(p1, *args)
                funcalls += 1
    
        if disp:
            msg = ("Failed to converge after %d iterations, value is %s."
                   % (itr + 1, p))
>           raise RuntimeError(msg)
E           RuntimeError: Failed to converge after 50 iterations, value is nan.

/usr/lib/python3/dist-packages/scipy/optimize/_zeros_py.py:391: RuntimeError
__________________ ERROR at setup of test_plot_calls_plt_plot __________________

    @pytest.fixture()
    def dummy_data():
        mass = 1 * u.kg
        fov = 30 * u.km
>       shadow = Shadow(mass=mass, fov=fov, n_rays=1000)

tests/test_plotting/test_rays/test_shadow.py:15: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3/dist-packages/astropy/units/decorators.py:316: in wrapper
    return_ = wrapped_function(*func_args, **func_kwargs)
einsteinpy/rays/shadow.py:29: in __init__
    root = newton(self._root_equation, 0.1, args=(i,))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

func = <bound method Shadow._root_equation of <einsteinpy.rays.shadow.Shadow object at 0xaf0c9660>>
x0 = np.float64(0.1), fprime = None, args = (np.float64(5.196152422706632),)
tol = 1.48e-08, maxiter = 50, fprime2 = None, x1 = None, rtol = 0.0
full_output = False, disp = True

    def newton(func, x0, fprime=None, args=(), tol=1.48e-8, maxiter=50,
               fprime2=None, x1=None, rtol=0.0,
               full_output=False, disp=True):
        """
        Find a root of a real or complex function using the Newton-Raphson
        (or secant or Halley's) method.
    
        Find a root of the scalar-valued function `func` given a nearby scalar
        starting point `x0`.
        The Newton-Raphson method is used if the derivative `fprime` of `func`
        is provided, otherwise the secant method is used. If the second order
        derivative `fprime2` of `func` is also provided, then Halley's method is
        used.
    
        If `x0` is a sequence with more than one item, `newton` returns an array:
        the roots of the function from each (scalar) starting point in `x0`.
        In this case, `func` must be vectorized to return a sequence or array of
        the same shape as its first argument. If `fprime` (`fprime2`) is given,
        then its return must also have the same shape: each element is the first
        (second) derivative of `func` with respect to its only variable evaluated
        at each element of its first argument.
    
        `newton` is for finding roots of a scalar-valued functions of a single
        variable. For problems involving several variables, see `root`.
    
        Parameters
        ----------
        func : callable
            The function whose root is wanted. It must be a function of a
            single variable of the form ``f(x,a,b,c...)``, where ``a,b,c...``
            are extra arguments that can be passed in the `args` parameter.
        x0 : float, sequence, or ndarray
            An initial estimate of the root that should be somewhere near the
            actual root. If not scalar, then `func` must be vectorized and return
            a sequence or array of the same shape as its first argument.
        fprime : callable, optional
            The derivative of the function when available and convenient. If it
            is None (default), then the secant method is used.
        args : tuple, optional
            Extra arguments to be used in the function call.
        tol : float, optional
            The allowable error of the root's value. If `func` is complex-valued,
            a larger `tol` is recommended as both the real and imaginary parts
            of `x` contribute to ``|x - x0|``.
        maxiter : int, optional
            Maximum number of iterations.
        fprime2 : callable, optional
            The second order derivative of the function when available and
            convenient. If it is None (default), then the normal Newton-Raphson
            or the secant method is used. If it is not None, then Halley's method
            is used.
        x1 : float, optional
            Another estimate of the root that should be somewhere near the
            actual root. Used if `fprime` is not provided.
        rtol : float, optional
            Tolerance (relative) for termination.
        full_output : bool, optional
            If `full_output` is False (default), the root is returned.
            If True and `x0` is scalar, the return value is ``(x, r)``, where ``x``
            is the root and ``r`` is a `RootResults` object.
            If True and `x0` is non-scalar, the return value is ``(x, converged,
            zero_der)`` (see Returns section for details).
        disp : bool, optional
            If True, raise a RuntimeError if the algorithm didn't converge, with
            the error message containing the number of iterations and current
            function value. Otherwise, the convergence status is recorded in a
            `RootResults` return object.
            Ignored if `x0` is not scalar.
            *Note: this has little to do with displaying, however,
            the `disp` keyword cannot be renamed for backwards compatibility.*
    
        Returns
        -------
        root : float, sequence, or ndarray
            Estimated location where function is zero.
        r : `RootResults`, optional
            Present if ``full_output=True`` and `x0` is scalar.
            Object containing information about the convergence. In particular,
            ``r.converged`` is True if the routine converged.
        converged : ndarray of bool, optional
            Present if ``full_output=True`` and `x0` is non-scalar.
            For vector functions, indicates which elements converged successfully.
        zero_der : ndarray of bool, optional
            Present if ``full_output=True`` and `x0` is non-scalar.
            For vector functions, indicates which elements had a zero derivative.
    
        See Also
        --------
        root_scalar : interface to root solvers for scalar functions
        root : interface to root solvers for multi-input, multi-output functions
    
        Notes
        -----
        The convergence rate of the Newton-Raphson method is quadratic,
        the Halley method is cubic, and the secant method is
        sub-quadratic. This means that if the function is well-behaved
        the actual error in the estimated root after the nth iteration
        is approximately the square (cube for Halley) of the error
        after the (n-1)th step. However, the stopping criterion used
        here is the step size and there is no guarantee that a root
        has been found. Consequently, the result should be verified.
        Safer algorithms are brentq, brenth, ridder, and bisect,
        but they all require that the root first be bracketed in an
        interval where the function changes sign. The brentq algorithm
        is recommended for general use in one dimensional problems
        when such an interval has been found.
    
        When `newton` is used with arrays, it is best suited for the following
        types of problems:
    
        * The initial guesses, `x0`, are all relatively the same distance from
          the roots.
        * Some or all of the extra arguments, `args`, are also arrays so that a
          class of similar problems can be solved together.
        * The size of the initial guesses, `x0`, is larger than O(100) elements.
          Otherwise, a naive loop may perform as well or better than a vector.
    
        Examples
        --------
        >>> import numpy as np
        >>> import matplotlib.pyplot as plt
        >>> from scipy import optimize
    
        >>> def f(x):
        ...     return (x**3 - 1)  # only one real root at x = 1
    
        ``fprime`` is not provided, use the secant method:
    
        >>> root = optimize.newton(f, 1.5)
        >>> root
        1.0000000000000016
        >>> root = optimize.newton(f, 1.5, fprime2=lambda x: 6 * x)
        >>> root
        1.0000000000000016
    
        Only ``fprime`` is provided, use the Newton-Raphson method:
    
        >>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2)
        >>> root
        1.0
    
        Both ``fprime2`` and ``fprime`` are provided, use Halley's method:
    
        >>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2,
        ...                        fprime2=lambda x: 6 * x)
        >>> root
        1.0
    
        When we want to find roots for a set of related starting values and/or
        function parameters, we can provide both of those as an array of inputs:
    
        >>> f = lambda x, a: x**3 - a
        >>> fder = lambda x, a: 3 * x**2
        >>> rng = np.random.default_rng()
        >>> x = rng.standard_normal(100)
        >>> a = np.arange(-50, 50)
        >>> vec_res = optimize.newton(f, x, fprime=fder, args=(a, ), maxiter=200)
    
        The above is the equivalent of solving for each value in ``(x, a)``
        separately in a for-loop, just faster:
    
        >>> loop_res = [optimize.newton(f, x0, fprime=fder, args=(a0,),
        ...                             maxiter=200)
        ...             for x0, a0 in zip(x, a)]
        >>> np.allclose(vec_res, loop_res)
        True
    
        Plot the results found for all values of ``a``:
    
        >>> analytical_result = np.sign(a) * np.abs(a)**(1/3)
        >>> fig, ax = plt.subplots()
        >>> ax.plot(a, analytical_result, 'o')
        >>> ax.plot(a, vec_res, '.')
        >>> ax.set_xlabel('$a$')
        >>> ax.set_ylabel('$x$ where $f(x, a)=0$')
        >>> plt.show()
    
        """
        if tol <= 0:
            raise ValueError("tol too small (%g <= 0)" % tol)
        maxiter = operator.index(maxiter)
        if maxiter < 1:
            raise ValueError("maxiter must be greater than 0")
        if np.size(x0) > 1:
            return _array_newton(func, x0, fprime, args, tol, maxiter, fprime2,
                                 full_output)
    
        # Convert to float (don't use float(x0); this works also for complex x0)
        # Use np.asarray because we want x0 to be a numpy object, not a Python
        # object. e.g. np.complex(1+1j) > 0 is possible, but (1 + 1j) > 0 raises
        # a TypeError
        x0 = np.asarray(x0)[()] * 1.0
        p0 = x0
        funcalls = 0
        if fprime is not None:
            # Newton-Raphson method
            method = "newton"
            for itr in range(maxiter):
                # first evaluate fval
                fval = func(p0, *args)
                funcalls += 1
                # If fval is 0, a root has been found, then terminate
                if fval == 0:
                    return _results_select(
                        full_output, (p0, funcalls, itr, _ECONVERGED), method)
                fder = fprime(p0, *args)
                funcalls += 1
                if fder == 0:
                    msg = "Derivative was zero."
                    if disp:
                        msg += (
                            " Failed to converge after %d iterations, value is %s."
                            % (itr + 1, p0))
                        raise RuntimeError(msg)
                    warnings.warn(msg, RuntimeWarning, stacklevel=2)
                    return _results_select(
                        full_output, (p0, funcalls, itr + 1, _ECONVERR), method)
                newton_step = fval / fder
                if fprime2:
                    fder2 = fprime2(p0, *args)
                    funcalls += 1
                    method = "halley"
                    # Halley's method:
                    #   newton_step /= (1.0 - 0.5 * newton_step * fder2 / fder)
                    # Only do it if denominator stays close enough to 1
                    # Rationale: If 1-adj < 0, then Halley sends x in the
                    # opposite direction to Newton. Doesn't happen if x is close
                    # enough to root.
                    adj = newton_step * fder2 / fder / 2
                    if np.abs(adj) < 1:
                        newton_step /= 1.0 - adj
                p = p0 - newton_step
                if np.isclose(p, p0, rtol=rtol, atol=tol):
                    return _results_select(
                        full_output, (p, funcalls, itr + 1, _ECONVERGED), method)
                p0 = p
        else:
            # Secant method
            method = "secant"
            if x1 is not None:
                if x1 == x0:
                    raise ValueError("x1 and x0 must be different")
                p1 = x1
            else:
                eps = 1e-4
                p1 = x0 * (1 + eps)
                p1 += (eps if p1 >= 0 else -eps)
            q0 = func(p0, *args)
            funcalls += 1
            q1 = func(p1, *args)
            funcalls += 1
            if abs(q1) < abs(q0):
                p0, p1, q0, q1 = p1, p0, q1, q0
            for itr in range(maxiter):
                if q1 == q0:
                    if p1 != p0:
                        msg = "Tolerance of %s reached." % (p1 - p0)
                        if disp:
                            msg += (
                                " Failed to converge after %d iterations, value is %s."
                                % (itr + 1, p1))
                            raise RuntimeError(msg)
                        warnings.warn(msg, RuntimeWarning, stacklevel=2)
                    p = (p1 + p0) / 2.0
                    return _results_select(
                        full_output, (p, funcalls, itr + 1, _ECONVERR), method)
                else:
                    if abs(q1) > abs(q0):
                        p = (-q0 / q1 * p1 + p0) / (1 - q0 / q1)
                    else:
                        p = (-q1 / q0 * p0 + p1) / (1 - q1 / q0)
                if np.isclose(p, p1, rtol=rtol, atol=tol):
                    return _results_select(
                        full_output, (p, funcalls, itr + 1, _ECONVERGED), method)
                p0, q0 = p1, q1
                p1 = p
                q1 = func(p1, *args)
                funcalls += 1
    
        if disp:
            msg = ("Failed to converge after %d iterations, value is %s."
                   % (itr + 1, p))
>           raise RuntimeError(msg)
E           RuntimeError: Failed to converge after 50 iterations, value is nan.

/usr/lib/python3/dist-packages/scipy/optimize/_zeros_py.py:391: RuntimeError
=============================== warnings summary ===============================
einsteinpy/ijit.py:30
.pybuild/cpython3_3.13_einsteinpy/build/tests/test_ijit/test_ijit_without_numba.py::test_decorator
  /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build/einsteinpy/ijit.py:30: UserWarning:
  
  Could not import numba package. All einsteinpy functions will work properly but the CPU intensive algorithms will be slow. Consider installing numba to boost performance.

.pybuild/cpython3_3.13_einsteinpy/build/tests/test_symbolic/test_christoffel.py::test_properties
  /usr/lib/python3/dist-packages/_pytest/python.py:163: PytestReturnNotNoneWarning:
  
  Expected None, but .pybuild/cpython3_3.13_einsteinpy/build/tests/test_symbolic/test_christoffel.py::test_properties returned True, which will be an error in a future version of pytest.  Did you mean to use `assert` instead of `return`?

.pybuild/cpython3_3.13_einsteinpy/build/tests/test_symbolic/test_schouten.py::test_SchoutenTensor_with_arbritary_config
  /usr/lib/python3/dist-packages/_pytest/python.py:163: PytestReturnNotNoneWarning:
  
  Expected None, but .pybuild/cpython3_3.13_einsteinpy/build/tests/test_symbolic/test_schouten.py::test_SchoutenTensor_with_arbritary_config returned True, which will be an error in a future version of pytest.  Did you mean to use `assert` instead of `return`?

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
ERROR tests/test_plotting/test_rays/test_shadow.py::test_plotter_has_correct_attributes
ERROR tests/test_plotting/test_rays/test_shadow.py::test_plot_calls_plt_plot
====== 285 passed, 9 xfailed, 4 warnings, 2 errors in 1343.04s (0:22:23) =======
E: pybuild pybuild:389: test: plugin distutils failed with: exit code=1: cd /build/reproducible-path/einsteinpy-0.4.0/.pybuild/cpython3_3.13_einsteinpy/build; python3.13 -m pytest tests
dh_auto_test: error: pybuild --test --test-pytest -i python{version} -p "3.12 3.13" returned exit code 13
make: *** [debian/rules:6: binary] Error 25
dpkg-buildpackage: error: debian/rules binary subprocess returned exit status 2
I: copying local configuration
E: Failed autobuilding of package
I: unmounting dev/ptmx filesystem
I: unmounting dev/pts filesystem
I: unmounting dev/shm filesystem
I: unmounting proc filesystem
I: unmounting sys filesystem
I: cleaning the build env 
I: removing directory /srv/workspace/pbuilder/30296 and its subdirectories