Warning: Permanently added '54.167.64.127' (ED25519) to the list of known hosts.

You can reproduce this build on your computer by running:

  sudo dnf install copr-rpmbuild
  /usr/bin/copr-rpmbuild --verbose --drop-resultdir --task-url https://copr.fedorainfracloud.org/backend/get-build-task/8715311-fedora-41-x86_64 --chroot fedora-41-x86_64


Version: 1.2
PID: 9616
Logging PID: 9617
Task:
{'allow_user_ssh': False,
 'appstream': False,
 'background': True,
 'build_id': 8715311,
 'buildroot_pkgs': [],
 'chroot': 'fedora-41-x86_64',
 'enable_net': False,
 'fedora_review': False,
 'git_hash': '088ac66daebd6b76699551b06204e829bfb78533',
 'git_repo': 'https://copr-dist-git.fedorainfracloud.org/git/@copr/PyPI/python-tapyoca',
 'isolation': 'default',
 'memory_reqs': 2048,
 'package_name': 'python-tapyoca',
 'package_version': '0.0.4-1',
 'project_dirname': 'PyPI',
 'project_name': 'PyPI',
 'project_owner': '@copr',
 'repo_priority': None,
 'repos': [{'baseurl': 'https://download.copr.fedorainfracloud.org/results/@copr/PyPI/fedora-41-x86_64/',
            'id': 'copr_base',
            'name': 'Copr repository',
            'priority': None}],
 'sandbox': '@copr/PyPI--ksurma',
 'source_json': {},
 'source_type': None,
 'ssh_public_keys': None,
 'storage': None,
 'submitter': 'ksurma',
 'tags': [],
 'task_id': '8715311-fedora-41-x86_64',
 'timeout': 18000,
 'uses_devel_repo': False,
 'with_opts': [],
 'without_opts': []}

Running: git clone https://copr-dist-git.fedorainfracloud.org/git/@copr/PyPI/python-tapyoca /var/lib/copr-rpmbuild/workspace/workdir-a8jvyids/python-tapyoca --depth 500 --no-single-branch --recursive

cmd: ['git', 'clone', 'https://copr-dist-git.fedorainfracloud.org/git/@copr/PyPI/python-tapyoca', '/var/lib/copr-rpmbuild/workspace/workdir-a8jvyids/python-tapyoca', '--depth', '500', '--no-single-branch', '--recursive']
cwd: .
rc: 0
stdout: 
stderr: Cloning into '/var/lib/copr-rpmbuild/workspace/workdir-a8jvyids/python-tapyoca'...

Running: git checkout 088ac66daebd6b76699551b06204e829bfb78533 --

cmd: ['git', 'checkout', '088ac66daebd6b76699551b06204e829bfb78533', '--']
cwd: /var/lib/copr-rpmbuild/workspace/workdir-a8jvyids/python-tapyoca
rc: 0
stdout: 
stderr: Note: switching to '088ac66daebd6b76699551b06204e829bfb78533'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by switching back to a branch.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -c with the switch command. Example:

  git switch -c <new-branch-name>

Or undo this operation with:

  git switch -

Turn off this advice by setting config variable advice.detachedHead to false

HEAD is now at 088ac66 automatic import of python-tapyoca

Running: dist-git-client sources

cmd: ['dist-git-client', 'sources']
cwd: /var/lib/copr-rpmbuild/workspace/workdir-a8jvyids/python-tapyoca
rc: 0
stdout: 
stderr: INFO: Reading stdout from command: git rev-parse --abbrev-ref HEAD
INFO: Reading stdout from command: git rev-parse HEAD
INFO: Reading sources specification file: sources
INFO: Downloading tapyoca-0.0.4.tar.gz
INFO: Reading stdout from command: curl --help all
INFO: Calling: curl -H Pragma: -o tapyoca-0.0.4.tar.gz --location --connect-timeout 60 --retry 3 --retry-delay 10 --remote-time --show-error --fail --retry-all-errors https://copr-dist-git.fedorainfracloud.org/repo/pkgs/@copr/PyPI/python-tapyoca/tapyoca-0.0.4.tar.gz/md5/cb8449a5a6c8f33605c653a47dba8f88/tapyoca-0.0.4.tar.gz
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 96711  100 96711    0     0  7062k      0 --:--:-- --:--:-- --:--:-- 7264k
INFO: Reading stdout from command: md5sum tapyoca-0.0.4.tar.gz

/usr/bin/tail: /var/lib/copr-rpmbuild/main.log: file truncated
Running (timeout=18000): unbuffer mock --spec /var/lib/copr-rpmbuild/workspace/workdir-a8jvyids/python-tapyoca/python-tapyoca.spec --sources /var/lib/copr-rpmbuild/workspace/workdir-a8jvyids/python-tapyoca --resultdir /var/lib/copr-rpmbuild/results --uniqueext 1740863228.713984 -r /var/lib/copr-rpmbuild/results/configs/child.cfg
INFO: mock.py version 6.0 starting (python version = 3.13.0, NVR = mock-6.0-1.fc41), args: /usr/libexec/mock/mock --spec /var/lib/copr-rpmbuild/workspace/workdir-a8jvyids/python-tapyoca/python-tapyoca.spec --sources /var/lib/copr-rpmbuild/workspace/workdir-a8jvyids/python-tapyoca --resultdir /var/lib/copr-rpmbuild/results --uniqueext 1740863228.713984 -r /var/lib/copr-rpmbuild/results/configs/child.cfg
Start(bootstrap): init plugins
INFO: tmpfs initialized
INFO: selinux enabled
INFO: chroot_scan: initialized
INFO: compress_logs: initialized
Finish(bootstrap): init plugins
Start: init plugins
INFO: tmpfs initialized
INFO: selinux enabled
INFO: chroot_scan: initialized
INFO: compress_logs: initialized
Finish: init plugins
INFO: Signal handler active
Start: run
INFO: Start(/var/lib/copr-rpmbuild/workspace/workdir-a8jvyids/python-tapyoca/python-tapyoca.spec)  Config(fedora-41-x86_64)
Start: clean chroot
Finish: clean chroot
Mock Version: 6.0
INFO: Mock Version: 6.0
Start(bootstrap): chroot init
INFO: mounting tmpfs at /var/lib/mock/fedora-41-x86_64-bootstrap-1740863228.713984/root.
INFO: calling preinit hooks
INFO: enabled root cache
INFO: enabled package manager cache
Start(bootstrap): cleaning package manager metadata
Finish(bootstrap): cleaning package manager metadata
INFO: Guessed host environment type: unknown
INFO: Using container image: registry.fedoraproject.org/fedora:41
INFO: Pulling image: registry.fedoraproject.org/fedora:41
INFO: Tagging container image as mock-bootstrap-7d5dfecb-5796-4b1b-86ff-53376ef5b41e
INFO: Checking that 3c6d584f74ca54f6f15eeca09fa68980b95a6495180a3b555b07dbe703a4f7cc image matches host's architecture
INFO: Copy content of container 3c6d584f74ca54f6f15eeca09fa68980b95a6495180a3b555b07dbe703a4f7cc to /var/lib/mock/fedora-41-x86_64-bootstrap-1740863228.713984/root
INFO: mounting 3c6d584f74ca54f6f15eeca09fa68980b95a6495180a3b555b07dbe703a4f7cc with podman image mount
INFO: image 3c6d584f74ca54f6f15eeca09fa68980b95a6495180a3b555b07dbe703a4f7cc as /var/lib/containers/storage/overlay/78abfe0bb6597fe1bbc72d94ba61d9bea5d50210b158df7e85d0652c301e9bc6/merged
INFO: umounting image 3c6d584f74ca54f6f15eeca09fa68980b95a6495180a3b555b07dbe703a4f7cc (/var/lib/containers/storage/overlay/78abfe0bb6597fe1bbc72d94ba61d9bea5d50210b158df7e85d0652c301e9bc6/merged) with podman image umount
INFO: Removing image mock-bootstrap-7d5dfecb-5796-4b1b-86ff-53376ef5b41e
INFO: Package manager dnf5 detected and used (fallback)
INFO: Not updating bootstrap chroot, bootstrap_image_ready=True
Start(bootstrap): creating root cache
Finish(bootstrap): creating root cache
Finish(bootstrap): chroot init
Start: chroot init
INFO: mounting tmpfs at /var/lib/mock/fedora-41-x86_64-1740863228.713984/root.
INFO: calling preinit hooks
INFO: enabled root cache
INFO: enabled package manager cache
Start: cleaning package manager metadata
Finish: cleaning package manager metadata
INFO: enabled HW Info plugin
INFO: Package manager dnf5 detected and used (direct choice)
INFO: Buildroot is handled by package management downloaded with a bootstrap image:
  rpm-4.20.0-1.fc41.x86_64
  rpm-sequoia-1.7.0-5.fc41.x86_64
  dnf5-5.2.10.0-2.fc41.x86_64
  dnf5-plugins-5.2.10.0-2.fc41.x86_64
Start: installing minimal buildroot with dnf5
Updating and loading repositories:
 updates                                100% |  36.7 MiB/s |  10.8 MiB |  00m00s
 fedora                                 100% |  52.9 MiB/s |  35.3 MiB |  00m01s
 Copr repository                        100% | 193.1 MiB/s |  42.1 MiB |  00m00s
Repositories loaded.
Package                            Arch   Version                     Repository      Size
Installing group/module packages:
 bash                              x86_64 5.2.32-1.fc41               fedora       8.2 MiB
 bzip2                             x86_64 1.0.8-19.fc41               fedora      95.7 KiB
 coreutils                         x86_64 9.5-11.fc41                 updates      5.7 MiB
 cpio                              x86_64 2.15-2.fc41                 fedora       1.1 MiB
 diffutils                         x86_64 3.10-8.fc41                 fedora       1.6 MiB
 fedora-release-common             noarch 41-29                       updates     19.7 KiB
 findutils                         x86_64 1:4.10.0-4.fc41             fedora       1.8 MiB
 gawk                              x86_64 5.3.0-4.fc41                fedora       1.7 MiB
 glibc-minimal-langpack            x86_64 2.40-21.fc41                updates      0.0   B
 grep                              x86_64 3.11-9.fc41                 fedora       1.0 MiB
 gzip                              x86_64 1.13-2.fc41                 fedora     389.0 KiB
 info                              x86_64 7.1-3.fc41                  fedora     361.8 KiB
 patch                             x86_64 2.7.6-25.fc41               fedora     266.7 KiB
 redhat-rpm-config                 noarch 293-1.fc41                  fedora     183.5 KiB
 rpm-build                         x86_64 4.20.0-1.fc41               fedora     194.3 KiB
 sed                               x86_64 4.9-3.fc41                  fedora     861.5 KiB
 shadow-utils                      x86_64 2:4.15.1-12.fc41            fedora       4.1 MiB
 tar                               x86_64 2:1.35-4.fc41               fedora       2.9 MiB
 unzip                             x86_64 6.0-64.fc41                 fedora     386.8 KiB
 util-linux                        x86_64 2.40.4-1.fc41               updates      3.6 MiB
 which                             x86_64 2.21-42.fc41                fedora      80.2 KiB
 xz                                x86_64 1:5.6.2-2.fc41              fedora       1.2 MiB
Installing dependencies:
 add-determinism                   x86_64 0.3.6-3.fc41                updates      2.4 MiB
 alternatives                      x86_64 1.31-1.fc41                 updates     64.8 KiB
 ansible-srpm-macros               noarch 1-16.fc41                   fedora      35.7 KiB
 audit-libs                        x86_64 4.0.3-1.fc41                updates    351.3 KiB
 authselect                        x86_64 1.5.0-8.fc41                fedora     157.6 KiB
 authselect-libs                   x86_64 1.5.0-8.fc41                fedora     822.2 KiB
 basesystem                        noarch 11-21.fc41                  fedora       0.0   B
 binutils                          x86_64 2.43.1-5.fc41               updates     27.4 MiB
 build-reproducibility-srpm-macros noarch 0.3.6-3.fc41                updates    735.0   B
 bzip2-libs                        x86_64 1.0.8-19.fc41               fedora      80.7 KiB
 ca-certificates                   noarch 2024.2.69_v8.0.401-1.0.fc41 fedora       2.4 MiB
 coreutils-common                  x86_64 9.5-11.fc41                 updates     11.2 MiB
 cracklib                          x86_64 2.9.11-6.fc41               fedora     238.9 KiB
 crypto-policies                   noarch 20250124-1.git4d262e7.fc41  updates    137.4 KiB
 curl                              x86_64 8.9.1-3.fc41                updates    793.5 KiB
 cyrus-sasl-lib                    x86_64 2.1.28-27.fc41              fedora       2.3 MiB
 debugedit                         x86_64 5.1-4.fc41                  updates    197.7 KiB
 dwz                               x86_64 0.15-8.fc41                 fedora     298.9 KiB
 ed                                x86_64 1.20.2-2.fc41               fedora     146.9 KiB
 efi-srpm-macros                   noarch 5-13.fc41                   updates     40.2 KiB
 elfutils                          x86_64 0.192-9.fc41                updates      2.7 MiB
 elfutils-debuginfod-client        x86_64 0.192-9.fc41                updates     84.2 KiB
 elfutils-default-yama-scope       noarch 0.192-9.fc41                updates      1.8 KiB
 elfutils-libelf                   x86_64 0.192-9.fc41                updates      1.2 MiB
 elfutils-libs                     x86_64 0.192-9.fc41                updates    670.2 KiB
 fedora-gpg-keys                   noarch 41-1                        fedora     126.4 KiB
 fedora-release                    noarch 41-29                       updates      0.0   B
 fedora-release-identity-basic     noarch 41-29                       updates    682.0   B
 fedora-repos                      noarch 41-1                        fedora       4.9 KiB
 file                              x86_64 5.45-7.fc41                 fedora     103.5 KiB
 file-libs                         x86_64 5.45-7.fc41                 fedora       9.9 MiB
 filesystem                        x86_64 3.18-23.fc41                fedora     106.0   B
 fonts-srpm-macros                 noarch 1:2.0.5-17.fc41             fedora      55.8 KiB
 forge-srpm-macros                 noarch 0.4.0-1.fc41                updates     38.9 KiB
 fpc-srpm-macros                   noarch 1.3-13.fc41                 fedora     144.0   B
 gdb-minimal                       x86_64 16.2-1.fc41                 updates     13.3 MiB
 gdbm                              x86_64 1:1.23-7.fc41               fedora     460.9 KiB
 gdbm-libs                         x86_64 1:1.23-7.fc41               fedora     121.9 KiB
 ghc-srpm-macros                   noarch 1.9.1-2.fc41                fedora     747.0   B
 glibc                             x86_64 2.40-21.fc41                updates      6.7 MiB
 glibc-common                      x86_64 2.40-21.fc41                updates      1.0 MiB
 glibc-gconv-extra                 x86_64 2.40-21.fc41                updates      7.9 MiB
 gmp                               x86_64 1:6.3.0-2.fc41              fedora     811.4 KiB
 gnat-srpm-macros                  noarch 6-6.fc41                    fedora       1.0 KiB
 go-srpm-macros                    noarch 3.6.0-5.fc41                updates     60.8 KiB
 jansson                           x86_64 2.13.1-10.fc41              fedora      88.3 KiB
 json-c                            x86_64 0.17-4.fc41                 fedora      82.4 KiB
 kernel-srpm-macros                noarch 1.0-24.fc41                 fedora       1.9 KiB
 keyutils-libs                     x86_64 1.6.3-4.fc41                fedora      54.4 KiB
 krb5-libs                         x86_64 1.21.3-4.fc41               updates      2.3 MiB
 libacl                            x86_64 2.3.2-2.fc41                fedora      40.0 KiB
 libarchive                        x86_64 3.7.4-4.fc41                updates    926.6 KiB
 libattr                           x86_64 2.5.2-4.fc41                fedora      28.5 KiB
 libblkid                          x86_64 2.40.4-1.fc41               updates    257.2 KiB
 libbrotli                         x86_64 1.1.0-5.fc41                fedora     837.6 KiB
 libcap                            x86_64 2.70-4.fc41                 fedora     220.2 KiB
 libcap-ng                         x86_64 0.8.5-3.fc41                fedora      69.2 KiB
 libcom_err                        x86_64 1.47.1-6.fc41               fedora      67.2 KiB
 libcurl                           x86_64 8.9.1-3.fc41                updates    809.3 KiB
 libeconf                          x86_64 0.6.2-3.fc41                fedora      58.0 KiB
 libevent                          x86_64 2.1.12-14.fc41              fedora     895.7 KiB
 libfdisk                          x86_64 2.40.4-1.fc41               updates    356.4 KiB
 libffi                            x86_64 3.4.6-3.fc41                fedora      86.4 KiB
 libgcc                            x86_64 14.2.1-7.fc41               updates    270.9 KiB
 libgomp                           x86_64 14.2.1-7.fc41               updates    514.2 KiB
 libidn2                           x86_64 2.3.7-2.fc41                fedora     329.1 KiB
 libmount                          x86_64 2.40.4-1.fc41               updates    348.8 KiB
 libnghttp2                        x86_64 1.62.1-2.fc41               fedora     166.1 KiB
 libnsl2                           x86_64 2.0.1-2.fc41                fedora      57.9 KiB
 libpkgconf                        x86_64 2.3.0-1.fc41                fedora      78.2 KiB
 libpsl                            x86_64 0.21.5-4.fc41               fedora      80.5 KiB
 libpwquality                      x86_64 1.4.5-11.fc41               fedora     417.8 KiB
 libselinux                        x86_64 3.7-5.fc41                  fedora     181.0 KiB
 libsemanage                       x86_64 3.7-2.fc41                  fedora     293.5 KiB
 libsepol                          x86_64 3.7-2.fc41                  fedora     817.8 KiB
 libsmartcols                      x86_64 2.40.4-1.fc41               updates    176.2 KiB
 libssh                            x86_64 0.10.6-8.fc41               fedora     513.3 KiB
 libssh-config                     noarch 0.10.6-8.fc41               fedora     277.0   B
 libstdc++                         x86_64 14.2.1-7.fc41               updates      2.7 MiB
 libtasn1                          x86_64 4.20.0-1.fc41               updates    180.4 KiB
 libtirpc                          x86_64 1.3.6-1.rc3.fc41            updates    197.6 KiB
 libtool-ltdl                      x86_64 2.4.7-12.fc41               fedora      66.2 KiB
 libunistring                      x86_64 1.1-8.fc41                  fedora       1.7 MiB
 libutempter                       x86_64 1.2.1-15.fc41               fedora      57.7 KiB
 libuuid                           x86_64 2.40.4-1.fc41               updates     39.9 KiB
 libverto                          x86_64 0.3.2-9.fc41                fedora      29.5 KiB
 libxcrypt                         x86_64 4.4.38-6.fc41               updates    288.5 KiB
 libxml2                           x86_64 2.12.9-1.fc41               updates      1.7 MiB
 libzstd                           x86_64 1.5.7-1.fc41                updates    804.0 KiB
 lua-libs                          x86_64 5.4.6-6.fc41                fedora     285.0 KiB
 lua-srpm-macros                   noarch 1-14.fc41                   fedora       1.3 KiB
 lz4-libs                          x86_64 1.10.0-1.fc41               fedora     145.5 KiB
 mpfr                              x86_64 4.2.1-5.fc41                fedora     832.1 KiB
 ncurses-base                      noarch 6.5-2.20240629.fc41         fedora     326.3 KiB
 ncurses-libs                      x86_64 6.5-2.20240629.fc41         fedora     975.2 KiB
 ocaml-srpm-macros                 noarch 10-3.fc41                   fedora       1.9 KiB
 openblas-srpm-macros              noarch 2-18.fc41                   fedora     112.0   B
 openldap                          x86_64 2.6.8-7.fc41                updates    631.4 KiB
 openssl-libs                      x86_64 1:3.2.4-1.fc41              updates      7.8 MiB
 p11-kit                           x86_64 0.25.5-3.fc41               fedora       2.2 MiB
 p11-kit-trust                     x86_64 0.25.5-3.fc41               fedora     391.4 KiB
 package-notes-srpm-macros         noarch 0.5-12.fc41                 fedora       1.6 KiB
 pam                               x86_64 1.6.1-7.fc41                updates      1.8 MiB
 pam-libs                          x86_64 1.6.1-7.fc41                updates    139.0 KiB
 pcre2                             x86_64 10.44-1.fc41.1              fedora     653.5 KiB
 pcre2-syntax                      noarch 10.44-1.fc41.1              fedora     251.6 KiB
 perl-srpm-macros                  noarch 1-56.fc41                   fedora     861.0   B
 pkgconf                           x86_64 2.3.0-1.fc41                fedora      88.6 KiB
 pkgconf-m4                        noarch 2.3.0-1.fc41                fedora      14.4 KiB
 pkgconf-pkg-config                x86_64 2.3.0-1.fc41                fedora     989.0   B
 popt                              x86_64 1.19-7.fc41                 fedora     136.9 KiB
 publicsuffix-list-dafsa           noarch 20250116-1.fc41             updates     68.5 KiB
 pyproject-srpm-macros             noarch 1.17.0-1.fc41               updates      1.9 KiB
 python-srpm-macros                noarch 3.13-3.fc41                 fedora      51.0 KiB
 qt5-srpm-macros                   noarch 5.15.15-1.fc41              fedora     500.0   B
 qt6-srpm-macros                   noarch 6.8.2-1.fc41                updates    456.0   B
 readline                          x86_64 8.2-10.fc41                 fedora     493.2 KiB
 rpm                               x86_64 4.20.0-1.fc41               fedora       3.1 MiB
 rpm-build-libs                    x86_64 4.20.0-1.fc41               fedora     206.7 KiB
 rpm-libs                          x86_64 4.20.0-1.fc41               fedora     725.9 KiB
 rpm-sequoia                       x86_64 1.7.0-5.fc41                updates      2.4 MiB
 rust-srpm-macros                  noarch 26.3-3.fc41                 fedora       4.8 KiB
 setup                             noarch 2.15.0-8.fc41               updates    720.7 KiB
 sqlite-libs                       x86_64 3.46.1-2.fc41               updates      1.5 MiB
 systemd-libs                      x86_64 256.11-1.fc41               updates      2.0 MiB
 util-linux-core                   x86_64 2.40.4-1.fc41               updates      1.5 MiB
 xxhash-libs                       x86_64 0.8.3-1.fc41                updates     88.5 KiB
 xz-libs                           x86_64 1:5.6.2-2.fc41              fedora     214.4 KiB
 zig-srpm-macros                   noarch 1-3.fc41                    fedora       1.1 KiB
 zip                               x86_64 3.0-41.fc41                 fedora     703.2 KiB
 zlib-ng-compat                    x86_64 2.2.3-2.fc41                updates    141.9 KiB
 zstd                              x86_64 1.5.7-1.fc41                updates      1.7 MiB
Installing groups:
 Buildsystem building group                                                               

Transaction Summary:
 Installing:       154 packages

Total size of inbound packages is 53 MiB. Need to download 53 MiB.
After this operation, 181 MiB extra will be used (install 181 MiB, remove 0 B).
[  1/154] bzip2-0:1.0.8-19.fc41.x86_64  100% |   5.7 MiB/s |  52.5 KiB |  00m00s
[  2/154] cpio-0:2.15-2.fc41.x86_64     100% |  21.9 MiB/s | 291.8 KiB |  00m00s
[  3/154] diffutils-0:3.10-8.fc41.x86_6 100% |  99.0 MiB/s | 405.4 KiB |  00m00s
[  4/154] bash-0:5.2.32-1.fc41.x86_64   100% | 106.3 MiB/s |   1.8 MiB |  00m00s
[  5/154] grep-0:3.11-9.fc41.x86_64     100% |  73.2 MiB/s | 299.7 KiB |  00m00s
[  6/154] findutils-1:4.10.0-4.fc41.x86 100% | 107.1 MiB/s | 548.5 KiB |  00m00s
[  7/154] gzip-0:1.13-2.fc41.x86_64     100% |  41.6 MiB/s | 170.2 KiB |  00m00s
[  8/154] info-0:7.1-3.fc41.x86_64      100% |  59.4 MiB/s | 182.5 KiB |  00m00s
[  9/154] patch-0:2.7.6-25.fc41.x86_64  100% |  42.6 MiB/s | 131.0 KiB |  00m00s
[ 10/154] redhat-rpm-config-0:293-1.fc4 100% |  40.1 MiB/s |  82.0 KiB |  00m00s
[ 11/154] rpm-build-0:4.20.0-1.fc41.x86 100% |  40.4 MiB/s |  82.8 KiB |  00m00s
[ 12/154] sed-0:4.9-3.fc41.x86_64       100% | 103.4 MiB/s | 317.7 KiB |  00m00s
[ 13/154] tar-2:1.35-4.fc41.x86_64      100% | 210.1 MiB/s | 860.7 KiB |  00m00s
[ 14/154] unzip-0:6.0-64.fc41.x86_64    100% |  60.2 MiB/s | 184.9 KiB |  00m00s
[ 15/154] shadow-utils-2:4.15.1-12.fc41 100% | 188.5 MiB/s |   1.3 MiB |  00m00s
[ 16/154] which-0:2.21-42.fc41.x86_64   100% |  20.3 MiB/s |  41.6 KiB |  00m00s
[ 17/154] xz-1:5.6.2-2.fc41.x86_64      100% | 153.5 MiB/s | 471.5 KiB |  00m00s
[ 18/154] fedora-release-common-0:41-29 100% |  11.5 MiB/s |  23.6 KiB |  00m00s
[ 19/154] glibc-minimal-langpack-0:2.40 100% |  49.0 MiB/s | 100.4 KiB |  00m00s
[ 20/154] coreutils-0:9.5-11.fc41.x86_6 100% | 183.3 MiB/s |   1.1 MiB |  00m00s
[ 21/154] gawk-0:5.3.0-4.fc41.x86_64    100% | 178.5 MiB/s |   1.1 MiB |  00m00s
[ 22/154] util-linux-0:2.40.4-1.fc41.x8 100% | 155.0 MiB/s |   1.1 MiB |  00m00s
[ 23/154] ncurses-libs-0:6.5-2.20240629 100% |  65.2 MiB/s | 334.0 KiB |  00m00s
[ 24/154] filesystem-0:3.18-23.fc41.x86 100% | 135.9 MiB/s |   1.1 MiB |  00m00s
[ 25/154] bzip2-libs-0:1.0.8-19.fc41.x8 100% |  20.1 MiB/s |  41.1 KiB |  00m00s
[ 26/154] libselinux-0:3.7-5.fc41.x86_6 100% |  42.9 MiB/s |  87.8 KiB |  00m00s
[ 27/154] libattr-0:2.5.2-4.fc41.x86_64 100% |  17.7 MiB/s |  18.2 KiB |  00m00s
[ 28/154] pcre2-0:10.44-1.fc41.1.x86_64 100% | 118.7 MiB/s | 243.1 KiB |  00m00s
[ 29/154] ed-0:1.20.2-2.fc41.x86_64     100% |  26.6 MiB/s |  81.8 KiB |  00m00s
[ 30/154] ansible-srpm-macros-0:1-16.fc 100% |  20.3 MiB/s |  20.8 KiB |  00m00s
[ 31/154] dwz-0:0.15-8.fc41.x86_64      100% |  67.8 MiB/s | 138.9 KiB |  00m00s
[ 32/154] fonts-srpm-macros-1:2.0.5-17. 100% |  26.3 MiB/s |  27.0 KiB |  00m00s
[ 33/154] file-0:5.45-7.fc41.x86_64     100% |  16.0 MiB/s |  49.1 KiB |  00m00s
[ 34/154] fpc-srpm-macros-0:1.3-13.fc41 100% |   7.8 MiB/s |   8.0 KiB |  00m00s
[ 35/154] ghc-srpm-macros-0:1.9.1-2.fc4 100% |   8.8 MiB/s |   9.1 KiB |  00m00s
[ 36/154] gnat-srpm-macros-0:6-6.fc41.n 100% |   4.4 MiB/s |   9.0 KiB |  00m00s
[ 37/154] kernel-srpm-macros-0:1.0-24.f 100% |   4.8 MiB/s |   9.9 KiB |  00m00s
[ 38/154] lua-srpm-macros-0:1-14.fc41.n 100% |   4.3 MiB/s |   8.9 KiB |  00m00s
[ 39/154] openblas-srpm-macros-0:2-18.f 100% |   3.8 MiB/s |   7.7 KiB |  00m00s
[ 40/154] ocaml-srpm-macros-0:10-3.fc41 100% |   4.5 MiB/s |   9.2 KiB |  00m00s
[ 41/154] package-notes-srpm-macros-0:0 100% |   3.2 MiB/s |   9.8 KiB |  00m00s
[ 42/154] perl-srpm-macros-0:1-56.fc41. 100% |   8.3 MiB/s |   8.5 KiB |  00m00s
[ 43/154] python-srpm-macros-0:3.13-3.f 100% |  11.6 MiB/s |  23.7 KiB |  00m00s
[ 44/154] qt5-srpm-macros-0:5.15.15-1.f 100% |   8.7 MiB/s |   8.9 KiB |  00m00s
[ 45/154] zig-srpm-macros-0:1-3.fc41.no 100% |   7.9 MiB/s |   8.1 KiB |  00m00s
[ 46/154] rust-srpm-macros-0:26.3-3.fc4 100% |   5.9 MiB/s |  12.1 KiB |  00m00s
[ 47/154] rpm-0:4.20.0-1.fc41.x86_64    100% | 133.6 MiB/s | 547.4 KiB |  00m00s
[ 48/154] popt-0:1.19-7.fc41.x86_64     100% |  64.4 MiB/s |  65.9 KiB |  00m00s
[ 49/154] readline-0:8.2-10.fc41.x86_64 100% | 104.1 MiB/s | 213.2 KiB |  00m00s
[ 50/154] zip-0:3.0-41.fc41.x86_64      100% |  51.7 MiB/s | 264.8 KiB |  00m00s
[ 51/154] rpm-build-libs-0:4.20.0-1.fc4 100% |  32.1 MiB/s |  98.6 KiB |  00m00s
[ 52/154] libeconf-0:0.6.2-3.fc41.x86_6 100% |  31.4 MiB/s |  32.2 KiB |  00m00s
[ 53/154] libacl-0:2.3.2-2.fc41.x86_64  100% |  12.0 MiB/s |  24.5 KiB |  00m00s
[ 54/154] rpm-libs-0:4.20.0-1.fc41.x86_ 100% |  75.5 MiB/s | 309.4 KiB |  00m00s
[ 55/154] libsemanage-0:3.7-2.fc41.x86_ 100% |  56.8 MiB/s | 116.3 KiB |  00m00s
[ 56/154] xz-libs-1:5.6.2-2.fc41.x86_64 100% |  54.6 MiB/s | 111.8 KiB |  00m00s
[ 57/154] libcap-0:2.70-4.fc41.x86_64   100% |  42.3 MiB/s |  86.7 KiB |  00m00s
[ 58/154] gmp-1:6.3.0-2.fc41.x86_64     100% |  77.6 MiB/s | 318.0 KiB |  00m00s
[ 59/154] fedora-repos-0:41-1.noarch    100% |   4.5 MiB/s |   9.2 KiB |  00m00s
[ 60/154] mpfr-0:4.2.1-5.fc41.x86_64    100% |  84.5 MiB/s | 346.3 KiB |  00m00s
[ 61/154] coreutils-common-0:9.5-11.fc4 100% | 176.9 MiB/s |   2.1 MiB |  00m00s
[ 62/154] glibc-common-0:2.40-21.fc41.x 100% |  76.0 MiB/s | 389.2 KiB |  00m00s
[ 63/154] util-linux-core-0:2.40.4-1.fc 100% | 120.2 MiB/s | 492.2 KiB |  00m00s
[ 64/154] libcap-ng-0:0.8.5-3.fc41.x86_ 100% |  15.9 MiB/s |  32.6 KiB |  00m00s
[ 65/154] libutempter-0:1.2.1-15.fc41.x 100% |  13.0 MiB/s |  26.6 KiB |  00m00s
[ 66/154] ncurses-base-0:6.5-2.20240629 100% |  86.2 MiB/s |  88.3 KiB |  00m00s
[ 67/154] libsepol-0:3.7-2.fc41.x86_64  100% | 111.4 MiB/s | 342.2 KiB |  00m00s
[ 68/154] pcre2-syntax-0:10.44-1.fc41.1 100% |  73.2 MiB/s | 149.9 KiB |  00m00s
[ 69/154] file-libs-0:5.45-7.fc41.x86_6 100% | 186.0 MiB/s | 762.0 KiB |  00m00s
[ 70/154] fedora-gpg-keys-0:41-1.noarch 100% |  65.3 MiB/s | 133.7 KiB |  00m00s
[ 71/154] lua-libs-0:5.4.6-6.fc41.x86_6 100% |  32.2 MiB/s | 132.0 KiB |  00m00s
[ 72/154] basesystem-0:11-21.fc41.noarc 100% |   7.2 MiB/s |   7.4 KiB |  00m00s
[ 73/154] audit-libs-0:4.0.3-1.fc41.x86 100% |  40.6 MiB/s | 124.8 KiB |  00m00s
[ 74/154] glibc-gconv-extra-0:2.40-21.f 100% | 185.9 MiB/s |   1.7 MiB |  00m00s
[ 75/154] libxcrypt-0:4.4.38-6.fc41.x86 100% |  24.9 MiB/s | 127.6 KiB |  00m00s
[ 76/154] glibc-0:2.40-21.fc41.x86_64   100% | 158.5 MiB/s |   2.2 MiB |  00m00s
[ 77/154] pam-libs-0:1.6.1-7.fc41.x86_6 100% |  14.0 MiB/s |  57.4 KiB |  00m00s
[ 78/154] setup-0:2.15.0-8.fc41.noarch  100% |  50.3 MiB/s | 154.6 KiB |  00m00s
[ 79/154] sqlite-libs-0:3.46.1-2.fc41.x 100% | 140.4 MiB/s | 719.0 KiB |  00m00s
[ 80/154] libzstd-0:1.5.7-1.fc41.x86_64 100% |  44.0 MiB/s | 315.4 KiB |  00m00s
[ 81/154] rpm-sequoia-0:1.7.0-5.fc41.x8 100% | 111.3 MiB/s | 911.4 KiB |  00m00s
[ 82/154] zlib-ng-compat-0:2.2.3-2.fc41 100% |  38.5 MiB/s |  78.9 KiB |  00m00s
[ 83/154] elfutils-libelf-0:0.192-9.fc4 100% | 101.2 MiB/s | 207.3 KiB |  00m00s
[ 84/154] elfutils-libs-0:0.192-9.fc41. 100% | 127.9 MiB/s | 261.8 KiB |  00m00s
[ 85/154] elfutils-debuginfod-client-0: 100% |  22.6 MiB/s |  46.3 KiB |  00m00s
[ 86/154] elfutils-0:0.192-9.fc41.x86_6 100% | 106.7 MiB/s | 546.1 KiB |  00m00s
[ 87/154] json-c-0:0.17-4.fc41.x86_64   100% |  14.3 MiB/s |  44.0 KiB |  00m00s
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[ 89/154] libgomp-0:14.2.1-7.fc41.x86_6 100% | 113.8 MiB/s | 349.5 KiB |  00m00s
[ 90/154] jansson-0:2.13.1-10.fc41.x86_ 100% |  21.7 MiB/s |  44.4 KiB |  00m00s
[ 91/154] debugedit-0:5.1-4.fc41.x86_64 100% |  36.9 MiB/s |  75.6 KiB |  00m00s
[ 92/154] libarchive-0:3.7.4-4.fc41.x86 100% |  99.9 MiB/s | 409.1 KiB |  00m00s
[ 93/154] lz4-libs-0:1.10.0-1.fc41.x86_ 100% |  23.0 MiB/s |  70.7 KiB |  00m00s
[ 94/154] pkgconf-pkg-config-0:2.3.0-1. 100% |   9.8 MiB/s |  10.0 KiB |  00m00s
[ 95/154] zstd-0:1.5.7-1.fc41.x86_64    100% | 118.0 MiB/s | 483.3 KiB |  00m00s
[ 96/154] pkgconf-0:2.3.0-1.fc41.x86_64 100% |  14.7 MiB/s |  45.2 KiB |  00m00s
[ 97/154] pkgconf-m4-0:2.3.0-1.fc41.noa 100% |   7.0 MiB/s |  14.3 KiB |  00m00s
[ 98/154] libpkgconf-0:2.3.0-1.fc41.x86 100% |  18.8 MiB/s |  38.5 KiB |  00m00s
[ 99/154] curl-0:8.9.1-3.fc41.x86_64    100% | 101.5 MiB/s | 311.9 KiB |  00m00s
[100/154] binutils-0:2.43.1-5.fc41.x86_ 100% | 261.7 MiB/s |   6.3 MiB |  00m00s
[101/154] build-reproducibility-srpm-ma 100% |   1.5 MiB/s |  10.8 KiB |  00m00s
[102/154] add-determinism-0:0.3.6-3.fc4 100% | 122.2 MiB/s | 875.9 KiB |  00m00s
[103/154] efi-srpm-macros-0:5-13.fc41.n 100% |  11.0 MiB/s |  22.5 KiB |  00m00s
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[105/154] go-srpm-macros-0:3.6.0-5.fc41 100% |  13.7 MiB/s |  28.0 KiB |  00m00s
[106/154] pyproject-srpm-macros-0:1.17. 100% |   6.8 MiB/s |  14.0 KiB |  00m00s
[107/154] qt6-srpm-macros-0:6.8.2-1.fc4 100% |   4.5 MiB/s |   9.2 KiB |  00m00s
[108/154] libuuid-0:2.40.4-1.fc41.x86_6 100% |  13.2 MiB/s |  27.1 KiB |  00m00s
[109/154] libblkid-0:2.40.4-1.fc41.x86_ 100% |  58.2 MiB/s | 119.2 KiB |  00m00s
[110/154] libstdc++-0:14.2.1-7.fc41.x86 100% | 200.9 MiB/s | 822.8 KiB |  00m00s
[111/154] libsmartcols-0:2.40.4-1.fc41. 100% |  77.5 MiB/s |  79.4 KiB |  00m00s
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[113/154] libfdisk-0:2.40.4-1.fc41.x86_ 100% |  74.5 MiB/s | 152.5 KiB |  00m00s
[114/154] systemd-libs-0:256.11-1.fc41. 100% | 168.9 MiB/s | 691.7 KiB |  00m00s
[115/154] pam-0:1.6.1-7.fc41.x86_64     100% | 108.4 MiB/s | 555.0 KiB |  00m00s
[116/154] authselect-0:1.5.0-8.fc41.x86 100% |  35.6 MiB/s | 145.8 KiB |  00m00s
[117/154] gdbm-libs-1:1.23-7.fc41.x86_6 100% |  13.7 MiB/s |  56.3 KiB |  00m00s
[118/154] libnsl2-0:2.0.1-2.fc41.x86_64 100% |  14.5 MiB/s |  29.6 KiB |  00m00s
[119/154] libpwquality-0:1.4.5-11.fc41. 100% |  58.1 MiB/s | 119.0 KiB |  00m00s
[120/154] cracklib-0:2.9.11-6.fc41.x86_ 100% |  45.0 MiB/s |  92.1 KiB |  00m00s
[121/154] authselect-libs-0:1.5.0-8.fc4 100% |  71.0 MiB/s | 218.0 KiB |  00m00s
[122/154] libtirpc-0:1.3.6-1.rc3.fc41.x 100% |  29.1 MiB/s |  89.4 KiB |  00m00s
[123/154] ca-certificates-0:2024.2.69_v 100% | 170.2 MiB/s | 871.2 KiB |  00m00s
[124/154] openssl-libs-1:3.2.4-1.fc41.x 100% | 231.1 MiB/s |   2.3 MiB |  00m00s
[125/154] libcom_err-0:1.47.1-6.fc41.x8 100% |   8.6 MiB/s |  26.6 KiB |  00m00s
[126/154] gdbm-1:1.23-7.fc41.x86_64     100% |  74.1 MiB/s | 151.8 KiB |  00m00s
[127/154] crypto-policies-0:20250124-1. 100% |  47.8 MiB/s |  97.8 KiB |  00m00s
[128/154] keyutils-libs-0:1.6.3-4.fc41. 100% |  30.9 MiB/s |  31.6 KiB |  00m00s
[129/154] krb5-libs-0:1.21.3-4.fc41.x86 100% | 184.8 MiB/s | 757.0 KiB |  00m00s
[130/154] libverto-0:0.3.2-9.fc41.x86_6 100% |   6.7 MiB/s |  20.7 KiB |  00m00s
[131/154] libxml2-0:2.12.9-1.fc41.x86_6 100% | 128.3 MiB/s | 656.8 KiB |  00m00s
[132/154] elfutils-default-yama-scope-0 100% |   6.0 MiB/s |  12.4 KiB |  00m00s
[133/154] alternatives-0:1.31-1.fc41.x8 100% |  19.2 MiB/s |  39.4 KiB |  00m00s
[134/154] libffi-0:3.4.6-3.fc41.x86_64  100% |  19.5 MiB/s |  39.9 KiB |  00m00s
[135/154] p11-kit-trust-0:0.25.5-3.fc41 100% |  64.5 MiB/s | 132.1 KiB |  00m00s
[136/154] p11-kit-0:0.25.5-3.fc41.x86_6 100% | 159.8 MiB/s | 490.9 KiB |  00m00s
[137/154] libtasn1-0:4.20.0-1.fc41.x86_ 100% |  72.7 MiB/s |  74.4 KiB |  00m00s
[138/154] fedora-release-0:41-29.noarch 100% |  12.5 MiB/s |  12.8 KiB |  00m00s
[139/154] xxhash-libs-0:0.8.3-1.fc41.x8 100% |  17.5 MiB/s |  35.9 KiB |  00m00s
[140/154] fedora-release-identity-basic 100% |   6.6 MiB/s |  13.6 KiB |  00m00s
[141/154] libcurl-0:8.9.1-3.fc41.x86_64 100% | 113.9 MiB/s | 350.0 KiB |  00m00s
[142/154] libbrotli-0:1.1.0-5.fc41.x86_ 100% |  83.1 MiB/s | 340.5 KiB |  00m00s
[143/154] libidn2-0:2.3.7-2.fc41.x86_64 100% |  38.5 MiB/s | 118.4 KiB |  00m00s
[144/154] gdb-minimal-0:16.2-1.fc41.x86 100% | 292.4 MiB/s |   4.4 MiB |  00m00s
[145/154] libnghttp2-0:1.62.1-2.fc41.x8 100% |  12.5 MiB/s |  76.6 KiB |  00m00s
[146/154] libpsl-0:0.21.5-4.fc41.x86_64 100% |  15.6 MiB/s |  64.1 KiB |  00m00s
[147/154] libssh-0:0.10.6-8.fc41.x86_64 100% |  68.9 MiB/s | 211.8 KiB |  00m00s
[148/154] libssh-config-0:0.10.6-8.fc41 100% |   3.0 MiB/s |   9.2 KiB |  00m00s
[149/154] libunistring-0:1.1-8.fc41.x86 100% | 133.0 MiB/s | 544.8 KiB |  00m00s
[150/154] publicsuffix-list-dafsa-0:202 100% |  28.7 MiB/s |  58.8 KiB |  00m00s
[151/154] openldap-0:2.6.8-7.fc41.x86_6 100% |  79.1 MiB/s | 243.0 KiB |  00m00s
[152/154] libevent-0:2.1.12-14.fc41.x86 100% | 125.7 MiB/s | 257.5 KiB |  00m00s
[153/154] cyrus-sasl-lib-0:2.1.28-27.fc 100% | 155.2 MiB/s | 794.9 KiB |  00m00s
[154/154] libtool-ltdl-0:2.4.7-12.fc41. 100% |  11.6 MiB/s |  35.6 KiB |  00m00s
--------------------------------------------------------------------------------
[154/154] Total                         100% |  83.5 MiB/s |  52.8 MiB |  00m01s
Running transaction
Importing OpenPGP key 0xE99D6AD1:
 UserID     : "Fedora (41) <fedora-41-primary@fedoraproject.org>"
 Fingerprint: 466CF2D8B60BC3057AA9453ED0622462E99D6AD1
 From       : file:///usr/share/distribution-gpg-keys/fedora/RPM-GPG-KEY-fedora-41-primary
The key was successfully imported.
[  1/156] Verify package files          100% | 956.0   B/s | 154.0   B |  00m00s
[  2/156] Prepare transaction           100% |   4.4 KiB/s | 154.0   B |  00m00s
[  3/156] Installing libgcc-0:14.2.1-7. 100% | 266.1 MiB/s | 272.5 KiB |  00m00s
[  4/156] Installing publicsuffix-list- 100% |   0.0   B/s |  69.2 KiB |  00m00s
[  5/156] Installing libssh-config-0:0. 100% |   0.0   B/s | 816.0   B |  00m00s
[  6/156] Installing fedora-release-ide 100% |   0.0   B/s | 940.0   B |  00m00s
[  7/156] Installing fedora-gpg-keys-0: 100% |  56.1 MiB/s | 172.2 KiB |  00m00s
[  8/156] Installing fedora-repos-0:41- 100% |   0.0   B/s |   5.7 KiB |  00m00s
[  9/156] Installing fedora-release-com 100% |  23.4 MiB/s |  24.0 KiB |  00m00s
[ 10/156] Installing fedora-release-0:4 100% |   0.0   B/s | 124.0   B |  00m00s
[ 11/156] Installing setup-0:2.15.0-8.f 100% |  64.5 MiB/s | 726.5 KiB |  00m00s
>>> [RPM] /etc/hosts created as /etc/hosts.rpmnew                               
[ 12/156] Installing filesystem-0:3.18- 100% |   3.9 MiB/s | 212.5 KiB |  00m00s
[ 13/156] Installing basesystem-0:11-21 100% |   0.0   B/s | 124.0   B |  00m00s
[ 14/156] Installing qt6-srpm-macros-0: 100% |   0.0   B/s | 732.0   B |  00m00s
[ 15/156] Installing pkgconf-m4-0:2.3.0 100% |   0.0   B/s |  14.8 KiB |  00m00s
[ 16/156] Installing pcre2-syntax-0:10. 100% | 248.1 MiB/s | 254.1 KiB |  00m00s
[ 17/156] Installing ncurses-base-0:6.5 100% |  85.9 MiB/s | 351.7 KiB |  00m00s
[ 18/156] Installing glibc-minimal-lang 100% |   0.0   B/s | 124.0   B |  00m00s
[ 19/156] Installing ncurses-libs-0:6.5 100% | 239.7 MiB/s | 981.8 KiB |  00m00s
[ 20/156] Installing glibc-0:2.40-21.fc 100% | 318.4 MiB/s |   6.7 MiB |  00m00s
[ 21/156] Installing bash-0:5.2.32-1.fc 100% | 453.8 MiB/s |   8.2 MiB |  00m00s
[ 22/156] Installing glibc-common-0:2.4 100% | 210.2 MiB/s |   1.1 MiB |  00m00s
[ 23/156] Installing glibc-gconv-extra- 100% | 309.3 MiB/s |   8.0 MiB |  00m00s
[ 24/156] Installing zlib-ng-compat-0:2 100% |   0.0   B/s | 142.7 KiB |  00m00s
[ 25/156] Installing bzip2-libs-0:1.0.8 100% |   0.0   B/s |  81.8 KiB |  00m00s
[ 26/156] Installing xz-libs-1:5.6.2-2. 100% | 210.4 MiB/s | 215.5 KiB |  00m00s
[ 27/156] Installing popt-0:1.19-7.fc41 100% |  70.1 MiB/s | 143.5 KiB |  00m00s
[ 28/156] Installing readline-0:8.2-10. 100% | 241.8 MiB/s | 495.3 KiB |  00m00s
[ 29/156] Installing libuuid-0:2.40.4-1 100% |   0.0   B/s |  41.0 KiB |  00m00s
[ 30/156] Installing libblkid-0:2.40.4- 100% | 252.1 MiB/s | 258.2 KiB |  00m00s
[ 31/156] Installing libattr-0:2.5.2-4. 100% |   0.0   B/s |  29.5 KiB |  00m00s
[ 32/156] Installing libacl-0:2.3.2-2.f 100% |   0.0   B/s |  40.7 KiB |  00m00s
[ 33/156] Installing gmp-1:6.3.0-2.fc41 100% | 397.3 MiB/s | 813.7 KiB |  00m00s
[ 34/156] Installing libxcrypt-0:4.4.38 100% | 284.4 MiB/s | 291.2 KiB |  00m00s
[ 35/156] Installing libzstd-0:1.5.7-1. 100% | 393.1 MiB/s | 805.1 KiB |  00m00s
[ 36/156] Installing elfutils-libelf-0: 100% | 390.0 MiB/s |   1.2 MiB |  00m00s
[ 37/156] Installing libstdc++-0:14.2.1 100% | 449.9 MiB/s |   2.7 MiB |  00m00s
[ 38/156] Installing libeconf-0:0.6.2-3 100% |   0.0   B/s |  59.7 KiB |  00m00s
[ 39/156] Installing gdbm-libs-1:1.23-7 100% | 120.7 MiB/s | 123.6 KiB |  00m00s
[ 40/156] Installing dwz-0:0.15-8.fc41. 100% | 293.3 MiB/s | 300.3 KiB |  00m00s
[ 41/156] Installing mpfr-0:4.2.1-5.fc4 100% | 407.1 MiB/s | 833.7 KiB |  00m00s
[ 42/156] Installing gawk-0:5.3.0-4.fc4 100% | 346.4 MiB/s |   1.7 MiB |  00m00s
[ 43/156] Installing unzip-0:6.0-64.fc4 100% | 381.1 MiB/s | 390.3 KiB |  00m00s
[ 44/156] Installing file-libs-0:5.45-7 100% | 764.2 MiB/s |   9.9 MiB |  00m00s
[ 45/156] Installing file-0:5.45-7.fc41 100% |  11.4 MiB/s | 105.0 KiB |  00m00s
[ 46/156] Installing crypto-policies-0: 100% |  40.0 MiB/s | 163.7 KiB |  00m00s
[ 47/156] Installing pcre2-0:10.44-1.fc 100% | 319.8 MiB/s | 654.9 KiB |  00m00s
[ 48/156] Installing grep-0:3.11-9.fc41 100% | 250.8 MiB/s |   1.0 MiB |  00m00s
[ 49/156] Installing xz-1:5.6.2-2.fc41. 100% | 241.0 MiB/s |   1.2 MiB |  00m00s
[ 50/156] Installing libcap-ng-0:0.8.5- 100% |   0.0   B/s |  71.0 KiB |  00m00s
[ 51/156] Installing audit-libs-0:4.0.3 100% | 345.1 MiB/s | 353.4 KiB |  00m00s
[ 52/156] Installing pam-libs-0:1.6.1-7 100% | 137.9 MiB/s | 141.3 KiB |  00m00s
[ 53/156] Installing libcap-0:2.70-4.fc 100% | 110.0 MiB/s | 225.2 KiB |  00m00s
[ 54/156] Installing systemd-libs-0:256 100% | 398.0 MiB/s |   2.0 MiB |  00m00s
[ 55/156] Installing libsepol-0:3.7-2.f 100% | 399.8 MiB/s | 818.8 KiB |  00m00s
[ 56/156] Installing libselinux-0:3.7-5 100% | 178.0 MiB/s | 182.3 KiB |  00m00s
[ 57/156] Installing sed-0:4.9-3.fc41.x 100% | 283.1 MiB/s | 869.7 KiB |  00m00s
[ 58/156] Installing findutils-1:4.10.0 100% | 371.6 MiB/s |   1.9 MiB |  00m00s
[ 59/156] Installing libmount-0:2.40.4- 100% | 341.6 MiB/s | 349.8 KiB |  00m00s
[ 60/156] Installing lua-libs-0:5.4.6-6 100% | 279.5 MiB/s | 286.2 KiB |  00m00s
[ 61/156] Installing lz4-libs-0:1.10.0- 100% | 143.1 MiB/s | 146.6 KiB |  00m00s
[ 62/156] Installing libsmartcols-0:2.4 100% | 173.2 MiB/s | 177.4 KiB |  00m00s
[ 63/156] Installing libcom_err-0:1.47. 100% |   0.0   B/s |  68.3 KiB |  00m00s
[ 64/156] Installing alternatives-0:1.3 100% |   0.0   B/s |  66.4 KiB |  00m00s
[ 65/156] Installing libffi-0:3.4.6-3.f 100% |   0.0   B/s |  87.8 KiB |  00m00s
[ 66/156] Installing libtasn1-0:4.20.0- 100% | 177.9 MiB/s | 182.2 KiB |  00m00s
[ 67/156] Installing p11-kit-0:0.25.5-3 100% | 315.3 MiB/s |   2.2 MiB |  00m00s
[ 68/156] Installing libunistring-0:1.1 100% | 432.7 MiB/s |   1.7 MiB |  00m00s
[ 69/156] Installing libidn2-0:2.3.7-2. 100% | 163.6 MiB/s | 335.1 KiB |  00m00s
[ 70/156] Installing libpsl-0:0.21.5-4. 100% |   0.0   B/s |  81.7 KiB |  00m00s
[ 71/156] Installing p11-kit-trust-0:0. 100% |  42.7 MiB/s | 393.1 KiB |  00m00s
[ 72/156] Installing util-linux-core-0: 100% | 245.8 MiB/s |   1.5 MiB |  00m00s
[ 73/156] Installing zstd-0:1.5.7-1.fc4 100% | 427.5 MiB/s |   1.7 MiB |  00m00s
[ 74/156] Installing tar-2:1.35-4.fc41. 100% | 422.6 MiB/s |   3.0 MiB |  00m00s
[ 75/156] Installing libsemanage-0:3.7- 100% | 144.2 MiB/s | 295.2 KiB |  00m00s
[ 76/156] Installing shadow-utils-2:4.1 100% | 173.6 MiB/s |   4.2 MiB |  00m00s
[ 77/156] Installing libutempter-0:1.2. 100% |  58.3 MiB/s |  59.7 KiB |  00m00s
[ 78/156] Installing zip-0:3.0-41.fc41. 100% | 345.2 MiB/s | 707.1 KiB |  00m00s
[ 79/156] Installing gdbm-1:1.23-7.fc41 100% | 227.4 MiB/s | 465.8 KiB |  00m00s
[ 80/156] Installing cyrus-sasl-lib-0:2 100% | 384.3 MiB/s |   2.3 MiB |  00m00s
[ 81/156] Installing libfdisk-0:2.40.4- 100% | 349.0 MiB/s | 357.4 KiB |  00m00s
[ 82/156] Installing libxml2-0:2.12.9-1 100% | 421.5 MiB/s |   1.7 MiB |  00m00s
[ 83/156] Installing bzip2-0:1.0.8-19.f 100% |  97.8 MiB/s | 100.2 KiB |  00m00s
[ 84/156] Installing sqlite-libs-0:3.46 100% | 368.2 MiB/s |   1.5 MiB |  00m00s
[ 85/156] Installing add-determinism-0: 100% | 471.2 MiB/s |   2.4 MiB |  00m00s
[ 86/156] Installing build-reproducibil 100% |   0.0   B/s |   1.0 KiB |  00m00s
[ 87/156] Installing ed-0:1.20.2-2.fc41 100% | 145.7 MiB/s | 149.2 KiB |  00m00s
[ 88/156] Installing patch-0:2.7.6-25.f 100% | 261.9 MiB/s | 268.2 KiB |  00m00s
[ 89/156] Installing elfutils-default-y 100% | 340.5 KiB/s |   2.0 KiB |  00m00s
[ 90/156] Installing elfutils-libs-0:0. 100% | 328.2 MiB/s | 672.1 KiB |  00m00s
[ 91/156] Installing cpio-0:2.15-2.fc41 100% | 274.9 MiB/s |   1.1 MiB |  00m00s
[ 92/156] Installing diffutils-0:3.10-8 100% | 318.1 MiB/s |   1.6 MiB |  00m00s
[ 93/156] Installing json-c-0:0.17-4.fc 100% |  81.7 MiB/s |  83.6 KiB |  00m00s
[ 94/156] Installing libgomp-0:14.2.1-7 100% | 503.5 MiB/s | 515.6 KiB |  00m00s
[ 95/156] Installing jansson-0:2.13.1-1 100% |   0.0   B/s |  89.7 KiB |  00m00s
[ 96/156] Installing libpkgconf-0:2.3.0 100% |   0.0   B/s |  79.3 KiB |  00m00s
[ 97/156] Installing pkgconf-0:2.3.0-1. 100% |  89.0 MiB/s |  91.1 KiB |  00m00s
[ 98/156] Installing pkgconf-pkg-config 100% |   0.0   B/s |   1.8 KiB |  00m00s
[ 99/156] Installing keyutils-libs-0:1. 100% |   0.0   B/s |  55.8 KiB |  00m00s
[100/156] Installing libverto-0:0.3.2-9 100% |   0.0   B/s |  31.3 KiB |  00m00s
[101/156] Installing xxhash-libs-0:0.8. 100% |   0.0   B/s |  89.9 KiB |  00m00s
[102/156] Installing libbrotli-0:1.1.0- 100% | 410.1 MiB/s | 839.9 KiB |  00m00s
[103/156] Installing libnghttp2-0:1.62. 100% | 163.2 MiB/s | 167.1 KiB |  00m00s
[104/156] Installing libtool-ltdl-0:2.4 100% |  65.7 MiB/s |  67.3 KiB |  00m00s
[105/156] Installing coreutils-common-0 100% | 447.6 MiB/s |  11.2 MiB |  00m00s
[106/156] Installing openssl-libs-1:3.2 100% | 489.3 MiB/s |   7.8 MiB |  00m00s
[107/156] Installing coreutils-0:9.5-11 100% | 285.3 MiB/s |   5.7 MiB |  00m00s
[108/156] Installing ca-certificates-0: 100% |   4.3 MiB/s |   2.4 MiB |  00m01s
[109/156] Installing krb5-libs-0:1.21.3 100% | 289.9 MiB/s |   2.3 MiB |  00m00s
[110/156] Installing libarchive-0:3.7.4 100% | 302.3 MiB/s | 928.6 KiB |  00m00s
[111/156] Installing libtirpc-0:1.3.6-1 100% | 194.7 MiB/s | 199.4 KiB |  00m00s
[112/156] Installing gzip-0:1.13-2.fc41 100% | 192.7 MiB/s | 394.6 KiB |  00m00s
[113/156] Installing authselect-libs-0: 100% | 204.4 MiB/s | 837.2 KiB |  00m00s
[114/156] Installing cracklib-0:2.9.11- 100% |  81.5 MiB/s | 250.3 KiB |  00m00s
[115/156] Installing libpwquality-0:1.4 100% | 140.0 MiB/s | 430.1 KiB |  00m00s
[116/156] Installing libnsl2-0:2.0.1-2. 100% |  57.7 MiB/s |  59.1 KiB |  00m00s
[117/156] Installing pam-0:1.6.1-7.fc41 100% | 187.8 MiB/s |   1.9 MiB |  00m00s
[118/156] Installing libssh-0:0.10.6-8. 100% | 251.7 MiB/s | 515.4 KiB |  00m00s
[119/156] Installing rpm-sequoia-0:1.7. 100% | 403.1 MiB/s |   2.4 MiB |  00m00s
[120/156] Installing rpm-libs-0:4.20.0- 100% | 355.2 MiB/s | 727.4 KiB |  00m00s
[121/156] Installing rpm-build-libs-0:4 100% | 202.6 MiB/s | 207.5 KiB |  00m00s
[122/156] Installing libevent-0:2.1.12- 100% | 292.8 MiB/s | 899.5 KiB |  00m00s
[123/156] Installing openldap-0:2.6.8-7 100% | 310.2 MiB/s | 635.2 KiB |  00m00s
[124/156] Installing libcurl-0:8.9.1-3. 100% | 395.7 MiB/s | 810.4 KiB |  00m00s
[125/156] Installing elfutils-debuginfo 100% |  84.4 MiB/s |  86.5 KiB |  00m00s
[126/156] Installing elfutils-0:0.192-9 100% | 382.2 MiB/s |   2.7 MiB |  00m00s
[127/156] Installing binutils-0:2.43.1- 100% | 402.8 MiB/s |  27.4 MiB |  00m00s
[128/156] Installing gdb-minimal-0:16.2 100% | 442.0 MiB/s |  13.3 MiB |  00m00s
[129/156] Installing debugedit-0:5.1-4. 100% | 195.7 MiB/s | 200.4 KiB |  00m00s
[130/156] Installing curl-0:8.9.1-3.fc4 100% |  70.7 MiB/s | 796.0 KiB |  00m00s
[131/156] Installing rpm-0:4.20.0-1.fc4 100% | 208.8 MiB/s |   2.5 MiB |  00m00s
[132/156] Installing lua-srpm-macros-0: 100% |   0.0   B/s |   1.9 KiB |  00m00s
[133/156] Installing zig-srpm-macros-0: 100% |   0.0   B/s |   1.7 KiB |  00m00s
[134/156] Installing efi-srpm-macros-0: 100% |   0.0   B/s |  41.2 KiB |  00m00s
[135/156] Installing rust-srpm-macros-0 100% |   0.0   B/s |   5.6 KiB |  00m00s
[136/156] Installing qt5-srpm-macros-0: 100% |   0.0   B/s | 776.0   B |  00m00s
[137/156] Installing perl-srpm-macros-0 100% |   0.0   B/s |   1.1 KiB |  00m00s
[138/156] Installing package-notes-srpm 100% |   0.0   B/s |   2.0 KiB |  00m00s
[139/156] Installing openblas-srpm-macr 100% |   0.0   B/s | 392.0   B |  00m00s
[140/156] Installing ocaml-srpm-macros- 100% |   0.0   B/s |   2.2 KiB |  00m00s
[141/156] Installing kernel-srpm-macros 100% |   0.0   B/s |   2.3 KiB |  00m00s
[142/156] Installing gnat-srpm-macros-0 100% |   0.0   B/s |   1.3 KiB |  00m00s
[143/156] Installing ghc-srpm-macros-0: 100% |   0.0   B/s |   1.0 KiB |  00m00s
[144/156] Installing fpc-srpm-macros-0: 100% |   0.0   B/s | 420.0   B |  00m00s
[145/156] Installing ansible-srpm-macro 100% |   0.0   B/s |  36.2 KiB |  00m00s
[146/156] Installing python-srpm-macros 100% |   0.0   B/s |  52.2 KiB |  00m00s
[147/156] Installing fonts-srpm-macros- 100% |   0.0   B/s |  57.0 KiB |  00m00s
[148/156] Installing forge-srpm-macros- 100% |   0.0   B/s |  40.3 KiB |  00m00s
[149/156] Installing go-srpm-macros-0:3 100% |  60.5 MiB/s |  62.0 KiB |  00m00s
[150/156] Installing redhat-rpm-config- 100% | 185.6 MiB/s | 190.1 KiB |  00m00s
[151/156] Installing rpm-build-0:4.20.0 100% |  99.0 MiB/s | 202.8 KiB |  00m00s
[152/156] Installing pyproject-srpm-mac 100% |   2.4 MiB/s |   2.5 KiB |  00m00s
[153/156] Installing util-linux-0:2.40. 100% | 176.4 MiB/s |   3.7 MiB |  00m00s
[154/156] Installing authselect-0:1.5.0 100% |  79.1 MiB/s | 161.9 KiB |  00m00s
[155/156] Installing which-0:2.21-42.fc 100% |  80.5 MiB/s |  82.4 KiB |  00m00s
[156/156] Installing info-0:7.1-3.fc41. 100% | 481.6 KiB/s | 362.2 KiB |  00m01s
Complete!
Finish: installing minimal buildroot with dnf5
Start: creating root cache
Finish: creating root cache
Finish: chroot init
INFO: Installed packages:
INFO: add-determinism-0.3.6-3.fc41.x86_64
alternatives-1.31-1.fc41.x86_64
ansible-srpm-macros-1-16.fc41.noarch
audit-libs-4.0.3-1.fc41.x86_64
authselect-1.5.0-8.fc41.x86_64
authselect-libs-1.5.0-8.fc41.x86_64
basesystem-11-21.fc41.noarch
bash-5.2.32-1.fc41.x86_64
binutils-2.43.1-5.fc41.x86_64
build-reproducibility-srpm-macros-0.3.6-3.fc41.noarch
bzip2-1.0.8-19.fc41.x86_64
bzip2-libs-1.0.8-19.fc41.x86_64
ca-certificates-2024.2.69_v8.0.401-1.0.fc41.noarch
coreutils-9.5-11.fc41.x86_64
coreutils-common-9.5-11.fc41.x86_64
cpio-2.15-2.fc41.x86_64
cracklib-2.9.11-6.fc41.x86_64
crypto-policies-20250124-1.git4d262e7.fc41.noarch
curl-8.9.1-3.fc41.x86_64
cyrus-sasl-lib-2.1.28-27.fc41.x86_64
debugedit-5.1-4.fc41.x86_64
diffutils-3.10-8.fc41.x86_64
dwz-0.15-8.fc41.x86_64
ed-1.20.2-2.fc41.x86_64
efi-srpm-macros-5-13.fc41.noarch
elfutils-0.192-9.fc41.x86_64
elfutils-debuginfod-client-0.192-9.fc41.x86_64
elfutils-default-yama-scope-0.192-9.fc41.noarch
elfutils-libelf-0.192-9.fc41.x86_64
elfutils-libs-0.192-9.fc41.x86_64
fedora-gpg-keys-41-1.noarch
fedora-release-41-29.noarch
fedora-release-common-41-29.noarch
fedora-release-identity-basic-41-29.noarch
fedora-repos-41-1.noarch
file-5.45-7.fc41.x86_64
file-libs-5.45-7.fc41.x86_64
filesystem-3.18-23.fc41.x86_64
findutils-4.10.0-4.fc41.x86_64
fonts-srpm-macros-2.0.5-17.fc41.noarch
forge-srpm-macros-0.4.0-1.fc41.noarch
fpc-srpm-macros-1.3-13.fc41.noarch
gawk-5.3.0-4.fc41.x86_64
gdb-minimal-16.2-1.fc41.x86_64
gdbm-1.23-7.fc41.x86_64
gdbm-libs-1.23-7.fc41.x86_64
ghc-srpm-macros-1.9.1-2.fc41.noarch
glibc-2.40-21.fc41.x86_64
glibc-common-2.40-21.fc41.x86_64
glibc-gconv-extra-2.40-21.fc41.x86_64
glibc-minimal-langpack-2.40-21.fc41.x86_64
gmp-6.3.0-2.fc41.x86_64
gnat-srpm-macros-6-6.fc41.noarch
go-srpm-macros-3.6.0-5.fc41.noarch
gpg-pubkey-e99d6ad1-64d2612c
grep-3.11-9.fc41.x86_64
gzip-1.13-2.fc41.x86_64
info-7.1-3.fc41.x86_64
jansson-2.13.1-10.fc41.x86_64
json-c-0.17-4.fc41.x86_64
kernel-srpm-macros-1.0-24.fc41.noarch
keyutils-libs-1.6.3-4.fc41.x86_64
krb5-libs-1.21.3-4.fc41.x86_64
libacl-2.3.2-2.fc41.x86_64
libarchive-3.7.4-4.fc41.x86_64
libattr-2.5.2-4.fc41.x86_64
libblkid-2.40.4-1.fc41.x86_64
libbrotli-1.1.0-5.fc41.x86_64
libcap-2.70-4.fc41.x86_64
libcap-ng-0.8.5-3.fc41.x86_64
libcom_err-1.47.1-6.fc41.x86_64
libcurl-8.9.1-3.fc41.x86_64
libeconf-0.6.2-3.fc41.x86_64
libevent-2.1.12-14.fc41.x86_64
libfdisk-2.40.4-1.fc41.x86_64
libffi-3.4.6-3.fc41.x86_64
libgcc-14.2.1-7.fc41.x86_64
libgomp-14.2.1-7.fc41.x86_64
libidn2-2.3.7-2.fc41.x86_64
libmount-2.40.4-1.fc41.x86_64
libnghttp2-1.62.1-2.fc41.x86_64
libnsl2-2.0.1-2.fc41.x86_64
libpkgconf-2.3.0-1.fc41.x86_64
libpsl-0.21.5-4.fc41.x86_64
libpwquality-1.4.5-11.fc41.x86_64
libselinux-3.7-5.fc41.x86_64
libsemanage-3.7-2.fc41.x86_64
libsepol-3.7-2.fc41.x86_64
libsmartcols-2.40.4-1.fc41.x86_64
libssh-0.10.6-8.fc41.x86_64
libssh-config-0.10.6-8.fc41.noarch
libstdc++-14.2.1-7.fc41.x86_64
libtasn1-4.20.0-1.fc41.x86_64
libtirpc-1.3.6-1.rc3.fc41.x86_64
libtool-ltdl-2.4.7-12.fc41.x86_64
libunistring-1.1-8.fc41.x86_64
libutempter-1.2.1-15.fc41.x86_64
libuuid-2.40.4-1.fc41.x86_64
libverto-0.3.2-9.fc41.x86_64
libxcrypt-4.4.38-6.fc41.x86_64
libxml2-2.12.9-1.fc41.x86_64
libzstd-1.5.7-1.fc41.x86_64
lua-libs-5.4.6-6.fc41.x86_64
lua-srpm-macros-1-14.fc41.noarch
lz4-libs-1.10.0-1.fc41.x86_64
mpfr-4.2.1-5.fc41.x86_64
ncurses-base-6.5-2.20240629.fc41.noarch
ncurses-libs-6.5-2.20240629.fc41.x86_64
ocaml-srpm-macros-10-3.fc41.noarch
openblas-srpm-macros-2-18.fc41.noarch
openldap-2.6.8-7.fc41.x86_64
openssl-libs-3.2.4-1.fc41.x86_64
p11-kit-0.25.5-3.fc41.x86_64
p11-kit-trust-0.25.5-3.fc41.x86_64
package-notes-srpm-macros-0.5-12.fc41.noarch
pam-1.6.1-7.fc41.x86_64
pam-libs-1.6.1-7.fc41.x86_64
patch-2.7.6-25.fc41.x86_64
pcre2-10.44-1.fc41.1.x86_64
pcre2-syntax-10.44-1.fc41.1.noarch
perl-srpm-macros-1-56.fc41.noarch
pkgconf-2.3.0-1.fc41.x86_64
pkgconf-m4-2.3.0-1.fc41.noarch
pkgconf-pkg-config-2.3.0-1.fc41.x86_64
popt-1.19-7.fc41.x86_64
publicsuffix-list-dafsa-20250116-1.fc41.noarch
pyproject-srpm-macros-1.17.0-1.fc41.noarch
python-srpm-macros-3.13-3.fc41.noarch
qt5-srpm-macros-5.15.15-1.fc41.noarch
qt6-srpm-macros-6.8.2-1.fc41.noarch
readline-8.2-10.fc41.x86_64
redhat-rpm-config-293-1.fc41.noarch
rpm-4.20.0-1.fc41.x86_64
rpm-build-4.20.0-1.fc41.x86_64
rpm-build-libs-4.20.0-1.fc41.x86_64
rpm-libs-4.20.0-1.fc41.x86_64
rpm-sequoia-1.7.0-5.fc41.x86_64
rust-srpm-macros-26.3-3.fc41.noarch
sed-4.9-3.fc41.x86_64
setup-2.15.0-8.fc41.noarch
shadow-utils-4.15.1-12.fc41.x86_64
sqlite-libs-3.46.1-2.fc41.x86_64
systemd-libs-256.11-1.fc41.x86_64
tar-1.35-4.fc41.x86_64
unzip-6.0-64.fc41.x86_64
util-linux-2.40.4-1.fc41.x86_64
util-linux-core-2.40.4-1.fc41.x86_64
which-2.21-42.fc41.x86_64
xxhash-libs-0.8.3-1.fc41.x86_64
xz-5.6.2-2.fc41.x86_64
xz-libs-5.6.2-2.fc41.x86_64
zig-srpm-macros-1-3.fc41.noarch
zip-3.0-41.fc41.x86_64
zlib-ng-compat-2.2.3-2.fc41.x86_64
zstd-1.5.7-1.fc41.x86_64
Start: buildsrpm
Start: rpmbuild -bs
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1740787200
Wrote: /builddir/build/SRPMS/python-tapyoca-0.0.4-1.fc41.src.rpm
Finish: rpmbuild -bs
INFO: chroot_scan: 1 files copied to /var/lib/copr-rpmbuild/results/chroot_scan
INFO: /var/lib/mock/fedora-41-x86_64-1740863228.713984/root/var/log/dnf5.log
INFO: chroot_scan: creating tarball /var/lib/copr-rpmbuild/results/chroot_scan.tar.gz
/bin/tar: Removing leading `/' from member names
Finish: buildsrpm
INFO: Done(/var/lib/copr-rpmbuild/workspace/workdir-a8jvyids/python-tapyoca/python-tapyoca.spec) Config(child) 0 minutes 20 seconds
INFO: Results and/or logs in: /var/lib/copr-rpmbuild/results
INFO: Cleaning up build root ('cleanup_on_success=True')
Start: clean chroot
INFO: unmounting tmpfs.
Finish: clean chroot
INFO: Start(/var/lib/copr-rpmbuild/results/python-tapyoca-0.0.4-1.fc41.src.rpm)  Config(fedora-41-x86_64)
Start(bootstrap): chroot init
INFO: mounting tmpfs at /var/lib/mock/fedora-41-x86_64-bootstrap-1740863228.713984/root.
INFO: reusing tmpfs at /var/lib/mock/fedora-41-x86_64-bootstrap-1740863228.713984/root.
INFO: calling preinit hooks
INFO: enabled root cache
INFO: enabled package manager cache
Start(bootstrap): cleaning package manager metadata
Finish(bootstrap): cleaning package manager metadata
Finish(bootstrap): chroot init
Start: chroot init
INFO: mounting tmpfs at /var/lib/mock/fedora-41-x86_64-1740863228.713984/root.
INFO: calling preinit hooks
INFO: enabled root cache
Start: unpacking root cache
Finish: unpacking root cache
INFO: enabled package manager cache
Start: cleaning package manager metadata
Finish: cleaning package manager metadata
INFO: enabled HW Info plugin
INFO: Buildroot is handled by package management downloaded with a bootstrap image:
  rpm-4.20.0-1.fc41.x86_64
  rpm-sequoia-1.7.0-5.fc41.x86_64
  dnf5-5.2.10.0-2.fc41.x86_64
  dnf5-plugins-5.2.10.0-2.fc41.x86_64
Finish: chroot init
Start: build phase for python-tapyoca-0.0.4-1.fc41.src.rpm
Start: build setup for python-tapyoca-0.0.4-1.fc41.src.rpm
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1740787200
Wrote: /builddir/build/SRPMS/python-tapyoca-0.0.4-1.fc41.src.rpm
Updating and loading repositories:
 updates                                100% | 507.4 KiB/s |  28.9 KiB |  00m00s
 fedora                                 100% | 525.6 KiB/s |  30.5 KiB |  00m00s
 Copr repository                        100% |  77.0 KiB/s |   1.5 KiB |  00m00s
Repositories loaded.
Package                 Arch   Version        Repository      Size
Installing:
 python3-devel          x86_64 3.13.2-1.fc41  updates      1.8 MiB
Installing dependencies:
 expat                  x86_64 2.6.4-1.fc41   updates    292.9 KiB
 libb2                  x86_64 0.98.1-12.fc41 fedora      42.2 KiB
 mpdecimal              x86_64 2.5.1-16.fc41  fedora     204.9 KiB
 pyproject-rpm-macros   noarch 1.17.0-1.fc41  updates    114.0 KiB
 python-pip-wheel       noarch 24.2-1.fc41    fedora       1.2 MiB
 python-rpm-macros      noarch 3.13-3.fc41    fedora      22.1 KiB
 python3                x86_64 3.13.2-1.fc41  updates     31.8 KiB
 python3-libs           x86_64 3.13.2-1.fc41  updates     40.4 MiB
 python3-packaging      noarch 24.2-3.fc41    updates    558.3 KiB
 python3-rpm-generators noarch 14-11.fc41     fedora      81.7 KiB
 python3-rpm-macros     noarch 3.13-3.fc41    fedora       6.4 KiB
 tzdata                 noarch 2025a-1.fc41   updates      1.6 MiB

Transaction Summary:
 Installing:        13 packages

Total size of inbound packages is 12 MiB. Need to download 12 MiB.
After this operation, 46 MiB extra will be used (install 46 MiB, remove 0 B).
[ 1/13] libb2-0:0.98.1-12.fc41.x86_64   100% |   3.1 MiB/s |  25.7 KiB |  00m00s
[ 2/13] mpdecimal-0:2.5.1-16.fc41.x86_6 100% |  43.4 MiB/s |  89.0 KiB |  00m00s
[ 3/13] python3-devel-0:3.13.2-1.fc41.x 100% |  26.3 MiB/s | 404.1 KiB |  00m00s
[ 4/13] expat-0:2.6.4-1.fc41.x86_64     100% |  55.9 MiB/s | 114.5 KiB |  00m00s
[ 5/13] python-pip-wheel-0:24.2-1.fc41. 100% | 171.7 MiB/s |   1.2 MiB |  00m00s
[ 6/13] tzdata-0:2025a-1.fc41.noarch    100% | 174.1 MiB/s | 713.3 KiB |  00m00s
[ 7/13] python3-0:3.13.2-1.fc41.x86_64  100% |   9.3 MiB/s |  28.5 KiB |  00m00s
[ 8/13] pyproject-rpm-macros-0:1.17.0-1 100% |  43.7 MiB/s |  44.7 KiB |  00m00s
[ 9/13] python-rpm-macros-0:3.13-3.fc41 100% |   8.6 MiB/s |  17.7 KiB |  00m00s
[10/13] python3-rpm-generators-0:14-11. 100% |  28.6 MiB/s |  29.3 KiB |  00m00s
[11/13] python3-rpm-macros-0:3.13-3.fc4 100% |   6.1 MiB/s |  12.4 KiB |  00m00s
[12/13] python3-libs-0:3.13.2-1.fc41.x8 100% | 246.3 MiB/s |   9.1 MiB |  00m00s
[13/13] python3-packaging-0:24.2-3.fc41 100% |  15.0 MiB/s | 153.8 KiB |  00m00s
--------------------------------------------------------------------------------
[13/13] Total                           100% | 111.3 MiB/s |  11.9 MiB |  00m00s
Running transaction
[ 1/15] Verify package files            100% | 406.0   B/s |  13.0   B |  00m00s
[ 2/15] Prepare transaction             100% | 590.0   B/s |  13.0   B |  00m00s
[ 3/15] Installing python-rpm-macros-0: 100% |   0.0   B/s |  22.8 KiB |  00m00s
[ 4/15] Installing python3-rpm-macros-0 100% |   0.0   B/s |   6.7 KiB |  00m00s
[ 5/15] Installing pyproject-rpm-macros 100% |  37.7 MiB/s | 115.9 KiB |  00m00s
[ 6/15] Installing tzdata-0:2025a-1.fc4 100% |  67.3 MiB/s |   1.9 MiB |  00m00s
[ 7/15] Installing expat-0:2.6.4-1.fc41 100% | 288.1 MiB/s | 295.0 KiB |  00m00s
[ 8/15] Installing python-pip-wheel-0:2 100% | 620.8 MiB/s |   1.2 MiB |  00m00s
[ 9/15] Installing mpdecimal-0:2.5.1-16 100% | 201.2 MiB/s | 206.0 KiB |  00m00s
[10/15] Installing libb2-0:0.98.1-12.fc 100% |  10.6 MiB/s |  43.3 KiB |  00m00s
[11/15] Installing python3-libs-0:3.13. 100% | 367.4 MiB/s |  40.8 MiB |  00m00s
[12/15] Installing python3-0:3.13.2-1.f 100% |  32.7 MiB/s |  33.5 KiB |  00m00s
[13/15] Installing python3-packaging-0: 100% | 278.6 MiB/s | 570.6 KiB |  00m00s
[14/15] Installing python3-rpm-generato 100% |  81.0 MiB/s |  82.9 KiB |  00m00s
[15/15] Installing python3-devel-0:3.13 100% |  82.6 MiB/s |   1.8 MiB |  00m00s
Complete!
Finish: build setup for python-tapyoca-0.0.4-1.fc41.src.rpm
Start: rpmbuild python-tapyoca-0.0.4-1.fc41.src.rpm
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1740787200
Executing(%mkbuilddir): /bin/sh -e /var/tmp/rpm-tmp.Rvlzow
+ umask 022
+ cd /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ test -d /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ /usr/bin/chmod -Rf a+rX,u+w,g-w,o-w /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ /usr/bin/rm -rf /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ /usr/bin/mkdir -p /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ /usr/bin/mkdir -p /builddir/build/BUILD/python-tapyoca-0.0.4-build/SPECPARTS
+ RPM_EC=0
++ jobs -p
+ exit 0
Executing(%prep): /bin/sh -e /var/tmp/rpm-tmp.PtPec6
+ umask 022
+ cd /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ cd /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ rm -rf tapyoca-0.0.4
+ /usr/lib/rpm/rpmuncompress -x /builddir/build/SOURCES/tapyoca-0.0.4.tar.gz
+ STATUS=0
+ '[' 0 -ne 0 ']'
+ cd tapyoca-0.0.4
+ /usr/bin/chmod -Rf a+rX,u+w,g-w,o-w .
+ RPM_EC=0
++ jobs -p
+ exit 0
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.HIzYSo
+ umask 022
+ cd /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ cd tapyoca-0.0.4
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(packaging)'
+ echo 'python3dist(pip) >= 19'
+ '[' -f pyproject.toml ']'
+ '[' -f setup.py ']'
+ echo 'python3dist(setuptools) >= 40.8'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/.pyproject-builddir
+ echo -n
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ VALAFLAGS=-g
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes --cap-lints=warn'
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ CC=gcc
+ CXX=g++
+ TMPDIR=/builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/.pyproject-builddir
+ RPM_TOXENV=py313
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/pyproject-wheeldir --output /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-buildrequires
Handling setuptools >= 40.8 from default build backend
Requirement not satisfied: setuptools >= 40.8
Exiting dependency generation pass: build backend
+ cat /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-buildrequires
+ rm -rfv '*.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Wrote: /builddir/build/SRPMS/python-tapyoca-0.0.4-1.fc41.buildreqs.nosrc.rpm
INFO: Going to install missing dynamic buildrequires
Updating and loading repositories:
 fedora                                 100% | 534.8 KiB/s |  30.5 KiB |  00m00s
 updates                                100% | 160.7 KiB/s |  28.9 KiB |  00m00s
 Copr repository                        100% | 140.0 KiB/s |   1.5 KiB |  00m00s
Repositories loaded.
Package "pyproject-rpm-macros-1.17.0-1.fc41.noarch" is already installed.
Package "python3-devel-3.13.2-1.fc41.x86_64" is already installed.
Package "python3-packaging-24.2-3.fc41.noarch" is already installed.

Package             Arch   Version       Repository      Size
Installing:
 python3-pip        noarch 24.2-1.fc41   fedora      11.4 MiB
 python3-setuptools noarch 69.2.0-8.fc41 fedora       7.2 MiB

Transaction Summary:
 Installing:         2 packages

Total size of inbound packages is 4 MiB. Need to download 4 MiB.
After this operation, 19 MiB extra will be used (install 19 MiB, remove 0 B).
[1/2] python3-setuptools-0:69.2.0-8.fc4 100% |  97.8 MiB/s |   1.6 MiB |  00m00s
[2/2] python3-pip-0:24.2-1.fc41.noarch  100% | 142.9 MiB/s |   2.7 MiB |  00m00s
--------------------------------------------------------------------------------
[2/2] Total                             100% |  21.9 MiB/s |   4.3 MiB |  00m00s
Running transaction
[1/4] Verify package files              100% | 181.0   B/s |   2.0   B |  00m00s
[2/4] Prepare transaction               100% | 133.0   B/s |   2.0   B |  00m00s
[3/4] Installing python3-setuptools-0:6 100% | 244.3 MiB/s |   7.3 MiB |  00m00s
[4/4] Installing python3-pip-0:24.2-1.f 100% | 220.1 MiB/s |  11.7 MiB |  00m00s
Complete!
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1740787200
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.0pfKnq
+ umask 022
+ cd /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ cd tapyoca-0.0.4
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(packaging)'
+ echo 'python3dist(pip) >= 19'
+ '[' -f pyproject.toml ']'
+ '[' -f setup.py ']'
+ echo 'python3dist(setuptools) >= 40.8'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/.pyproject-builddir
+ echo -n
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ VALAFLAGS=-g
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes --cap-lints=warn'
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ CC=gcc
+ CXX=g++
+ TMPDIR=/builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/.pyproject-builddir
+ RPM_TOXENV=py313
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/pyproject-wheeldir --output /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-buildrequires
Handling setuptools >= 40.8 from default build backend
Requirement satisfied: setuptools >= 40.8
   (installed: setuptools 69.2.0)
!!!! containing_folder_name=tapyoca-0.0.4 but setup name is tapyoca
Setup params -------------------------------------------------------
{
  "name": "tapyoca",
  "version": "0.0.4",
  "url": "https://github.com/thorwhalen/tapyoca",
  "packages": [
    "tapyoca",
    "tapyoca.agglutination",
    "tapyoca.covid",
    "tapyoca.darpa",
    "tapyoca.demonyms",
    "tapyoca.indexing_podcasts",
    "tapyoca.parquet_deformations",
    "tapyoca.phoneming"
  ],
  "include_package_data": true,
  "platforms": "any",
  "long_description": "# tapyoca\nA medley of small projects\n\n\n# parquet_deformations\n\nI'm calling these [Parquet deformations](https://www.theguardian.com/artanddesign/alexs-adventures-in-numberland/2014/sep/09/crazy-paving-the-twisted-world-of-parquet-deformations#:~:text=In%20the%201960s%20an%20American,the%20regularity%20of%20the%20tiling.) but purest would lynch me. \n\nReally, I just wanted to transform one word into another word, gradually, as I've seen in some of [Escher's](https://en.wikipedia.org/wiki/M._C._Escher) work, so I looked it up, and saw that it's called parquet deformations. The math looked enticing, but I had no time for that, so I did the first way I could think of: Mapping pixels to pixels (in some fashion -- but nearest neighbors is the method that yields nicest results, under the pixel-level restriction). \n\nOf course, this can be applied to any image (that will be transformed to B/W (not even gray -- I mean actual B/W), and there's several ways you can perform the parquet (I like the gif rendering). \n\nThe main function (exposed as a script) is `mk_deformation_image`. All you need is to specify two images (or words). If you want, of course, you can specify:\n- `n_steps`: Number of steps from start to end image\n- `save_to_file`: path to file to save too (if not given, will just return the image object)\n- `kind`: 'gif', 'horizontal_stack', or 'vertical_stack'\n- `coordinate_mapping_maker`: A function that will return the mapping between start and end. \nThis function should return a pair (`from_coord`, `to_coord`) of aligned matrices whose 2 columns are the the \n`(x, y)` coordinates, and the rows represent aligned positions that should be mapped. \n\n\n\n## Examples\n\n### Two words...\n\n\n```python\nfit_to_size = 400\nstart_im = image_of_text('sensor').rotate(90, expand=1)\nend_im = image_of_text('meaning').rotate(90, expand=1)\nstart_and_end_image(start_im, end_im)\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_5_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 15, kind='h').resize((500,200))\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_6_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im.transpose(4), end_im.transpose(4), 5, kind='v').resize((200,200))\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_7_0.png)\n\n\n\n\n```python\nf = 'sensor_meaning_knn.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_knn.gif?76128495\">\n\n\n\n\n```python\nf = 'sensor_meaning_scan.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f, \n                     coordinate_mapping_maker='scan')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_scan.gif?76996026\">\n\n\n\n\n```python\nf = 'sensor_meaning_random.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f, \n                     coordinate_mapping_maker='random')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_random.gif?80233280\">\n\n\n\n### From a list of words\n\n\n```python\nstart_words = ['sensor', 'vibration', 'tempature']\nend_words = ['sense', 'meaning', 'detection']\nstart_im, end_im = make_start_and_end_images_with_words(\n    start_words, end_words, perm=True, repeat=2, size=150)\nstart_and_end_image(start_im, end_im).resize((600, 200))\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_12_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 5)\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_13_0.png)\n\n\n\n\n```python\nf = 'bunch_of_words.gif'\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"bunch_of_words.gif?7402792\">\n\n\n\n## From files\n\n\n```python\nstart_im = Image.open('sensor_strip_01.png')\nend_im = Image.open('sense_strip_01.png')\nstart_and_end_image(start_im.resize((200, 500)), end_im.resize((200, 500)))\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_16_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 7)\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_17_0.png)\n\n\n\n\n```python\nf = 'medley.gif'\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"medley.gif?39255021\">\n\n\n\n\n```python\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f, coordinate_mapping_maker='scan')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning.gif?41172115\">\n\n\n\n## an image and some text\n\n\n```python\nstart_im = 'img/waveform_01.png'  # will first look for a file, and if not consider as text\nend_im = 'makes sense'\n\nmk_gif_of_deformations(start_im, end_im, n_steps=20, \n                               save_to_file='image_and_text.gif')\ndisplay_gif('image_and_text.gif')  \n```\n\n\n\n\n<img src=\"image_and_text.gif?92524789\">\n\n\n\n\n\n\n# demonys\n\n## What do we think about other peoples?\n\nThis project is meant to get an idea of what people think of people for different nations, as seen by what they ask google about them. \n\nHere I use python code to acquire, clean up, and analyze the data. \n\n### Demonym\n\nIf you're like me and enjoy the false and fleeting impression of superiority that comes when you know a word someone else doesn't. If you're like me and go to parties for the sole purpose of seeking victims to get a one-up on, here's a cool word to add to your arsenal:\n\n**demonym**: a noun used to denote the natives or inhabitants of a particular country, state, city, etc.\n_\"he struggled for the correct demonym for the people of Manchester\"_\n\n### Back-story of this analysis\n \nDuring a discussion (about traveling in Europe) someone said \"why are the swiss so miserable\". Now, I wouldn't say that the swiss were especially miserable (a couple of ex-girlfriends aside), but to be fair he was contrasting with Italians, so perhaps he has a point. I apologize if you are swiss, or one of the two ex-girlfriends -- nothing personal, this is all for effect. \n\nWe googled \"why are the swiss so \", and sure enough, \"why are the swiss so miserable\" came up as one of the suggestions. So we got curious and started googling other peoples: the French, the Germans, etc.\n\nThat's the back-story of this analysis. This analysis is meant to get an idea of what we think of peoples from other countries. Of course, one can rightfully critique the approach I'll take to gauge \"what we think\" -- all three of these words should, but will not, be defined. I'm just going to see what google's *current* auto-suggest comes back with when I enter \"why are the X so \" (where X will be a noun that denotes the natives of inhabitants of a particular country; a *demonym* if you will). \n\n### Warning\n\nAgain, word of warning: All data and analyses are biased. \nTake everything you'll read here (and to be fair, what you read anywhere) with a grain of salt. \nFor simplicitly I'll saying things like \"what we think of...\" or \"who do we most...\", etc.\nBut I don't **really** mean that.\n\n### Resources\n\n* http://www.geography-site.co.uk/pages/countries/demonyms.html for my list of demonyms.\n* google for my suggestion engine, using the url prefix: `http://suggestqueries.google.com/complete/search?client=chrome&q=`\n\n\n## The results\n\n### In a nutshell\n\nBelow is listed 73 demonyms along with words extracted from the very first google suggestion when you type. \n\n`why are the DEMONYM so `\n\n```text\nafghan    \t                eyes beautiful\nalbanian  \t                     beautiful\namerican  \t          girl dolls expensive\naustralian\t                          tall\nbelgian   \t                    fries good\nbhutanese \t                         happy\nbrazilian \t              good at football\nbritish   \t     full of grief and despair\nbulgarian \t              properties cheap\nburmese   \t             cats affectionate\ncambodian \t                   cows skinny\ncanadian  \t                          nice\nchinese   \t                       healthy\ncolombian \t                  avocados big\ncuban     \t                   cigars good\nczech     \t                          tall\ndominican \t  republic and haiti different\negyptian  \t                gods important\nenglish   \t                      reserved\neritrean  \t                     beautiful\nethiopian \t                     beautiful\nfilipino  \t                         proud\nfinn      \t               shoes expensive\nfrench    \t                       healthy\ngerman    \t                          tall\ngreek     \t                gods messed up\nhaitian   \t                parents strict\nhungarian \t                    words long\nindian    \t            tv debates chaotic\nindonesian\t                         smart\niranian   \t                     beautiful\nisraeli   \t           startups successful\nitalian   \t                         short\njamaican  \t                sprinters fast\njapanese  \t                        polite\nkenyan    \t                  runners good\nlebanese  \t                          rich\nmalagasy  \t                    names long\nmalaysian \t                   drivers bad\nmaltese   \t                          rude\nmongolian \t                  horses small\nmoroccan  \t                rugs expensive\nnepalese  \t                     beautiful\nnigerian  \t                          tall\nnorth korean\t                      hats big\nnorwegian \t                 flights cheap\npakistani \t                          fair\nperuvian  \t               blueberries big\npole      \t                  vaulters hot\nportuguese\t                         short\npuerto rican\t       and cuban flags similar\nromanian  \t                     beautiful\nrussian   \t                  good at math\nsamoan    \t                           big\nsaudi     \t                      arrogant\nscottish  \t                        bitter\nsenegalese\t                          tall\nserbian   \t                          tall\nsingaporean\t                          rude\nsomali    \t                parents strict\nsouth african\t                     plugs big\nsouth korean\t                          tall\nsri lankan\t                          dark\nsudanese  \t                          tall\nswiss     \t        good at making watches\nsyrian    \t                families large\ntaiwanese \t                        pretty\nthai      \t                        pretty\ntongan    \t                           big\nukrainian \t                     beautiful\nvietnamese\t        fiercely nationalistic\nwelsh     \t                          dark\nzambian   \t                emeralds cheap\n```\n\n\nNotes:\n* The queries actually have a space after the \"so\", which matters so as to omit suggestions containing words that start with so.\n* Only the tail of the suggestion is shown -- minus prefix (`why are the DEMONYM` or `why are DEMONYM`) as well as the `so`, where ever it lands in the suggestion. \nFor example, the first suggestion for the american demonym was \"why are american dolls so expensive\", which results in the \"dolls expensive\" association. \n\n\n### Who do we most talk/ask about?\n\nThe original list contained 217 demonyms, but many of these yielded no suggestions (to the specific query format I used, that is). \nOnly 73 demonyms gave me at least one suggestion. \nBut within those, number of suggestions range between 1 and 20 (which is probably the default maximum number of suggestions for the API I used). \nSo, pretending that the number of suggestions is an indicator of how much we have to say, or how many different opinions we have, of each of the covered nationalities, \nhere's the top 15 demonyms people talk about, with the corresponding number of suggestions \n(proxy for \"the number of different things people ask about the said nationality). \n\n```text\nfrench         20\nsingaporean    20\ngerman         20\nbritish        20\nswiss          20\nenglish        19\nitalian        18\ncuban          18\ncanadian       18\nwelsh          18\naustralian     17\nmaltese        16\namerican       16\njapanese       14\nscottish       14\n```\n\n### Who do we least talk/ask about?\n\nConversely, here are the 19 demonyms that came back with only one suggestion.\n\n```text\nsomali          1\nbhutanese       1\nsyrian          1\ntongan          1\ncambodian       1\nmalagasy        1\nsaudi           1\nserbian         1\nczech           1\neritrean        1\nfinn            1\npuerto rican    1\npole            1\nhaitian         1\nhungarian       1\nperuvian        1\nmoroccan        1\nmongolian       1\nzambian         1\n```\n\n### What do we think about people?\n\nWhy are the French so...\n\nHow would you (if you're (un)lucky enough to know the French) finish this sentence?\nYou might even have several opinions about the French, and any other group of people you've rubbed shoulders with.\nWhat words would your palette contain to describe different nationalities?\nWhat words would others (at least those that ask questions to google) use?\n\nWell, here's what my auto-suggest search gave me. A set of 357 unique words and expressions to describe the 72 nationalities. \nSo a long tail of words use only for one nationality. But some words occur for more than one nationality. \nHere are the top 12 words/expressions used to describe people of the world. \n\n```text\nbeautiful         11\ntall              11\nshort              9\nnames long         8\nproud              8\nparents strict     8\nsmart              8\nnice               7\nboring             6\nrich               5\ndark               5\nsuccessful         5\n```\n\n### Who is beautiful? Who is tall? Who is short? Who is smart?\n\n```text\nbeautiful      : albanian, eritrean, ethiopian, filipino, iranian, lebanese, nepalese, pakistani, romanian, ukrainian, vietnamese\ntall           : australian, czech, german, nigerian, pakistani, samoan, senegalese, serbian, south korean, sudanese, taiwanese\nshort          : filipino, indonesian, italian, maltese, nepalese, pakistani, portuguese, singaporean, welsh\nnames long     : indian, malagasy, nigerian, portuguese, russian, sri lankan, thai, welsh\nproud          : albanian, ethiopian, filipino, iranian, lebanese, portuguese, scottish, welsh\nparents strict : albanian, ethiopian, haitian, indian, lebanese, pakistani, somali, sri lankan\nsmart          : indonesian, iranian, lebanese, pakistani, romanian, singaporean, taiwanese, vietnamese\nnice           : canadian, english, filipino, nepalese, portuguese, taiwanese, thai\nboring         : british, english, french, german, singaporean, swiss\nrich           : lebanese, pakistani, singaporean, taiwanese, vietnamese\ndark           : filipino, senegalese, sri lankan, vietnamese, welsh\nsuccessful     : chinese, english, japanese, lebanese, swiss\n```\n\n## How did I do it?\n\nI scraped a list of (country, demonym) pairs from a table in http://www.geography-site.co.uk/pages/countries/demonyms.html.\n\nThen I diagnosed these and manually made a mapping to simplify some \"complex\" entries, \nsuch as mapping an entry such as \"Irishman or Irishwoman or Irish\" to \"Irish\".\n\nUsing the google suggest API (http://suggestqueries.google.com/complete/search?client=chrome&q=), I requested what the suggestions \nfor `why are the $demonym so ` query pattern, for `$demonym` running through all 217 demonyms from the list above, \nstoring the results for each if the results were non-empty. \n\nThen, it was just a matter of pulling this data into memory, formatting it a bit, and creating a pandas dataframe that I could then interrogate.\n \n## Resources you can find here\n\nThe code to do this analysis yourself, from scratch here: `data_acquisition.py`.\n\nThe jupyter notebook I actually used when I developed this: `01 - Demonyms and adjectives - why are the french so....ipynb`\n \nNote you'll need to pip install py2store if you haven't already.\n\nIn the `data` folder you'll find\n* country_demonym.p: A pickle of a dataframe of countries and corresponding demonyms\n* country_demonym.xlsx: The same as above, but in excel form\n* demonym_suggested_characteristics.p: A pickle of 73 demonyms and auto-suggestion information, including characteristics. \n* what_we_think_about_demonyns.xlsx: An excel containing various statistics about demonyms and their (perceived) characteristics\n \n\n\n\n\n\n# Agglutinations\n\nInspired from a [tweet](https://twitter.com/raymondh/status/1311003482531401729) from Raymond Hettinger this morning:\n\n_Resist the urge to elide the underscore in multiword function or method names_\n\nSo I wondered...\n\n## Gluglus\n\nThe gluglu of a word is the number of partitions you can make of that word into words (of length at least 2 (so no using a or i)).\n(No \"gluglu\" isn't an actual term -- unless everyone starts using it from now on. \nBut it was inspired from an actual [linguistic term](https://en.wikipedia.org/wiki/Agglutination).)\n\nFor example, the gluglu of ``newspaper`` is 4:\n\n```\nnewspaper\n    new spa per\n    news pa per\n    news paper\n```\n\nEvery (valid) word has gluglu at least 1.\n\n\n## How many standard library names have gluglus at last 2?\n\n108\n\nHere's [the list](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/standard_lib_gluglus.txt) of all of them.\n\nThe winner has a gluglu of 6 (not 7 because formatannotationrelativeto isn't in the dictionary)\n\n```\nformatannotationrelativeto\n\tfor mat an not at ion relative to\n\tfor mat annotation relative to\n\tform at an not at ion relative to\n\tform at annotation relative to\n\tformat an not at ion relative to\n\tformat annotation relative to\n```\n\n## Details\n\n### Dictionary\n\nReally it depends on what dictionary we use. \nHere, I used a very conservative one. \nThe intersection of two lists: The [corncob](http://www.mieliestronk.com/corncob_lowercase.txt) \nand the [google10000](https://raw.githubusercontent.com/first20hours/google-10000-english/master/google-10000-english-usa.txt) word lists.\nAdditionally, I only kept of those, those that had at least 2 letters, and had only letters (no hyphens or disturbing diacritics).\n\nDiacritics. Look it up. Impress your next nerd date.\n\nIm left with 8116 words. You can find them [here](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/words_8116.csv).\n\n### Standard Lib Names\n\nSurprisingly, that was the hardest part. I know I'm missing some, but that's enough rabbit-holing. \n\nWhat I did (modulo some exceptions I won't look into) was to walk the standard lib modules (even that list wasn't a given!) \nextracting (recursively( the names of any (non-underscored) attributes if they were modules or callables, \nas well as extracting the arguments of these callables (when they had signatures).\n\nYou can find the code I used to extract these names [here](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/py_names.py) \nand the actual list [there](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/standard_lib_module_names.csv).\n\n\n\n# covid\n\n## Bar Chart Races (applied to covid-19 spread)\n\nThe module will show is how to make these:\n- Confirmed cases (by country): https://public.flourish.studio/visualisation/1704821/\n- Deaths (by country): https://public.flourish.studio/visualisation/1705644/\n- US Confirmed cases (by state): https://public.flourish.studio/visualisation/1794768/\n- US Deaths (by state): https://public.flourish.studio/visualisation/1794797/\n\n### The script\n\nIf you just want to run this as a script to get the job done, you have one here: \nhttps://raw.githubusercontent.com/thorwhalen/tapyoca/master/covid/covid_bar_chart_race.py\n\nRun like this\n```\n$ python covid_bar_chart_race.py -h\nusage: covid_bar_chart_race.py [-h] {mk-and-save-covid-data,update-covid-data,instructions-to-make-bar-chart-race} ...\n\npositional arguments:\n  {mk-and-save-covid-data,update-covid-data,instructions-to-make-bar-chart-race}\n    mk-and-save-covid-data\n                        :param data_sources: Dirpath or py2store Store where the data is :param kinds: The kinds of data you want to compute and save :param\n                        skip_first_days: :param verbose: :return:\n    update-covid-data   update the coronavirus data\n    instructions-to-make-bar-chart-race\n\noptional arguments:\n  -h, --help            show this help message and exit\n ```\n \n \n### The jupyter notebook\n\nThe notebook (the .ipynb file) shows you how to do it step by step in case you want to reuse the methods for other stuff.\n\n\n\n## Getting and preparing the data\n\nCorona virus data here: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset (direct download: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset/download). It's currently updated daily, so download a fresh copy if you want.\n\nPopulation data here: http://api.worldbank.org/v2/en/indicator/SP.POP.TOTL?downloadformat=csv\n\nIt comes under the form of a zip file (currently named `novel-corona-virus-2019-dataset.zip` with several `.csv` files in them. We use `py2store` (To install: `pip install py2store`. Project lives here: https://github.com/i2mint/py2store) to access and pre-prepare it. It allows us to not have to unzip the file and replace the older folder with it every time we download a new one. It also gives us the csvs as `pandas.DataFrame` already. \n\n\n```python\nimport pandas as pd\nfrom io import BytesIO\nfrom py2store import kv_wrap, ZipReader  # google it and pip install it\nfrom py2store.caching import mk_cached_store\nfrom py2store import QuickPickleStore\nfrom py2store.sources import FuncReader\n\ndef country_flag_image_url():\n    import pandas as pd\n    return pd.read_csv(\n        'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv')\n\ndef kaggle_coronavirus_dataset():\n    import kaggle\n    from io import BytesIO\n    # didn't find the pure binary download function, so using temp dir to emulate\n    from tempfile import mkdtemp  \n    download_dir = mkdtemp()\n    filename = 'novel-corona-virus-2019-dataset.zip'\n    zip_file = os.path.join(download_dir, filename)\n    \n    dataset = 'sudalairajkumar/novel-corona-virus-2019-dataset'\n    kaggle.api.dataset_download_files(dataset, download_dir)\n    with open(zip_file, 'rb') as fp:\n        b = fp.read()\n    return BytesIO(b)\n\ndef city_population_in_time():\n    import pandas as pd\n    return pd.read_csv(\n        'https://gist.githubusercontent.com/johnburnmurdoch/'\n        '4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations'\n    )\n\ndef country_flag_image_url_prep(df: pd.DataFrame):\n    # delete the region col (we don't need it)\n    del df['region']\n    # rewriting a few (not all) of the country names to match those found in kaggle covid data\n    # Note: The list is not complete! Add to it as needed\n    old_and_new = [('USA', 'US'), \n                   ('Iran, Islamic Rep.', 'Iran'), \n                   ('UK', 'United Kingdom'), \n                   ('Korea, Rep.', 'Korea, South')]\n    for old, new in old_and_new:\n        df['country'] = df['country'].replace(old, new)\n\n    return df\n\n\n@kv_wrap.outcoming_vals(lambda x: pd.read_csv(BytesIO(x)))  # this is to format the data as a dataframe\nclass ZippedCsvs(ZipReader):\n    pass\n# equivalent to ZippedCsvs = kv_wrap.outcoming_vals(lambda x: pd.read_csv(BytesIO(x)))(ZipReader)\n```\n\n\n```python\n# Enter here the place you want to cache your data\nmy_local_cache = os.path.expanduser('~/ddir/my_sources')\n```\n\n\n```python\nCachedFuncReader = mk_cached_store(FuncReader, QuickPickleStore(my_local_cache))\n```\n\n\n```python\ndata_sources = CachedFuncReader([country_flag_image_url, \n                                 kaggle_coronavirus_dataset, \n                                 city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ncovid_datasets = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\nlist(covid_datasets)\n```\n\n\n\n\n    ['COVID19_line_list_data.csv',\n     'COVID19_open_line_list.csv',\n     'covid_19_data.csv',\n     'time_series_covid_19_confirmed.csv',\n     'time_series_covid_19_confirmed_US.csv',\n     'time_series_covid_19_deaths.csv',\n     'time_series_covid_19_deaths_US.csv',\n     'time_series_covid_19_recovered.csv']\n\n\n\n\n```python\ncovid_datasets['time_series_covid_19_confirmed.csv'].head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Province/State</th>\n      <th>Country/Region</th>\n      <th>Lat</th>\n      <th>Long</th>\n      <th>1/22/20</th>\n      <th>1/23/20</th>\n      <th>1/24/20</th>\n      <th>1/25/20</th>\n      <th>1/26/20</th>\n      <th>1/27/20</th>\n      <th>...</th>\n      <th>3/24/20</th>\n      <th>3/25/20</th>\n      <th>3/26/20</th>\n      <th>3/27/20</th>\n      <th>3/28/20</th>\n      <th>3/29/20</th>\n      <th>3/30/20</th>\n      <th>3/31/20</th>\n      <th>4/1/20</th>\n      <th>4/2/20</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>Afghanistan</td>\n      <td>33.0000</td>\n      <td>65.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>74</td>\n      <td>84</td>\n      <td>94</td>\n      <td>110</td>\n      <td>110</td>\n      <td>120</td>\n      <td>170</td>\n      <td>174</td>\n      <td>237</td>\n      <td>273</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>Albania</td>\n      <td>41.1533</td>\n      <td>20.1683</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>123</td>\n      <td>146</td>\n      <td>174</td>\n      <td>186</td>\n      <td>197</td>\n      <td>212</td>\n      <td>223</td>\n      <td>243</td>\n      <td>259</td>\n      <td>277</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>Algeria</td>\n      <td>28.0339</td>\n      <td>1.6596</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>264</td>\n      <td>302</td>\n      <td>367</td>\n      <td>409</td>\n      <td>454</td>\n      <td>511</td>\n      <td>584</td>\n      <td>716</td>\n      <td>847</td>\n      <td>986</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>Andorra</td>\n      <td>42.5063</td>\n      <td>1.5218</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>164</td>\n      <td>188</td>\n      <td>224</td>\n      <td>267</td>\n      <td>308</td>\n      <td>334</td>\n      <td>370</td>\n      <td>376</td>\n      <td>390</td>\n      <td>428</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>Angola</td>\n      <td>-11.2027</td>\n      <td>17.8739</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>5</td>\n      <td>7</td>\n      <td>7</td>\n      <td>7</td>\n      <td>8</td>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 76 columns</p>\n</div>\n\n\n\n\n```python\ncountry_flag_image_url = data_sources['country_flag_image_url']\ncountry_flag_image_url.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nfrom IPython.display import Image\nflag_image_url_of_country = country_flag_image_url.set_index('country')['flag_image_url']\nImage(url=flag_image_url_of_country['Australia'])\n```\n\n\n\n\n<img src=\"https://www.countryflags.io/au/flat/64.png\"/>\n\n\n\n### Update coronavirus data\n\n\n```python\n# To update the coronavirus data:\ndef update_covid_data(data_sources):\n    \"\"\"update the coronavirus data\"\"\"\n    if 'kaggle_coronavirus_dataset' in data_sources._caching_store:\n        del data_sources._caching_store['kaggle_coronavirus_dataset']  # delete the cached item\n    _ = data_sources['kaggle_coronavirus_dataset']\n\n# update_covid_data(data_sources)  # uncomment here when you want to update\n```\n\n### Prepare data for flourish upload\n\n\n```python\nimport re\n\ndef print_if_verbose(verbose, *args, **kwargs):\n    if verbose:\n        print(*args, **kwargs)\n        \ndef country_data_for_data_kind(data_sources, kind='confirmed', skip_first_days=0, verbose=False):\n    \"\"\"kind can be 'confirmed', 'deaths', 'confirmed_US', 'confirmed_US', 'recovered'\"\"\"\n    \n    covid_datasets = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\n    \n    df = covid_datasets[f'time_series_covid_19_{kind}.csv']\n    # df = s['time_series_covid_19_deaths.csv']\n    if 'Province/State' in df.columns:\n        df.loc[df['Province/State'].isna(), 'Province/State'] = 'n/a'  # to avoid problems arising from NaNs\n\n    print_if_verbose(verbose, f\"Before data shape: {df.shape}\")\n\n    # drop some columns we don't need\n    p = re.compile('\\d+/\\d+/\\d+')\n\n    assert all(isinstance(x, str) for x in df.columns)\n    date_cols = [x for x in df.columns if p.match(x)]\n    if not kind.endswith('US'):\n        df = df.loc[:, ['Country/Region'] + date_cols]\n        # group countries and sum up the contributions of their states/regions/pargs\n        df['country'] = df.pop('Country/Region')\n        df = df.groupby('country').sum()\n    else:\n        df = df.loc[:, ['Province_State'] + date_cols]\n        df['state'] = df.pop('Province_State')\n        df = df.groupby('state').sum()\n\n    \n    print_if_verbose(verbose, f\"After data shape: {df.shape}\")\n    df = df.iloc[:, skip_first_days:]\n    \n    if not kind.endswith('US'):\n        # Joining with the country image urls and saving as an xls\n        country_image_url = country_flag_image_url_prep(data_sources['country_flag_image_url'])\n        t = df.copy()\n        t.columns = [str(x)[:10] for x in t.columns]\n        t = t.reset_index(drop=False)\n        t = country_image_url.merge(t, how='outer')\n        t = t.set_index('country')\n        df = t\n    else:    \n        pass\n\n    return df\n\n\ndef mk_and_save_country_data_for_data_kind(data_sources, kind='confirmed', skip_first_days=0, verbose=False):\n    t = country_data_for_data_kind(data_sources, kind, skip_first_days, verbose)\n    filepath = f'country_covid_{kind}.xlsx'\n    t.to_excel(filepath)\n    print_if_verbose(verbose, f\"Was saved here: {filepath}\")\n\n```\n\n\n```python\n# for kind in ['confirmed', 'deaths', 'recovered', 'confirmed_US', 'deaths_US']:\nfor kind in ['confirmed', 'deaths', 'recovered', 'confirmed_US', 'deaths_US']:\n    mk_and_save_country_data_for_data_kind(data_sources, kind=kind, skip_first_days=39, verbose=True)\n```\n\n    Before data shape: (262, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_confirmed.xlsx\n    Before data shape: (262, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_deaths.xlsx\n    Before data shape: (248, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_recovered.xlsx\n    Before data shape: (3253, 86)\n    After data shape: (58, 75)\n    Was saved here: country_covid_confirmed_US.xlsx\n    Before data shape: (3253, 87)\n    After data shape: (58, 75)\n    Was saved here: country_covid_deaths_US.xlsx\n\n\n### Upload to Flourish, tune, and publish\n\nGo to https://public.flourish.studio/, get a free account, and play.\n\nGot to https://app.flourish.studio/templates\n\nChoose \"Bar chart race\". At the time of writing this, it was here: https://app.flourish.studio/visualisation/1706060/\n\n... and then play with the settings\n\n\n## Discussion of the methods\n\n\n```python\nfrom py2store import *\nfrom IPython.display import Image\n```\n\n### country flags images\n\nThe manual data prep looks something like this.\n\n\n```python\nimport pandas as pd\n\n# get the csv data from the url\ncountry_image_url_source = \\\n    'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv'\ncountry_image_url = pd.read_csv(country_image_url_source)\n\n# delete the region col (we don't need it)\ndel country_image_url['region']\n\n# rewriting a few (not all) of the country names to match those found in kaggle covid data\n# Note: The list is not complete! Add to it as needed\n# TODO: (Wishful) Using a general smart soft-matching algorithm to do this automatically.\n# TODO:    This could use edit-distance, synonyms, acronym generation, etc.\nold_and_new = [('USA', 'US'), \n               ('Iran, Islamic Rep.', 'Iran'), \n               ('UK', 'United Kingdom'), \n               ('Korea, Rep.', 'Korea, South')]\nfor old, new in old_and_new:\n    country_image_url['country'] = country_image_url['country'].replace(old, new)\n\nimage_url_of_country = country_image_url.set_index('country')['flag_image_url']\n\ncountry_image_url.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nImage(url=image_url_of_country['Australia'])\n```\n\n\n\n\n<img src=\"https://www.countryflags.io/au/flat/64.png\"/>\n\n\n\n### Caching the flag images data\n\nDownloading our data sources every time we need them is not sustainable. What if they're big? What if you're offline or have slow internet (yes, dear future reader, even in the US, during coronavirus times!)?\n\nCaching. A \"cache aside\" read-cache. That's the word. py2store has tools for that (most of which are are caching.py). \n\nSo let's say we're going to have a local folder where we'll store various datas we download. The principle is as follows:\n\n\n```python\nfrom py2store.caching import mk_cached_store\n\nclass TheSource(dict): ...\nthe_cache = {}\nTheCacheSource = mk_cached_store(TheSource, the_cache)\n\nthe_source = TheSource({'green': 'eggs', 'and': 'ham'})\n\nthe_cached_source = TheCacheSource(the_source)\nprint(f\"the_cache: {the_cache}\")\nprint(f\"Getting green...\")\nthe_cached_source['green']\nprint(f\"the_cache: {the_cache}\")\nprint(\"... so the next time the_cached_source will get it's green from that the_cache\")\n```\n\n    the_cache: {}\n    Getting green...\n    the_cache: {'green': 'eggs'}\n    ... so the next time the_cached_source will get it's green from that the_cache\n\n\nBut now, you'll notice a slight problem ahead. What exactly does our source store (or rather reader) looks like? In it's raw form it would take urls as it's keys, and the response of a request as it's value. That store wouldn't have an `__iter__` for sure (unless you're Google). But more to the point here, the `mk_cached_store` tool uses the same key for the source and the cache, and we can't just use the url as is, to be a local file path. \n\nThere's many ways we could solve this. One way is to add a key map layer on the cache store, so externally, it speaks the url key language, but internally it will map that url to a valid local file path. We've been there, we got the T-shirt!\n\nBut what we're going to do is a bit different: We're going to do the key mapping in the source store itself. It seems to make more sense in our context: We have a data source of `name: data` pairs, and if we impose that the name should be a valid file name, we don't need to have a key map in the cache store.\n\nSo let's start by building this `MyDataStore` store. We'll start by defining the functions that get us the data we want. \n\n\n```python\ndef country_flag_image_url():\n    import pandas as pd\n    return pd.read_csv(\n        'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv')\n\ndef kaggle_coronavirus_dataset():\n    import kaggle\n    from io import BytesIO\n    # didn't find the pure binary download function, so using temp dir to emulate\n    from tempfile import mkdtemp  \n    download_dir = mkdtemp()\n    filename = 'novel-corona-virus-2019-dataset.zip'\n    zip_file = os.path.join(download_dir, filename)\n    \n    dataset = 'sudalairajkumar/novel-corona-virus-2019-dataset'\n    kaggle.api.dataset_download_files(dataset, download_dir)\n    with open(zip_file, 'rb') as fp:\n        b = fp.read()\n    return BytesIO(b)\n\ndef city_population_in_time():\n    import pandas as pd\n    return pd.read_csv(\n        'https://gist.githubusercontent.com/johnburnmurdoch/'\n        '4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations'\n    )\n```\n\nNow we can make a store that simply uses these function names as the keys, and their returned value as the values.\n\n\n```python\nfrom py2store.base import KvReader\nfrom functools import lru_cache\n\nclass FuncReader(KvReader):\n    _getitem_cache_size = 999\n    def __init__(self, funcs):\n        # TODO: assert no free arguments (arguments are allowed but must all have defaults)\n        self.funcs = funcs\n        self._func_of_name = {func.__name__: func for func in funcs}\n\n    def __contains__(self, k):\n        return k in self._func_of_name\n    \n    def __iter__(self):\n        yield from self._func_of_name\n        \n    def __len__(self):\n        return len(self._func_of_name)\n\n    @lru_cache(maxsize=_getitem_cache_size)\n    def __getitem__(self, k):\n        return self._func_of_name[k]()  # call the func\n    \n    def __hash__(self):\n        return 1\n    \n```\n\n\n```python\ndata_sources = FuncReader([country_flag_image_url, kaggle_coronavirus_dataset, city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['city_population_in_time']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>group</th>\n      <th>year</th>\n      <th>value</th>\n      <th>subGroup</th>\n      <th>city_id</th>\n      <th>lastValue</th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1575</td>\n      <td>200.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1576</td>\n      <td>212.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1577</td>\n      <td>224.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>212.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1578</td>\n      <td>236.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>224.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1579</td>\n      <td>248.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>236.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6247</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1561</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6248</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1562</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6249</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1563</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6250</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1564</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6251</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1565</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n  </tbody>\n</table>\n<p>6252 rows \u00d7 9 columns</p>\n</div>\n\n\n\nBut we wanted this all to be cached locally, right? So a few more lines to do that!\n\n\n```python\nfrom py2store.caching import mk_cached_store\nfrom py2store import QuickPickleStore\n    \nmy_local_cache = os.path.expanduser('~/ddir/my_sources')\n\nCachedFuncReader = mk_cached_store(FuncReader, QuickPickleStore(my_local_cache))\n```\n\n\n```python\ndata_sources = CachedFuncReader([country_flag_image_url, kaggle_coronavirus_dataset, city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['city_population_in_time']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>group</th>\n      <th>year</th>\n      <th>value</th>\n      <th>subGroup</th>\n      <th>city_id</th>\n      <th>lastValue</th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1575</td>\n      <td>200.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1576</td>\n      <td>212.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1577</td>\n      <td>224.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>212.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1578</td>\n      <td>236.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>224.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1579</td>\n      <td>248.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>236.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6247</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1561</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6248</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1562</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6249</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1563</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6250</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1564</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6251</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1565</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n  </tbody>\n</table>\n<p>6252 rows \u00d7 9 columns</p>\n</div>\n\n\n\n\n```python\nz = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\nlist(z)\n```\n",
  "long_description_content_type": "text/markdown",
  "description_file": "README.md",
  "root_url": "https://github.com/thorwhalen",
  "description": "A medley of things that got coded because there was an itch to do so",
  "author": "thorwhalen",
  "license": "Apache Software License",
  "description-file": "README.md",
  "install_requires": [],
  "keywords": [
    "documentation",
    "packaging",
    "publishing"
  ]
}/usr/lib/python3.13/site-packages/setuptools/dist.py:476: SetuptoolsDeprecationWarning: Invalid dash-separated options
!!

        ********************************************************************************
        Usage of dash-separated 'description-file' will not be supported in future
        versions. Please use the underscore name 'description_file' instead.

        This deprecation is overdue, please update your project and remove deprecated
        calls to avoid build errors in the future.

        See https://setuptools.pypa.io/en/latest/userguide/declarative_config.html for details.
        ********************************************************************************

!!
  opt = self.warn_dash_deprecation(opt, section)
/usr/lib/python3.13/site-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'description_file'
  warnings.warn(msg)
/usr/lib/python3.13/site-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'root_url'
  warnings.warn(msg)
/usr/lib/python3.13/site-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'description-file'
  warnings.warn(msg)

--------------------------------------------------------------------
running egg_info
writing tapyoca.egg-info/PKG-INFO
writing dependency_links to tapyoca.egg-info/dependency_links.txt
writing top-level names to tapyoca.egg-info/top_level.txt
reading manifest file 'tapyoca.egg-info/SOURCES.txt'
adding license file 'LICENSE'
writing manifest file 'tapyoca.egg-info/SOURCES.txt'
Handling wheel from get_requires_for_build_wheel
Requirement not satisfied: wheel
Exiting dependency generation pass: get_requires_for_build_wheel
+ cat /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-buildrequires
+ rm -rfv '*.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Wrote: /builddir/build/SRPMS/python-tapyoca-0.0.4-1.fc41.buildreqs.nosrc.rpm
INFO: Going to install missing dynamic buildrequires
Updating and loading repositories:
 fedora                                 100% |   1.0 MiB/s |  30.5 KiB |  00m00s
 updates                                100% | 932.9 KiB/s |  28.9 KiB |  00m00s
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Repositories loaded.
Package "pyproject-rpm-macros-1.17.0-1.fc41.noarch" is already installed.
Package "python3-devel-3.13.2-1.fc41.x86_64" is already installed.
Package "python3-packaging-24.2-3.fc41.noarch" is already installed.
Package "python3-pip-24.2-1.fc41.noarch" is already installed.
Package "python3-setuptools-69.2.0-8.fc41.noarch" is already installed.

Total size of inbound packages is 166 KiB. Need to download 166 KiB.
After this operation, 516 KiB extra will be used (install 516 KiB, remove 0 B).
Package        Arch   Version         Repository      Size
Installing:
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[3/3] Installing python3-wheel-1:0.43.0 100% |  52.3 MiB/s | 535.1 KiB |  00m00s
Complete!
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1740787200
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.YqfYbu
+ umask 022
+ cd /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ cd tapyoca-0.0.4
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(packaging)'
+ echo 'python3dist(pip) >= 19'
+ '[' -f pyproject.toml ']'
+ '[' -f setup.py ']'
+ echo 'python3dist(setuptools) >= 40.8'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/.pyproject-builddir
+ echo -n
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ VALAFLAGS=-g
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes --cap-lints=warn'
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ CC=gcc
+ CXX=g++
+ TMPDIR=/builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/.pyproject-builddir
+ RPM_TOXENV=py313
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/pyproject-wheeldir --output /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-buildrequires
Handling setuptools >= 40.8 from default build backend
Requirement satisfied: setuptools >= 40.8
   (installed: setuptools 69.2.0)
!!!! containing_folder_name=tapyoca-0.0.4 but setup name is tapyoca
Setup params -------------------------------------------------------
{
  "name": "tapyoca",
  "version": "0.0.4",
  "url": "https://github.com/thorwhalen/tapyoca",
  "packages": [
    "tapyoca",
    "tapyoca.agglutination",
    "tapyoca.covid",
    "tapyoca.darpa",
    "tapyoca.demonyms",
    "tapyoca.indexing_podcasts",
    "tapyoca.parquet_deformations",
    "tapyoca.phoneming"
  ],
  "include_package_data": true,
  "platforms": "any",
  "long_description": "# tapyoca\nA medley of small projects\n\n\n# parquet_deformations\n\nI'm calling these [Parquet deformations](https://www.theguardian.com/artanddesign/alexs-adventures-in-numberland/2014/sep/09/crazy-paving-the-twisted-world-of-parquet-deformations#:~:text=In%20the%201960s%20an%20American,the%20regularity%20of%20the%20tiling.) but purest would lynch me. \n\nReally, I just wanted to transform one word into another word, gradually, as I've seen in some of [Escher's](https://en.wikipedia.org/wiki/M._C._Escher) work, so I looked it up, and saw that it's called parquet deformations. The math looked enticing, but I had no time for that, so I did the first way I could think of: Mapping pixels to pixels (in some fashion -- but nearest neighbors is the method that yields nicest results, under the pixel-level restriction). \n\nOf course, this can be applied to any image (that will be transformed to B/W (not even gray -- I mean actual B/W), and there's several ways you can perform the parquet (I like the gif rendering). \n\nThe main function (exposed as a script) is `mk_deformation_image`. All you need is to specify two images (or words). If you want, of course, you can specify:\n- `n_steps`: Number of steps from start to end image\n- `save_to_file`: path to file to save too (if not given, will just return the image object)\n- `kind`: 'gif', 'horizontal_stack', or 'vertical_stack'\n- `coordinate_mapping_maker`: A function that will return the mapping between start and end. \nThis function should return a pair (`from_coord`, `to_coord`) of aligned matrices whose 2 columns are the the \n`(x, y)` coordinates, and the rows represent aligned positions that should be mapped. \n\n\n\n## Examples\n\n### Two words...\n\n\n```python\nfit_to_size = 400\nstart_im = image_of_text('sensor').rotate(90, expand=1)\nend_im = image_of_text('meaning').rotate(90, expand=1)\nstart_and_end_image(start_im, end_im)\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_5_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 15, kind='h').resize((500,200))\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_6_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im.transpose(4), end_im.transpose(4), 5, kind='v').resize((200,200))\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_7_0.png)\n\n\n\n\n```python\nf = 'sensor_meaning_knn.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_knn.gif?76128495\">\n\n\n\n\n```python\nf = 'sensor_meaning_scan.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f, \n                     coordinate_mapping_maker='scan')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_scan.gif?76996026\">\n\n\n\n\n```python\nf = 'sensor_meaning_random.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f, \n                     coordinate_mapping_maker='random')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_random.gif?80233280\">\n\n\n\n### From a list of words\n\n\n```python\nstart_words = ['sensor', 'vibration', 'tempature']\nend_words = ['sense', 'meaning', 'detection']\nstart_im, end_im = make_start_and_end_images_with_words(\n    start_words, end_words, perm=True, repeat=2, size=150)\nstart_and_end_image(start_im, end_im).resize((600, 200))\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_12_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 5)\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_13_0.png)\n\n\n\n\n```python\nf = 'bunch_of_words.gif'\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"bunch_of_words.gif?7402792\">\n\n\n\n## From files\n\n\n```python\nstart_im = Image.open('sensor_strip_01.png')\nend_im = Image.open('sense_strip_01.png')\nstart_and_end_image(start_im.resize((200, 500)), end_im.resize((200, 500)))\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_16_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 7)\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_17_0.png)\n\n\n\n\n```python\nf = 'medley.gif'\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"medley.gif?39255021\">\n\n\n\n\n```python\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f, coordinate_mapping_maker='scan')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning.gif?41172115\">\n\n\n\n## an image and some text\n\n\n```python\nstart_im = 'img/waveform_01.png'  # will first look for a file, and if not consider as text\nend_im = 'makes sense'\n\nmk_gif_of_deformations(start_im, end_im, n_steps=20, \n                               save_to_file='image_and_text.gif')\ndisplay_gif('image_and_text.gif')  \n```\n\n\n\n\n<img src=\"image_and_text.gif?92524789\">\n\n\n\n\n\n\n# demonys\n\n## What do we think about other peoples?\n\nThis project is meant to get an idea of what people think of people for different nations, as seen by what they ask google about them. \n\nHere I use python code to acquire, clean up, and analyze the data. \n\n### Demonym\n\nIf you're like me and enjoy the false and fleeting impression of superiority that comes when you know a word someone else doesn't. If you're like me and go to parties for the sole purpose of seeking victims to get a one-up on, here's a cool word to add to your arsenal:\n\n**demonym**: a noun used to denote the natives or inhabitants of a particular country, state, city, etc.\n_\"he struggled for the correct demonym for the people of Manchester\"_\n\n### Back-story of this analysis\n \nDuring a discussion (about traveling in Europe) someone said \"why are the swiss so miserable\". Now, I wouldn't say that the swiss were especially miserable (a couple of ex-girlfriends aside), but to be fair he was contrasting with Italians, so perhaps he has a point. I apologize if you are swiss, or one of the two ex-girlfriends -- nothing personal, this is all for effect. \n\nWe googled \"why are the swiss so \", and sure enough, \"why are the swiss so miserable\" came up as one of the suggestions. So we got curious and started googling other peoples: the French, the Germans, etc.\n\nThat's the back-story of this analysis. This analysis is meant to get an idea of what we think of peoples from other countries. Of course, one can rightfully critique the approach I'll take to gauge \"what we think\" -- all three of these words should, but will not, be defined. I'm just going to see what google's *current* auto-suggest comes back with when I enter \"why are the X so \" (where X will be a noun that denotes the natives of inhabitants of a particular country; a *demonym* if you will). \n\n### Warning\n\nAgain, word of warning: All data and analyses are biased. \nTake everything you'll read here (and to be fair, what you read anywhere) with a grain of salt. \nFor simplicitly I'll saying things like \"what we think of...\" or \"who do we most...\", etc.\nBut I don't **really** mean that.\n\n### Resources\n\n* http://www.geography-site.co.uk/pages/countries/demonyms.html for my list of demonyms.\n* google for my suggestion engine, using the url prefix: `http://suggestqueries.google.com/complete/search?client=chrome&q=`\n\n\n## The results\n\n### In a nutshell\n\nBelow is listed 73 demonyms along with words extracted from the very first google suggestion when you type. \n\n`why are the DEMONYM so `\n\n```text\nafghan    \t                eyes beautiful\nalbanian  \t                     beautiful\namerican  \t          girl dolls expensive\naustralian\t                          tall\nbelgian   \t                    fries good\nbhutanese \t                         happy\nbrazilian \t              good at football\nbritish   \t     full of grief and despair\nbulgarian \t              properties cheap\nburmese   \t             cats affectionate\ncambodian \t                   cows skinny\ncanadian  \t                          nice\nchinese   \t                       healthy\ncolombian \t                  avocados big\ncuban     \t                   cigars good\nczech     \t                          tall\ndominican \t  republic and haiti different\negyptian  \t                gods important\nenglish   \t                      reserved\neritrean  \t                     beautiful\nethiopian \t                     beautiful\nfilipino  \t                         proud\nfinn      \t               shoes expensive\nfrench    \t                       healthy\ngerman    \t                          tall\ngreek     \t                gods messed up\nhaitian   \t                parents strict\nhungarian \t                    words long\nindian    \t            tv debates chaotic\nindonesian\t                         smart\niranian   \t                     beautiful\nisraeli   \t           startups successful\nitalian   \t                         short\njamaican  \t                sprinters fast\njapanese  \t                        polite\nkenyan    \t                  runners good\nlebanese  \t                          rich\nmalagasy  \t                    names long\nmalaysian \t                   drivers bad\nmaltese   \t                          rude\nmongolian \t                  horses small\nmoroccan  \t                rugs expensive\nnepalese  \t                     beautiful\nnigerian  \t                          tall\nnorth korean\t                      hats big\nnorwegian \t                 flights cheap\npakistani \t                          fair\nperuvian  \t               blueberries big\npole      \t                  vaulters hot\nportuguese\t                         short\npuerto rican\t       and cuban flags similar\nromanian  \t                     beautiful\nrussian   \t                  good at math\nsamoan    \t                           big\nsaudi     \t                      arrogant\nscottish  \t                        bitter\nsenegalese\t                          tall\nserbian   \t                          tall\nsingaporean\t                          rude\nsomali    \t                parents strict\nsouth african\t                     plugs big\nsouth korean\t                          tall\nsri lankan\t                          dark\nsudanese  \t                          tall\nswiss     \t        good at making watches\nsyrian    \t                families large\ntaiwanese \t                        pretty\nthai      \t                        pretty\ntongan    \t                           big\nukrainian \t                     beautiful\nvietnamese\t        fiercely nationalistic\nwelsh     \t                          dark\nzambian   \t                emeralds cheap\n```\n\n\nNotes:\n* The queries actually have a space after the \"so\", which matters so as to omit suggestions containing words that start with so.\n* Only the tail of the suggestion is shown -- minus prefix (`why are the DEMONYM` or `why are DEMONYM`) as well as the `so`, where ever it lands in the suggestion. \nFor example, the first suggestion for the american demonym was \"why are american dolls so expensive\", which results in the \"dolls expensive\" association. \n\n\n### Who do we most talk/ask about?\n\nThe original list contained 217 demonyms, but many of these yielded no suggestions (to the specific query format I used, that is). \nOnly 73 demonyms gave me at least one suggestion. \nBut within those, number of suggestions range between 1 and 20 (which is probably the default maximum number of suggestions for the API I used). \nSo, pretending that the number of suggestions is an indicator of how much we have to say, or how many different opinions we have, of each of the covered nationalities, \nhere's the top 15 demonyms people talk about, with the corresponding number of suggestions \n(proxy for \"the number of different things people ask about the said nationality). \n\n```text\nfrench         20\nsingaporean    20\ngerman         20\nbritish        20\nswiss          20\nenglish        19\nitalian        18\ncuban          18\ncanadian       18\nwelsh          18\naustralian     17\nmaltese        16\namerican       16\njapanese       14\nscottish       14\n```\n\n### Who do we least talk/ask about?\n\nConversely, here are the 19 demonyms that came back with only one suggestion.\n\n```text\nsomali          1\nbhutanese       1\nsyrian          1\ntongan          1\ncambodian       1\nmalagasy        1\nsaudi           1\nserbian         1\nczech           1\neritrean        1\nfinn            1\npuerto rican    1\npole            1\nhaitian         1\nhungarian       1\nperuvian        1\nmoroccan        1\nmongolian       1\nzambian         1\n```\n\n### What do we think about people?\n\nWhy are the French so...\n\nHow would you (if you're (un)lucky enough to know the French) finish this sentence?\nYou might even have several opinions about the French, and any other group of people you've rubbed shoulders with.\nWhat words would your palette contain to describe different nationalities?\nWhat words would others (at least those that ask questions to google) use?\n\nWell, here's what my auto-suggest search gave me. A set of 357 unique words and expressions to describe the 72 nationalities. \nSo a long tail of words use only for one nationality. But some words occur for more than one nationality. \nHere are the top 12 words/expressions used to describe people of the world. \n\n```text\nbeautiful         11\ntall              11\nshort              9\nnames long         8\nproud              8\nparents strict     8\nsmart              8\nnice               7\nboring             6\nrich               5\ndark               5\nsuccessful         5\n```\n\n### Who is beautiful? Who is tall? Who is short? Who is smart?\n\n```text\nbeautiful      : albanian, eritrean, ethiopian, filipino, iranian, lebanese, nepalese, pakistani, romanian, ukrainian, vietnamese\ntall           : australian, czech, german, nigerian, pakistani, samoan, senegalese, serbian, south korean, sudanese, taiwanese\nshort          : filipino, indonesian, italian, maltese, nepalese, pakistani, portuguese, singaporean, welsh\nnames long     : indian, malagasy, nigerian, portuguese, russian, sri lankan, thai, welsh\nproud          : albanian, ethiopian, filipino, iranian, lebanese, portuguese, scottish, welsh\nparents strict : albanian, ethiopian, haitian, indian, lebanese, pakistani, somali, sri lankan\nsmart          : indonesian, iranian, lebanese, pakistani, romanian, singaporean, taiwanese, vietnamese\nnice           : canadian, english, filipino, nepalese, portuguese, taiwanese, thai\nboring         : british, english, french, german, singaporean, swiss\nrich           : lebanese, pakistani, singaporean, taiwanese, vietnamese\ndark           : filipino, senegalese, sri lankan, vietnamese, welsh\nsuccessful     : chinese, english, japanese, lebanese, swiss\n```\n\n## How did I do it?\n\nI scraped a list of (country, demonym) pairs from a table in http://www.geography-site.co.uk/pages/countries/demonyms.html.\n\nThen I diagnosed these and manually made a mapping to simplify some \"complex\" entries, \nsuch as mapping an entry such as \"Irishman or Irishwoman or Irish\" to \"Irish\".\n\nUsing the google suggest API (http://suggestqueries.google.com/complete/search?client=chrome&q=), I requested what the suggestions \nfor `why are the $demonym so ` query pattern, for `$demonym` running through all 217 demonyms from the list above, \nstoring the results for each if the results were non-empty. \n\nThen, it was just a matter of pulling this data into memory, formatting it a bit, and creating a pandas dataframe that I could then interrogate.\n \n## Resources you can find here\n\nThe code to do this analysis yourself, from scratch here: `data_acquisition.py`.\n\nThe jupyter notebook I actually used when I developed this: `01 - Demonyms and adjectives - why are the french so....ipynb`\n \nNote you'll need to pip install py2store if you haven't already.\n\nIn the `data` folder you'll find\n* country_demonym.p: A pickle of a dataframe of countries and corresponding demonyms\n* country_demonym.xlsx: The same as above, but in excel form\n* demonym_suggested_characteristics.p: A pickle of 73 demonyms and auto-suggestion information, including characteristics. \n* what_we_think_about_demonyns.xlsx: An excel containing various statistics about demonyms and their (perceived) characteristics\n \n\n\n\n\n\n# Agglutinations\n\nInspired from a [tweet](https://twitter.com/raymondh/status/1311003482531401729) from Raymond Hettinger this morning:\n\n_Resist the urge to elide the underscore in multiword function or method names_\n\nSo I wondered...\n\n## Gluglus\n\nThe gluglu of a word is the number of partitions you can make of that word into words (of length at least 2 (so no using a or i)).\n(No \"gluglu\" isn't an actual term -- unless everyone starts using it from now on. \nBut it was inspired from an actual [linguistic term](https://en.wikipedia.org/wiki/Agglutination).)\n\nFor example, the gluglu of ``newspaper`` is 4:\n\n```\nnewspaper\n    new spa per\n    news pa per\n    news paper\n```\n\nEvery (valid) word has gluglu at least 1.\n\n\n## How many standard library names have gluglus at last 2?\n\n108\n\nHere's [the list](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/standard_lib_gluglus.txt) of all of them.\n\nThe winner has a gluglu of 6 (not 7 because formatannotationrelativeto isn't in the dictionary)\n\n```\nformatannotationrelativeto\n\tfor mat an not at ion relative to\n\tfor mat annotation relative to\n\tform at an not at ion relative to\n\tform at annotation relative to\n\tformat an not at ion relative to\n\tformat annotation relative to\n```\n\n## Details\n\n### Dictionary\n\nReally it depends on what dictionary we use. \nHere, I used a very conservative one. \nThe intersection of two lists: The [corncob](http://www.mieliestronk.com/corncob_lowercase.txt) \nand the [google10000](https://raw.githubusercontent.com/first20hours/google-10000-english/master/google-10000-english-usa.txt) word lists.\nAdditionally, I only kept of those, those that had at least 2 letters, and had only letters (no hyphens or disturbing diacritics).\n\nDiacritics. Look it up. Impress your next nerd date.\n\nIm left with 8116 words. You can find them [here](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/words_8116.csv).\n\n### Standard Lib Names\n\nSurprisingly, that was the hardest part. I know I'm missing some, but that's enough rabbit-holing. \n\nWhat I did (modulo some exceptions I won't look into) was to walk the standard lib modules (even that list wasn't a given!) \nextracting (recursively( the names of any (non-underscored) attributes if they were modules or callables, \nas well as extracting the arguments of these callables (when they had signatures).\n\nYou can find the code I used to extract these names [here](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/py_names.py) \nand the actual list [there](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/standard_lib_module_names.csv).\n\n\n\n# covid\n\n## Bar Chart Races (applied to covid-19 spread)\n\nThe module will show is how to make these:\n- Confirmed cases (by country): https://public.flourish.studio/visualisation/1704821/\n- Deaths (by country): https://public.flourish.studio/visualisation/1705644/\n- US Confirmed cases (by state): https://public.flourish.studio/visualisation/1794768/\n- US Deaths (by state): https://public.flourish.studio/visualisation/1794797/\n\n### The script\n\nIf you just want to run this as a script to get the job done, you have one here: \nhttps://raw.githubusercontent.com/thorwhalen/tapyoca/master/covid/covid_bar_chart_race.py\n\nRun like this\n```\n$ python covid_bar_chart_race.py -h\nusage: covid_bar_chart_race.py [-h] {mk-and-save-covid-data,update-covid-data,instructions-to-make-bar-chart-race} ...\n\npositional arguments:\n  {mk-and-save-covid-data,update-covid-data,instructions-to-make-bar-chart-race}\n    mk-and-save-covid-data\n                        :param data_sources: Dirpath or py2store Store where the data is :param kinds: The kinds of data you want to compute and save :param\n                        skip_first_days: :param verbose: :return:\n    update-covid-data   update the coronavirus data\n    instructions-to-make-bar-chart-race\n\noptional arguments:\n  -h, --help            show this help message and exit\n ```\n \n \n### The jupyter notebook\n\nThe notebook (the .ipynb file) shows you how to do it step by step in case you want to reuse the methods for other stuff.\n\n\n\n## Getting and preparing the data\n\nCorona virus data here: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset (direct download: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset/download). It's currently updated daily, so download a fresh copy if you want.\n\nPopulation data here: http://api.worldbank.org/v2/en/indicator/SP.POP.TOTL?downloadformat=csv\n\nIt comes under the form of a zip file (currently named `novel-corona-virus-2019-dataset.zip` with several `.csv` files in them. We use `py2store` (To install: `pip install py2store`. Project lives here: https://github.com/i2mint/py2store) to access and pre-prepare it. It allows us to not have to unzip the file and replace the older folder with it every time we download a new one. It also gives us the csvs as `pandas.DataFrame` already. \n\n\n```python\nimport pandas as pd\nfrom io import BytesIO\nfrom py2store import kv_wrap, ZipReader  # google it and pip install it\nfrom py2store.caching import mk_cached_store\nfrom py2store import QuickPickleStore\nfrom py2store.sources import FuncReader\n\ndef country_flag_image_url():\n    import pandas as pd\n    return pd.read_csv(\n        'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv')\n\ndef kaggle_coronavirus_dataset():\n    import kaggle\n    from io import BytesIO\n    # didn't find the pure binary download function, so using temp dir to emulate\n    from tempfile import mkdtemp  \n    download_dir = mkdtemp()\n    filename = 'novel-corona-virus-2019-dataset.zip'\n    zip_file = os.path.join(download_dir, filename)\n    \n    dataset = 'sudalairajkumar/novel-corona-virus-2019-dataset'\n    kaggle.api.dataset_download_files(dataset, download_dir)\n    with open(zip_file, 'rb') as fp:\n        b = fp.read()\n    return BytesIO(b)\n\ndef city_population_in_time():\n    import pandas as pd\n    return pd.read_csv(\n        'https://gist.githubusercontent.com/johnburnmurdoch/'\n        '4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations'\n    )\n\ndef country_flag_image_url_prep(df: pd.DataFrame):\n    # delete the region col (we don't need it)\n    del df['region']\n    # rewriting a few (not all) of the country names to match those found in kaggle covid data\n    # Note: The list is not complete! Add to it as needed\n    old_and_new = [('USA', 'US'), \n                   ('Iran, Islamic Rep.', 'Iran'), \n                   ('UK', 'United Kingdom'), \n                   ('Korea, Rep.', 'Korea, South')]\n    for old, new in old_and_new:\n        df['country'] = df['country'].replace(old, new)\n\n    return df\n\n\n@kv_wrap.outcoming_vals(lambda x: pd.read_csv(BytesIO(x)))  # this is to format the data as a dataframe\nclass ZippedCsvs(ZipReader):\n    pass\n# equivalent to ZippedCsvs = kv_wrap.outcoming_vals(lambda x: pd.read_csv(BytesIO(x)))(ZipReader)\n```\n\n\n```python\n# Enter here the place you want to cache your data\nmy_local_cache = os.path.expanduser('~/ddir/my_sources')\n```\n\n\n```python\nCachedFuncReader = mk_cached_store(FuncReader, QuickPickleStore(my_local_cache))\n```\n\n\n```python\ndata_sources = CachedFuncReader([country_flag_image_url, \n                                 kaggle_coronavirus_dataset, \n                                 city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ncovid_datasets = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\nlist(covid_datasets)\n```\n\n\n\n\n    ['COVID19_line_list_data.csv',\n     'COVID19_open_line_list.csv',\n     'covid_19_data.csv',\n     'time_series_covid_19_confirmed.csv',\n     'time_series_covid_19_confirmed_US.csv',\n     'time_series_covid_19_deaths.csv',\n     'time_series_covid_19_deaths_US.csv',\n     'time_series_covid_19_recovered.csv']\n\n\n\n\n```python\ncovid_datasets['time_series_covid_19_confirmed.csv'].head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Province/State</th>\n      <th>Country/Region</th>\n      <th>Lat</th>\n      <th>Long</th>\n      <th>1/22/20</th>\n      <th>1/23/20</th>\n      <th>1/24/20</th>\n      <th>1/25/20</th>\n      <th>1/26/20</th>\n      <th>1/27/20</th>\n      <th>...</th>\n      <th>3/24/20</th>\n      <th>3/25/20</th>\n      <th>3/26/20</th>\n      <th>3/27/20</th>\n      <th>3/28/20</th>\n      <th>3/29/20</th>\n      <th>3/30/20</th>\n      <th>3/31/20</th>\n      <th>4/1/20</th>\n      <th>4/2/20</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>Afghanistan</td>\n      <td>33.0000</td>\n      <td>65.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>74</td>\n      <td>84</td>\n      <td>94</td>\n      <td>110</td>\n      <td>110</td>\n      <td>120</td>\n      <td>170</td>\n      <td>174</td>\n      <td>237</td>\n      <td>273</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>Albania</td>\n      <td>41.1533</td>\n      <td>20.1683</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>123</td>\n      <td>146</td>\n      <td>174</td>\n      <td>186</td>\n      <td>197</td>\n      <td>212</td>\n      <td>223</td>\n      <td>243</td>\n      <td>259</td>\n      <td>277</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>Algeria</td>\n      <td>28.0339</td>\n      <td>1.6596</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>264</td>\n      <td>302</td>\n      <td>367</td>\n      <td>409</td>\n      <td>454</td>\n      <td>511</td>\n      <td>584</td>\n      <td>716</td>\n      <td>847</td>\n      <td>986</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>Andorra</td>\n      <td>42.5063</td>\n      <td>1.5218</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>164</td>\n      <td>188</td>\n      <td>224</td>\n      <td>267</td>\n      <td>308</td>\n      <td>334</td>\n      <td>370</td>\n      <td>376</td>\n      <td>390</td>\n      <td>428</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>Angola</td>\n      <td>-11.2027</td>\n      <td>17.8739</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>5</td>\n      <td>7</td>\n      <td>7</td>\n      <td>7</td>\n      <td>8</td>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 76 columns</p>\n</div>\n\n\n\n\n```python\ncountry_flag_image_url = data_sources['country_flag_image_url']\ncountry_flag_image_url.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nfrom IPython.display import Image\nflag_image_url_of_country = country_flag_image_url.set_index('country')['flag_image_url']\nImage(url=flag_image_url_of_country['Australia'])\n```\n\n\n\n\n<img src=\"https://www.countryflags.io/au/flat/64.png\"/>\n\n\n\n### Update coronavirus data\n\n\n```python\n# To update the coronavirus data:\ndef update_covid_data(data_sources):\n    \"\"\"update the coronavirus data\"\"\"\n    if 'kaggle_coronavirus_dataset' in data_sources._caching_store:\n        del data_sources._caching_store['kaggle_coronavirus_dataset']  # delete the cached item\n    _ = data_sources['kaggle_coronavirus_dataset']\n\n# update_covid_data(data_sources)  # uncomment here when you want to update\n```\n\n### Prepare data for flourish upload\n\n\n```python\nimport re\n\ndef print_if_verbose(verbose, *args, **kwargs):\n    if verbose:\n        print(*args, **kwargs)\n        \ndef country_data_for_data_kind(data_sources, kind='confirmed', skip_first_days=0, verbose=False):\n    \"\"\"kind can be 'confirmed', 'deaths', 'confirmed_US', 'confirmed_US', 'recovered'\"\"\"\n    \n    covid_datasets = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\n    \n    df = covid_datasets[f'time_series_covid_19_{kind}.csv']\n    # df = s['time_series_covid_19_deaths.csv']\n    if 'Province/State' in df.columns:\n        df.loc[df['Province/State'].isna(), 'Province/State'] = 'n/a'  # to avoid problems arising from NaNs\n\n    print_if_verbose(verbose, f\"Before data shape: {df.shape}\")\n\n    # drop some columns we don't need\n    p = re.compile('\\d+/\\d+/\\d+')\n\n    assert all(isinstance(x, str) for x in df.columns)\n    date_cols = [x for x in df.columns if p.match(x)]\n    if not kind.endswith('US'):\n        df = df.loc[:, ['Country/Region'] + date_cols]\n        # group countries and sum up the contributions of their states/regions/pargs\n        df['country'] = df.pop('Country/Region')\n        df = df.groupby('country').sum()\n    else:\n        df = df.loc[:, ['Province_State'] + date_cols]\n        df['state'] = df.pop('Province_State')\n        df = df.groupby('state').sum()\n\n    \n    print_if_verbose(verbose, f\"After data shape: {df.shape}\")\n    df = df.iloc[:, skip_first_days:]\n    \n    if not kind.endswith('US'):\n        # Joining with the country image urls and saving as an xls\n        country_image_url = country_flag_image_url_prep(data_sources['country_flag_image_url'])\n        t = df.copy()\n        t.columns = [str(x)[:10] for x in t.columns]\n        t = t.reset_index(drop=False)\n        t = country_image_url.merge(t, how='outer')\n        t = t.set_index('country')\n        df = t\n    else:    \n        pass\n\n    return df\n\n\ndef mk_and_save_country_data_for_data_kind(data_sources, kind='confirmed', skip_first_days=0, verbose=False):\n    t = country_data_for_data_kind(data_sources, kind, skip_first_days, verbose)\n    filepath = f'country_covid_{kind}.xlsx'\n    t.to_excel(filepath)\n    print_if_verbose(verbose, f\"Was saved here: {filepath}\")\n\n```\n\n\n```python\n# for kind in ['confirmed', 'deaths', 'recovered', 'confirmed_US', 'deaths_US']:\nfor kind in ['confirmed', 'deaths', 'recovered', 'confirmed_US', 'deaths_US']:\n    mk_and_save_country_data_for_data_kind(data_sources, kind=kind, skip_first_days=39, verbose=True)\n```\n\n    Before data shape: (262, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_confirmed.xlsx\n    Before data shape: (262, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_deaths.xlsx\n    Before data shape: (248, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_recovered.xlsx\n    Before data shape: (3253, 86)\n    After data shape: (58, 75)\n    Was saved here: country_covid_confirmed_US.xlsx\n    Before data shape: (3253, 87)\n    After data shape: (58, 75)\n    Was saved here: country_covid_deaths_US.xlsx\n\n\n### Upload to Flourish, tune, and publish\n\nGo to https://public.flourish.studio/, get a free account, and play.\n\nGot to https://app.flourish.studio/templates\n\nChoose \"Bar chart race\". At the time of writing this, it was here: https://app.flourish.studio/visualisation/1706060/\n\n... and then play with the settings\n\n\n## Discussion of the methods\n\n\n```python\nfrom py2store import *\nfrom IPython.display import Image\n```\n\n### country flags images\n\nThe manual data prep looks something like this.\n\n\n```python\nimport pandas as pd\n\n# get the csv data from the url\ncountry_image_url_source = \\\n    'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv'\ncountry_image_url = pd.read_csv(country_image_url_source)\n\n# delete the region col (we don't need it)\ndel country_image_url['region']\n\n# rewriting a few (not all) of the country names to match those found in kaggle covid data\n# Note: The list is not complete! Add to it as needed\n# TODO: (Wishful) Using a general smart soft-matching algorithm to do this automatically.\n# TODO:    This could use edit-distance, synonyms, acronym generation, etc.\nold_and_new = [('USA', 'US'), \n               ('Iran, Islamic Rep.', 'Iran'), \n               ('UK', 'United Kingdom'), \n               ('Korea, Rep.', 'Korea, South')]\nfor old, new in old_and_new:\n    country_image_url['country'] = country_image_url['country'].replace(old, new)\n\nimage_url_of_country = country_image_url.set_index('country')['flag_image_url']\n\ncountry_image_url.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nImage(url=image_url_of_country['Australia'])\n```\n\n\n\n\n<img src=\"https://www.countryflags.io/au/flat/64.png\"/>\n\n\n\n### Caching the flag images data\n\nDownloading our data sources every time we need them is not sustainable. What if they're big? What if you're offline or have slow internet (yes, dear future reader, even in the US, during coronavirus times!)?\n\nCaching. A \"cache aside\" read-cache. That's the word. py2store has tools for that (most of which are are caching.py). \n\nSo let's say we're going to have a local folder where we'll store various datas we download. The principle is as follows:\n\n\n```python\nfrom py2store.caching import mk_cached_store\n\nclass TheSource(dict): ...\nthe_cache = {}\nTheCacheSource = mk_cached_store(TheSource, the_cache)\n\nthe_source = TheSource({'green': 'eggs', 'and': 'ham'})\n\nthe_cached_source = TheCacheSource(the_source)\nprint(f\"the_cache: {the_cache}\")\nprint(f\"Getting green...\")\nthe_cached_source['green']\nprint(f\"the_cache: {the_cache}\")\nprint(\"... so the next time the_cached_source will get it's green from that the_cache\")\n```\n\n    the_cache: {}\n    Getting green...\n    the_cache: {'green': 'eggs'}\n    ... so the next time the_cached_source will get it's green from that the_cache\n\n\nBut now, you'll notice a slight problem ahead. What exactly does our source store (or rather reader) looks like? In it's raw form it would take urls as it's keys, and the response of a request as it's value. That store wouldn't have an `__iter__` for sure (unless you're Google). But more to the point here, the `mk_cached_store` tool uses the same key for the source and the cache, and we can't just use the url as is, to be a local file path. \n\nThere's many ways we could solve this. One way is to add a key map layer on the cache store, so externally, it speaks the url key language, but internally it will map that url to a valid local file path. We've been there, we got the T-shirt!\n\nBut what we're going to do is a bit different: We're going to do the key mapping in the source store itself. It seems to make more sense in our context: We have a data source of `name: data` pairs, and if we impose that the name should be a valid file name, we don't need to have a key map in the cache store.\n\nSo let's start by building this `MyDataStore` store. We'll start by defining the functions that get us the data we want. \n\n\n```python\ndef country_flag_image_url():\n    import pandas as pd\n    return pd.read_csv(\n        'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv')\n\ndef kaggle_coronavirus_dataset():\n    import kaggle\n    from io import BytesIO\n    # didn't find the pure binary download function, so using temp dir to emulate\n    from tempfile import mkdtemp  \n    download_dir = mkdtemp()\n    filename = 'novel-corona-virus-2019-dataset.zip'\n    zip_file = os.path.join(download_dir, filename)\n    \n    dataset = 'sudalairajkumar/novel-corona-virus-2019-dataset'\n    kaggle.api.dataset_download_files(dataset, download_dir)\n    with open(zip_file, 'rb') as fp:\n        b = fp.read()\n    return BytesIO(b)\n\ndef city_population_in_time():\n    import pandas as pd\n    return pd.read_csv(\n        'https://gist.githubusercontent.com/johnburnmurdoch/'\n        '4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations'\n    )\n```\n\nNow we can make a store that simply uses these function names as the keys, and their returned value as the values.\n\n\n```python\nfrom py2store.base import KvReader\nfrom functools import lru_cache\n\nclass FuncReader(KvReader):\n    _getitem_cache_size = 999\n    def __init__(self, funcs):\n        # TODO: assert no free arguments (arguments are allowed but must all have defaults)\n        self.funcs = funcs\n        self._func_of_name = {func.__name__: func for func in funcs}\n\n    def __contains__(self, k):\n        return k in self._func_of_name\n    \n    def __iter__(self):\n        yield from self._func_of_name\n        \n    def __len__(self):\n        return len(self._func_of_name)\n\n    @lru_cache(maxsize=_getitem_cache_size)\n    def __getitem__(self, k):\n        return self._func_of_name[k]()  # call the func\n    \n    def __hash__(self):\n        return 1\n    \n```\n\n\n```python\ndata_sources = FuncReader([country_flag_image_url, kaggle_coronavirus_dataset, city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['city_population_in_time']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>group</th>\n      <th>year</th>\n      <th>value</th>\n      <th>subGroup</th>\n      <th>city_id</th>\n      <th>lastValue</th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1575</td>\n      <td>200.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1576</td>\n      <td>212.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1577</td>\n      <td>224.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>212.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1578</td>\n      <td>236.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>224.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1579</td>\n      <td>248.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>236.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6247</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1561</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6248</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1562</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6249</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1563</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6250</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1564</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6251</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1565</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n  </tbody>\n</table>\n<p>6252 rows \u00d7 9 columns</p>\n</div>\n\n\n\nBut we wanted this all to be cached locally, right? So a few more lines to do that!\n\n\n```python\nfrom py2store.caching import mk_cached_store\nfrom py2store import QuickPickleStore\n    \nmy_local_cache = os.path.expanduser('~/ddir/my_sources')\n\nCachedFuncReader = mk_cached_store(FuncReader, QuickPickleStore(my_local_cache))\n```\n\n\n```python\ndata_sources = CachedFuncReader([country_flag_image_url, kaggle_coronavirus_dataset, city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['city_population_in_time']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>group</th>\n      <th>year</th>\n      <th>value</th>\n      <th>subGroup</th>\n      <th>city_id</th>\n      <th>lastValue</th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1575</td>\n      <td>200.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1576</td>\n      <td>212.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1577</td>\n      <td>224.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>212.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1578</td>\n      <td>236.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>224.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1579</td>\n      <td>248.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>236.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6247</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1561</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6248</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1562</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6249</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1563</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6250</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1564</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6251</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1565</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n  </tbody>\n</table>\n<p>6252 rows \u00d7 9 columns</p>\n</div>\n\n\n\n\n```python\nz = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\nlist(z)\n```\n",
  "long_description_content_type": "text/markdown",
  "description_file": "README.md",
  "root_url": "https://github.com/thorwhalen",
  "description": "A medley of things that got coded because there was an itch to do so",
  "author": "thorwhalen",
  "license": "Apache Software License",
  "description-file": "README.md",
  "install_requires": [],
  "keywords": [
    "documentation",
    "packaging",
    "publishing"
  ]
}/usr/lib/python3.13/site-packages/setuptools/dist.py:476: SetuptoolsDeprecationWarning: Invalid dash-separated options
!!

        ********************************************************************************
        Usage of dash-separated 'description-file' will not be supported in future
        versions. Please use the underscore name 'description_file' instead.

        This deprecation is overdue, please update your project and remove deprecated
        calls to avoid build errors in the future.

        See https://setuptools.pypa.io/en/latest/userguide/declarative_config.html for details.
        ********************************************************************************

!!
  opt = self.warn_dash_deprecation(opt, section)
/usr/lib/python3.13/site-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'description_file'
  warnings.warn(msg)
/usr/lib/python3.13/site-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'root_url'
  warnings.warn(msg)
/usr/lib/python3.13/site-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'description-file'
  warnings.warn(msg)

--------------------------------------------------------------------
running egg_info
writing tapyoca.egg-info/PKG-INFO
writing dependency_links to tapyoca.egg-info/dependency_links.txt
writing top-level names to tapyoca.egg-info/top_level.txt
reading manifest file 'tapyoca.egg-info/SOURCES.txt'
adding license file 'LICENSE'
writing manifest file 'tapyoca.egg-info/SOURCES.txt'
Handling wheel from get_requires_for_build_wheel
Requirement satisfied: wheel
   (installed: wheel 0.43.0)
!!!! containing_folder_name=tapyoca-0.0.4 but setup name is tapyoca
Setup params -------------------------------------------------------
{
  "name": "tapyoca",
  "version": "0.0.4",
  "url": "https://github.com/thorwhalen/tapyoca",
  "packages": [
    "tapyoca",
    "tapyoca.agglutination",
    "tapyoca.covid",
    "tapyoca.darpa",
    "tapyoca.demonyms",
    "tapyoca.indexing_podcasts",
    "tapyoca.parquet_deformations",
    "tapyoca.phoneming"
  ],
  "include_package_data": true,
  "platforms": "any",
  "long_description": "# tapyoca\nA medley of small projects\n\n\n# parquet_deformations\n\nI'm calling these [Parquet deformations](https://www.theguardian.com/artanddesign/alexs-adventures-in-numberland/2014/sep/09/crazy-paving-the-twisted-world-of-parquet-deformations#:~:text=In%20the%201960s%20an%20American,the%20regularity%20of%20the%20tiling.) but purest would lynch me. \n\nReally, I just wanted to transform one word into another word, gradually, as I've seen in some of [Escher's](https://en.wikipedia.org/wiki/M._C._Escher) work, so I looked it up, and saw that it's called parquet deformations. The math looked enticing, but I had no time for that, so I did the first way I could think of: Mapping pixels to pixels (in some fashion -- but nearest neighbors is the method that yields nicest results, under the pixel-level restriction). \n\nOf course, this can be applied to any image (that will be transformed to B/W (not even gray -- I mean actual B/W), and there's several ways you can perform the parquet (I like the gif rendering). \n\nThe main function (exposed as a script) is `mk_deformation_image`. All you need is to specify two images (or words). If you want, of course, you can specify:\n- `n_steps`: Number of steps from start to end image\n- `save_to_file`: path to file to save too (if not given, will just return the image object)\n- `kind`: 'gif', 'horizontal_stack', or 'vertical_stack'\n- `coordinate_mapping_maker`: A function that will return the mapping between start and end. \nThis function should return a pair (`from_coord`, `to_coord`) of aligned matrices whose 2 columns are the the \n`(x, y)` coordinates, and the rows represent aligned positions that should be mapped. \n\n\n\n## Examples\n\n### Two words...\n\n\n```python\nfit_to_size = 400\nstart_im = image_of_text('sensor').rotate(90, expand=1)\nend_im = image_of_text('meaning').rotate(90, expand=1)\nstart_and_end_image(start_im, end_im)\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_5_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 15, kind='h').resize((500,200))\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_6_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im.transpose(4), end_im.transpose(4), 5, kind='v').resize((200,200))\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_7_0.png)\n\n\n\n\n```python\nf = 'sensor_meaning_knn.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_knn.gif?76128495\">\n\n\n\n\n```python\nf = 'sensor_meaning_scan.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f, \n                     coordinate_mapping_maker='scan')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_scan.gif?76996026\">\n\n\n\n\n```python\nf = 'sensor_meaning_random.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f, \n                     coordinate_mapping_maker='random')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_random.gif?80233280\">\n\n\n\n### From a list of words\n\n\n```python\nstart_words = ['sensor', 'vibration', 'tempature']\nend_words = ['sense', 'meaning', 'detection']\nstart_im, end_im = make_start_and_end_images_with_words(\n    start_words, end_words, perm=True, repeat=2, size=150)\nstart_and_end_image(start_im, end_im).resize((600, 200))\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_12_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 5)\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_13_0.png)\n\n\n\n\n```python\nf = 'bunch_of_words.gif'\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"bunch_of_words.gif?7402792\">\n\n\n\n## From files\n\n\n```python\nstart_im = Image.open('sensor_strip_01.png')\nend_im = Image.open('sense_strip_01.png')\nstart_and_end_image(start_im.resize((200, 500)), end_im.resize((200, 500)))\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_16_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 7)\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_17_0.png)\n\n\n\n\n```python\nf = 'medley.gif'\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"medley.gif?39255021\">\n\n\n\n\n```python\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f, coordinate_mapping_maker='scan')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning.gif?41172115\">\n\n\n\n## an image and some text\n\n\n```python\nstart_im = 'img/waveform_01.png'  # will first look for a file, and if not consider as text\nend_im = 'makes sense'\n\nmk_gif_of_deformations(start_im, end_im, n_steps=20, \n                               save_to_file='image_and_text.gif')\ndisplay_gif('image_and_text.gif')  \n```\n\n\n\n\n<img src=\"image_and_text.gif?92524789\">\n\n\n\n\n\n\n# demonys\n\n## What do we think about other peoples?\n\nThis project is meant to get an idea of what people think of people for different nations, as seen by what they ask google about them. \n\nHere I use python code to acquire, clean up, and analyze the data. \n\n### Demonym\n\nIf you're like me and enjoy the false and fleeting impression of superiority that comes when you know a word someone else doesn't. If you're like me and go to parties for the sole purpose of seeking victims to get a one-up on, here's a cool word to add to your arsenal:\n\n**demonym**: a noun used to denote the natives or inhabitants of a particular country, state, city, etc.\n_\"he struggled for the correct demonym for the people of Manchester\"_\n\n### Back-story of this analysis\n \nDuring a discussion (about traveling in Europe) someone said \"why are the swiss so miserable\". Now, I wouldn't say that the swiss were especially miserable (a couple of ex-girlfriends aside), but to be fair he was contrasting with Italians, so perhaps he has a point. I apologize if you are swiss, or one of the two ex-girlfriends -- nothing personal, this is all for effect. \n\nWe googled \"why are the swiss so \", and sure enough, \"why are the swiss so miserable\" came up as one of the suggestions. So we got curious and started googling other peoples: the French, the Germans, etc.\n\nThat's the back-story of this analysis. This analysis is meant to get an idea of what we think of peoples from other countries. Of course, one can rightfully critique the approach I'll take to gauge \"what we think\" -- all three of these words should, but will not, be defined. I'm just going to see what google's *current* auto-suggest comes back with when I enter \"why are the X so \" (where X will be a noun that denotes the natives of inhabitants of a particular country; a *demonym* if you will). \n\n### Warning\n\nAgain, word of warning: All data and analyses are biased. \nTake everything you'll read here (and to be fair, what you read anywhere) with a grain of salt. \nFor simplicitly I'll saying things like \"what we think of...\" or \"who do we most...\", etc.\nBut I don't **really** mean that.\n\n### Resources\n\n* http://www.geography-site.co.uk/pages/countries/demonyms.html for my list of demonyms.\n* google for my suggestion engine, using the url prefix: `http://suggestqueries.google.com/complete/search?client=chrome&q=`\n\n\n## The results\n\n### In a nutshell\n\nBelow is listed 73 demonyms along with words extracted from the very first google suggestion when you type. \n\n`why are the DEMONYM so `\n\n```text\nafghan    \t                eyes beautiful\nalbanian  \t                     beautiful\namerican  \t          girl dolls expensive\naustralian\t                          tall\nbelgian   \t                    fries good\nbhutanese \t                         happy\nbrazilian \t              good at football\nbritish   \t     full of grief and despair\nbulgarian \t              properties cheap\nburmese   \t             cats affectionate\ncambodian \t                   cows skinny\ncanadian  \t                          nice\nchinese   \t                       healthy\ncolombian \t                  avocados big\ncuban     \t                   cigars good\nczech     \t                          tall\ndominican \t  republic and haiti different\negyptian  \t                gods important\nenglish   \t                      reserved\neritrean  \t                     beautiful\nethiopian \t                     beautiful\nfilipino  \t                         proud\nfinn      \t               shoes expensive\nfrench    \t                       healthy\ngerman    \t                          tall\ngreek     \t                gods messed up\nhaitian   \t                parents strict\nhungarian \t                    words long\nindian    \t            tv debates chaotic\nindonesian\t                         smart\niranian   \t                     beautiful\nisraeli   \t           startups successful\nitalian   \t                         short\njamaican  \t                sprinters fast\njapanese  \t                        polite\nkenyan    \t                  runners good\nlebanese  \t                          rich\nmalagasy  \t                    names long\nmalaysian \t                   drivers bad\nmaltese   \t                          rude\nmongolian \t                  horses small\nmoroccan  \t                rugs expensive\nnepalese  \t                     beautiful\nnigerian  \t                          tall\nnorth korean\t                      hats big\nnorwegian \t                 flights cheap\npakistani \t                          fair\nperuvian  \t               blueberries big\npole      \t                  vaulters hot\nportuguese\t                         short\npuerto rican\t       and cuban flags similar\nromanian  \t                     beautiful\nrussian   \t                  good at math\nsamoan    \t                           big\nsaudi     \t                      arrogant\nscottish  \t                        bitter\nsenegalese\t                          tall\nserbian   \t                          tall\nsingaporean\t                          rude\nsomali    \t                parents strict\nsouth african\t                     plugs big\nsouth korean\t                          tall\nsri lankan\t                          dark\nsudanese  \t                          tall\nswiss     \t        good at making watches\nsyrian    \t                families large\ntaiwanese \t                        pretty\nthai      \t                        pretty\ntongan    \t                           big\nukrainian \t                     beautiful\nvietnamese\t        fiercely nationalistic\nwelsh     \t                          dark\nzambian   \t                emeralds cheap\n```\n\n\nNotes:\n* The queries actually have a space after the \"so\", which matters so as to omit suggestions containing words that start with so.\n* Only the tail of the suggestion is shown -- minus prefix (`why are the DEMONYM` or `why are DEMONYM`) as well as the `so`, where ever it lands in the suggestion. \nFor example, the first suggestion for the american demonym was \"why are american dolls so expensive\", which results in the \"dolls expensive\" association. \n\n\n### Who do we most talk/ask about?\n\nThe original list contained 217 demonyms, but many of these yielded no suggestions (to the specific query format I used, that is). \nOnly 73 demonyms gave me at least one suggestion. \nBut within those, number of suggestions range between 1 and 20 (which is probably the default maximum number of suggestions for the API I used). \nSo, pretending that the number of suggestions is an indicator of how much we have to say, or how many different opinions we have, of each of the covered nationalities, \nhere's the top 15 demonyms people talk about, with the corresponding number of suggestions \n(proxy for \"the number of different things people ask about the said nationality). \n\n```text\nfrench         20\nsingaporean    20\ngerman         20\nbritish        20\nswiss          20\nenglish        19\nitalian        18\ncuban          18\ncanadian       18\nwelsh          18\naustralian     17\nmaltese        16\namerican       16\njapanese       14\nscottish       14\n```\n\n### Who do we least talk/ask about?\n\nConversely, here are the 19 demonyms that came back with only one suggestion.\n\n```text\nsomali          1\nbhutanese       1\nsyrian          1\ntongan          1\ncambodian       1\nmalagasy        1\nsaudi           1\nserbian         1\nczech           1\neritrean        1\nfinn            1\npuerto rican    1\npole            1\nhaitian         1\nhungarian       1\nperuvian        1\nmoroccan        1\nmongolian       1\nzambian         1\n```\n\n### What do we think about people?\n\nWhy are the French so...\n\nHow would you (if you're (un)lucky enough to know the French) finish this sentence?\nYou might even have several opinions about the French, and any other group of people you've rubbed shoulders with.\nWhat words would your palette contain to describe different nationalities?\nWhat words would others (at least those that ask questions to google) use?\n\nWell, here's what my auto-suggest search gave me. A set of 357 unique words and expressions to describe the 72 nationalities. \nSo a long tail of words use only for one nationality. But some words occur for more than one nationality. \nHere are the top 12 words/expressions used to describe people of the world. \n\n```text\nbeautiful         11\ntall              11\nshort              9\nnames long         8\nproud              8\nparents strict     8\nsmart              8\nnice               7\nboring             6\nrich               5\ndark               5\nsuccessful         5\n```\n\n### Who is beautiful? Who is tall? Who is short? Who is smart?\n\n```text\nbeautiful      : albanian, eritrean, ethiopian, filipino, iranian, lebanese, nepalese, pakistani, romanian, ukrainian, vietnamese\ntall           : australian, czech, german, nigerian, pakistani, samoan, senegalese, serbian, south korean, sudanese, taiwanese\nshort          : filipino, indonesian, italian, maltese, nepalese, pakistani, portuguese, singaporean, welsh\nnames long     : indian, malagasy, nigerian, portuguese, russian, sri lankan, thai, welsh\nproud          : albanian, ethiopian, filipino, iranian, lebanese, portuguese, scottish, welsh\nparents strict : albanian, ethiopian, haitian, indian, lebanese, pakistani, somali, sri lankan\nsmart          : indonesian, iranian, lebanese, pakistani, romanian, singaporean, taiwanese, vietnamese\nnice           : canadian, english, filipino, nepalese, portuguese, taiwanese, thai\nboring         : british, english, french, german, singaporean, swiss\nrich           : lebanese, pakistani, singaporean, taiwanese, vietnamese\ndark           : filipino, senegalese, sri lankan, vietnamese, welsh\nsuccessful     : chinese, english, japanese, lebanese, swiss\n```\n\n## How did I do it?\n\nI scraped a list of (country, demonym) pairs from a table in http://www.geography-site.co.uk/pages/countries/demonyms.html.\n\nThen I diagnosed these and manually made a mapping to simplify some \"complex\" entries, \nsuch as mapping an entry such as \"Irishman or Irishwoman or Irish\" to \"Irish\".\n\nUsing the google suggest API (http://suggestqueries.google.com/complete/search?client=chrome&q=), I requested what the suggestions \nfor `why are the $demonym so ` query pattern, for `$demonym` running through all 217 demonyms from the list above, \nstoring the results for each if the results were non-empty. \n\nThen, it was just a matter of pulling this data into memory, formatting it a bit, and creating a pandas dataframe that I could then interrogate.\n \n## Resources you can find here\n\nThe code to do this analysis yourself, from scratch here: `data_acquisition.py`.\n\nThe jupyter notebook I actually used when I developed this: `01 - Demonyms and adjectives - why are the french so....ipynb`\n \nNote you'll need to pip install py2store if you haven't already.\n\nIn the `data` folder you'll find\n* country_demonym.p: A pickle of a dataframe of countries and corresponding demonyms\n* country_demonym.xlsx: The same as above, but in excel form\n* demonym_suggested_characteristics.p: A pickle of 73 demonyms and auto-suggestion information, including characteristics. \n* what_we_think_about_demonyns.xlsx: An excel containing various statistics about demonyms and their (perceived) characteristics\n \n\n\n\n\n\n# Agglutinations\n\nInspired from a [tweet](https://twitter.com/raymondh/status/1311003482531401729) from Raymond Hettinger this morning:\n\n_Resist the urge to elide the underscore in multiword function or method names_\n\nSo I wondered...\n\n## Gluglus\n\nThe gluglu of a word is the number of partitions you can make of that word into words (of length at least 2 (so no using a or i)).\n(No \"gluglu\" isn't an actual term -- unless everyone starts using it from now on. \nBut it was inspired from an actual [linguistic term](https://en.wikipedia.org/wiki/Agglutination).)\n\nFor example, the gluglu of ``newspaper`` is 4:\n\n```\nnewspaper\n    new spa per\n    news pa per\n    news paper\n```\n\nEvery (valid) word has gluglu at least 1.\n\n\n## How many standard library names have gluglus at last 2?\n\n108\n\nHere's [the list](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/standard_lib_gluglus.txt) of all of them.\n\nThe winner has a gluglu of 6 (not 7 because formatannotationrelativeto isn't in the dictionary)\n\n```\nformatannotationrelativeto\n\tfor mat an not at ion relative to\n\tfor mat annotation relative to\n\tform at an not at ion relative to\n\tform at annotation relative to\n\tformat an not at ion relative to\n\tformat annotation relative to\n```\n\n## Details\n\n### Dictionary\n\nReally it depends on what dictionary we use. \nHere, I used a very conservative one. \nThe intersection of two lists: The [corncob](http://www.mieliestronk.com/corncob_lowercase.txt) \nand the [google10000](https://raw.githubusercontent.com/first20hours/google-10000-english/master/google-10000-english-usa.txt) word lists.\nAdditionally, I only kept of those, those that had at least 2 letters, and had only letters (no hyphens or disturbing diacritics).\n\nDiacritics. Look it up. Impress your next nerd date.\n\nIm left with 8116 words. You can find them [here](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/words_8116.csv).\n\n### Standard Lib Names\n\nSurprisingly, that was the hardest part. I know I'm missing some, but that's enough rabbit-holing. \n\nWhat I did (modulo some exceptions I won't look into) was to walk the standard lib modules (even that list wasn't a given!) \nextracting (recursively( the names of any (non-underscored) attributes if they were modules or callables, \nas well as extracting the arguments of these callables (when they had signatures).\n\nYou can find the code I used to extract these names [here](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/py_names.py) \nand the actual list [there](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/standard_lib_module_names.csv).\n\n\n\n# covid\n\n## Bar Chart Races (applied to covid-19 spread)\n\nThe module will show is how to make these:\n- Confirmed cases (by country): https://public.flourish.studio/visualisation/1704821/\n- Deaths (by country): https://public.flourish.studio/visualisation/1705644/\n- US Confirmed cases (by state): https://public.flourish.studio/visualisation/1794768/\n- US Deaths (by state): https://public.flourish.studio/visualisation/1794797/\n\n### The script\n\nIf you just want to run this as a script to get the job done, you have one here: \nhttps://raw.githubusercontent.com/thorwhalen/tapyoca/master/covid/covid_bar_chart_race.py\n\nRun like this\n```\n$ python covid_bar_chart_race.py -h\nusage: covid_bar_chart_race.py [-h] {mk-and-save-covid-data,update-covid-data,instructions-to-make-bar-chart-race} ...\n\npositional arguments:\n  {mk-and-save-covid-data,update-covid-data,instructions-to-make-bar-chart-race}\n    mk-and-save-covid-data\n                        :param data_sources: Dirpath or py2store Store where the data is :param kinds: The kinds of data you want to compute and save :param\n                        skip_first_days: :param verbose: :return:\n    update-covid-data   update the coronavirus data\n    instructions-to-make-bar-chart-race\n\noptional arguments:\n  -h, --help            show this help message and exit\n ```\n \n \n### The jupyter notebook\n\nThe notebook (the .ipynb file) shows you how to do it step by step in case you want to reuse the methods for other stuff.\n\n\n\n## Getting and preparing the data\n\nCorona virus data here: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset (direct download: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset/download). It's currently updated daily, so download a fresh copy if you want.\n\nPopulation data here: http://api.worldbank.org/v2/en/indicator/SP.POP.TOTL?downloadformat=csv\n\nIt comes under the form of a zip file (currently named `novel-corona-virus-2019-dataset.zip` with several `.csv` files in them. We use `py2store` (To install: `pip install py2store`. Project lives here: https://github.com/i2mint/py2store) to access and pre-prepare it. It allows us to not have to unzip the file and replace the older folder with it every time we download a new one. It also gives us the csvs as `pandas.DataFrame` already. \n\n\n```python\nimport pandas as pd\nfrom io import BytesIO\nfrom py2store import kv_wrap, ZipReader  # google it and pip install it\nfrom py2store.caching import mk_cached_store\nfrom py2store import QuickPickleStore\nfrom py2store.sources import FuncReader\n\ndef country_flag_image_url():\n    import pandas as pd\n    return pd.read_csv(\n        'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv')\n\ndef kaggle_coronavirus_dataset():\n    import kaggle\n    from io import BytesIO\n    # didn't find the pure binary download function, so using temp dir to emulate\n    from tempfile import mkdtemp  \n    download_dir = mkdtemp()\n    filename = 'novel-corona-virus-2019-dataset.zip'\n    zip_file = os.path.join(download_dir, filename)\n    \n    dataset = 'sudalairajkumar/novel-corona-virus-2019-dataset'\n    kaggle.api.dataset_download_files(dataset, download_dir)\n    with open(zip_file, 'rb') as fp:\n        b = fp.read()\n    return BytesIO(b)\n\ndef city_population_in_time():\n    import pandas as pd\n    return pd.read_csv(\n        'https://gist.githubusercontent.com/johnburnmurdoch/'\n        '4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations'\n    )\n\ndef country_flag_image_url_prep(df: pd.DataFrame):\n    # delete the region col (we don't need it)\n    del df['region']\n    # rewriting a few (not all) of the country names to match those found in kaggle covid data\n    # Note: The list is not complete! Add to it as needed\n    old_and_new = [('USA', 'US'), \n                   ('Iran, Islamic Rep.', 'Iran'), \n                   ('UK', 'United Kingdom'), \n                   ('Korea, Rep.', 'Korea, South')]\n    for old, new in old_and_new:\n        df['country'] = df['country'].replace(old, new)\n\n    return df\n\n\n@kv_wrap.outcoming_vals(lambda x: pd.read_csv(BytesIO(x)))  # this is to format the data as a dataframe\nclass ZippedCsvs(ZipReader):\n    pass\n# equivalent to ZippedCsvs = kv_wrap.outcoming_vals(lambda x: pd.read_csv(BytesIO(x)))(ZipReader)\n```\n\n\n```python\n# Enter here the place you want to cache your data\nmy_local_cache = os.path.expanduser('~/ddir/my_sources')\n```\n\n\n```python\nCachedFuncReader = mk_cached_store(FuncReader, QuickPickleStore(my_local_cache))\n```\n\n\n```python\ndata_sources = CachedFuncReader([country_flag_image_url, \n                                 kaggle_coronavirus_dataset, \n                                 city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ncovid_datasets = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\nlist(covid_datasets)\n```\n\n\n\n\n    ['COVID19_line_list_data.csv',\n     'COVID19_open_line_list.csv',\n     'covid_19_data.csv',\n     'time_series_covid_19_confirmed.csv',\n     'time_series_covid_19_confirmed_US.csv',\n     'time_series_covid_19_deaths.csv',\n     'time_series_covid_19_deaths_US.csv',\n     'time_series_covid_19_recovered.csv']\n\n\n\n\n```python\ncovid_datasets['time_series_covid_19_confirmed.csv'].head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Province/State</th>\n      <th>Country/Region</th>\n      <th>Lat</th>\n      <th>Long</th>\n      <th>1/22/20</th>\n      <th>1/23/20</th>\n      <th>1/24/20</th>\n      <th>1/25/20</th>\n      <th>1/26/20</th>\n      <th>1/27/20</th>\n      <th>...</th>\n      <th>3/24/20</th>\n      <th>3/25/20</th>\n      <th>3/26/20</th>\n      <th>3/27/20</th>\n      <th>3/28/20</th>\n      <th>3/29/20</th>\n      <th>3/30/20</th>\n      <th>3/31/20</th>\n      <th>4/1/20</th>\n      <th>4/2/20</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>Afghanistan</td>\n      <td>33.0000</td>\n      <td>65.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>74</td>\n      <td>84</td>\n      <td>94</td>\n      <td>110</td>\n      <td>110</td>\n      <td>120</td>\n      <td>170</td>\n      <td>174</td>\n      <td>237</td>\n      <td>273</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>Albania</td>\n      <td>41.1533</td>\n      <td>20.1683</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>123</td>\n      <td>146</td>\n      <td>174</td>\n      <td>186</td>\n      <td>197</td>\n      <td>212</td>\n      <td>223</td>\n      <td>243</td>\n      <td>259</td>\n      <td>277</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>Algeria</td>\n      <td>28.0339</td>\n      <td>1.6596</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>264</td>\n      <td>302</td>\n      <td>367</td>\n      <td>409</td>\n      <td>454</td>\n      <td>511</td>\n      <td>584</td>\n      <td>716</td>\n      <td>847</td>\n      <td>986</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>Andorra</td>\n      <td>42.5063</td>\n      <td>1.5218</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>164</td>\n      <td>188</td>\n      <td>224</td>\n      <td>267</td>\n      <td>308</td>\n      <td>334</td>\n      <td>370</td>\n      <td>376</td>\n      <td>390</td>\n      <td>428</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>Angola</td>\n      <td>-11.2027</td>\n      <td>17.8739</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>5</td>\n      <td>7</td>\n      <td>7</td>\n      <td>7</td>\n      <td>8</td>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 76 columns</p>\n</div>\n\n\n\n\n```python\ncountry_flag_image_url = data_sources['country_flag_image_url']\ncountry_flag_image_url.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nfrom IPython.display import Image\nflag_image_url_of_country = country_flag_image_url.set_index('country')['flag_image_url']\nImage(url=flag_image_url_of_country['Australia'])\n```\n\n\n\n\n<img src=\"https://www.countryflags.io/au/flat/64.png\"/>\n\n\n\n### Update coronavirus data\n\n\n```python\n# To update the coronavirus data:\ndef update_covid_data(data_sources):\n    \"\"\"update the coronavirus data\"\"\"\n    if 'kaggle_coronavirus_dataset' in data_sources._caching_store:\n        del data_sources._caching_store['kaggle_coronavirus_dataset']  # delete the cached item\n    _ = data_sources['kaggle_coronavirus_dataset']\n\n# update_covid_data(data_sources)  # uncomment here when you want to update\n```\n\n### Prepare data for flourish upload\n\n\n```python\nimport re\n\ndef print_if_verbose(verbose, *args, **kwargs):\n    if verbose:\n        print(*args, **kwargs)\n        \ndef country_data_for_data_kind(data_sources, kind='confirmed', skip_first_days=0, verbose=False):\n    \"\"\"kind can be 'confirmed', 'deaths', 'confirmed_US', 'confirmed_US', 'recovered'\"\"\"\n    \n    covid_datasets = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\n    \n    df = covid_datasets[f'time_series_covid_19_{kind}.csv']\n    # df = s['time_series_covid_19_deaths.csv']\n    if 'Province/State' in df.columns:\n        df.loc[df['Province/State'].isna(), 'Province/State'] = 'n/a'  # to avoid problems arising from NaNs\n\n    print_if_verbose(verbose, f\"Before data shape: {df.shape}\")\n\n    # drop some columns we don't need\n    p = re.compile('\\d+/\\d+/\\d+')\n\n    assert all(isinstance(x, str) for x in df.columns)\n    date_cols = [x for x in df.columns if p.match(x)]\n    if not kind.endswith('US'):\n        df = df.loc[:, ['Country/Region'] + date_cols]\n        # group countries and sum up the contributions of their states/regions/pargs\n        df['country'] = df.pop('Country/Region')\n        df = df.groupby('country').sum()\n    else:\n        df = df.loc[:, ['Province_State'] + date_cols]\n        df['state'] = df.pop('Province_State')\n        df = df.groupby('state').sum()\n\n    \n    print_if_verbose(verbose, f\"After data shape: {df.shape}\")\n    df = df.iloc[:, skip_first_days:]\n    \n    if not kind.endswith('US'):\n        # Joining with the country image urls and saving as an xls\n        country_image_url = country_flag_image_url_prep(data_sources['country_flag_image_url'])\n        t = df.copy()\n        t.columns = [str(x)[:10] for x in t.columns]\n        t = t.reset_index(drop=False)\n        t = country_image_url.merge(t, how='outer')\n        t = t.set_index('country')\n        df = t\n    else:    \n        pass\n\n    return df\n\n\ndef mk_and_save_country_data_for_data_kind(data_sources, kind='confirmed', skip_first_days=0, verbose=False):\n    t = country_data_for_data_kind(data_sources, kind, skip_first_days, verbose)\n    filepath = f'country_covid_{kind}.xlsx'\n    t.to_excel(filepath)\n    print_if_verbose(verbose, f\"Was saved here: {filepath}\")\n\n```\n\n\n```python\n# for kind in ['confirmed', 'deaths', 'recovered', 'confirmed_US', 'deaths_US']:\nfor kind in ['confirmed', 'deaths', 'recovered', 'confirmed_US', 'deaths_US']:\n    mk_and_save_country_data_for_data_kind(data_sources, kind=kind, skip_first_days=39, verbose=True)\n```\n\n    Before data shape: (262, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_confirmed.xlsx\n    Before data shape: (262, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_deaths.xlsx\n    Before data shape: (248, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_recovered.xlsx\n    Before data shape: (3253, 86)\n    After data shape: (58, 75)\n    Was saved here: country_covid_confirmed_US.xlsx\n    Before data shape: (3253, 87)\n    After data shape: (58, 75)\n    Was saved here: country_covid_deaths_US.xlsx\n\n\n### Upload to Flourish, tune, and publish\n\nGo to https://public.flourish.studio/, get a free account, and play.\n\nGot to https://app.flourish.studio/templates\n\nChoose \"Bar chart race\". At the time of writing this, it was here: https://app.flourish.studio/visualisation/1706060/\n\n... and then play with the settings\n\n\n## Discussion of the methods\n\n\n```python\nfrom py2store import *\nfrom IPython.display import Image\n```\n\n### country flags images\n\nThe manual data prep looks something like this.\n\n\n```python\nimport pandas as pd\n\n# get the csv data from the url\ncountry_image_url_source = \\\n    'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv'\ncountry_image_url = pd.read_csv(country_image_url_source)\n\n# delete the region col (we don't need it)\ndel country_image_url['region']\n\n# rewriting a few (not all) of the country names to match those found in kaggle covid data\n# Note: The list is not complete! Add to it as needed\n# TODO: (Wishful) Using a general smart soft-matching algorithm to do this automatically.\n# TODO:    This could use edit-distance, synonyms, acronym generation, etc.\nold_and_new = [('USA', 'US'), \n               ('Iran, Islamic Rep.', 'Iran'), \n               ('UK', 'United Kingdom'), \n               ('Korea, Rep.', 'Korea, South')]\nfor old, new in old_and_new:\n    country_image_url['country'] = country_image_url['country'].replace(old, new)\n\nimage_url_of_country = country_image_url.set_index('country')['flag_image_url']\n\ncountry_image_url.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nImage(url=image_url_of_country['Australia'])\n```\n\n\n\n\n<img src=\"https://www.countryflags.io/au/flat/64.png\"/>\n\n\n\n### Caching the flag images data\n\nDownloading our data sources every time we need them is not sustainable. What if they're big? What if you're offline or have slow internet (yes, dear future reader, even in the US, during coronavirus times!)?\n\nCaching. A \"cache aside\" read-cache. That's the word. py2store has tools for that (most of which are are caching.py). \n\nSo let's say we're going to have a local folder where we'll store various datas we download. The principle is as follows:\n\n\n```python\nfrom py2store.caching import mk_cached_store\n\nclass TheSource(dict): ...\nthe_cache = {}\nTheCacheSource = mk_cached_store(TheSource, the_cache)\n\nthe_source = TheSource({'green': 'eggs', 'and': 'ham'})\n\nthe_cached_source = TheCacheSource(the_source)\nprint(f\"the_cache: {the_cache}\")\nprint(f\"Getting green...\")\nthe_cached_source['green']\nprint(f\"the_cache: {the_cache}\")\nprint(\"... so the next time the_cached_source will get it's green from that the_cache\")\n```\n\n    the_cache: {}\n    Getting green...\n    the_cache: {'green': 'eggs'}\n    ... so the next time the_cached_source will get it's green from that the_cache\n\n\nBut now, you'll notice a slight problem ahead. What exactly does our source store (or rather reader) looks like? In it's raw form it would take urls as it's keys, and the response of a request as it's value. That store wouldn't have an `__iter__` for sure (unless you're Google). But more to the point here, the `mk_cached_store` tool uses the same key for the source and the cache, and we can't just use the url as is, to be a local file path. \n\nThere's many ways we could solve this. One way is to add a key map layer on the cache store, so externally, it speaks the url key language, but internally it will map that url to a valid local file path. We've been there, we got the T-shirt!\n\nBut what we're going to do is a bit different: We're going to do the key mapping in the source store itself. It seems to make more sense in our context: We have a data source of `name: data` pairs, and if we impose that the name should be a valid file name, we don't need to have a key map in the cache store.\n\nSo let's start by building this `MyDataStore` store. We'll start by defining the functions that get us the data we want. \n\n\n```python\ndef country_flag_image_url():\n    import pandas as pd\n    return pd.read_csv(\n        'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv')\n\ndef kaggle_coronavirus_dataset():\n    import kaggle\n    from io import BytesIO\n    # didn't find the pure binary download function, so using temp dir to emulate\n    from tempfile import mkdtemp  \n    download_dir = mkdtemp()\n    filename = 'novel-corona-virus-2019-dataset.zip'\n    zip_file = os.path.join(download_dir, filename)\n    \n    dataset = 'sudalairajkumar/novel-corona-virus-2019-dataset'\n    kaggle.api.dataset_download_files(dataset, download_dir)\n    with open(zip_file, 'rb') as fp:\n        b = fp.read()\n    return BytesIO(b)\n\ndef city_population_in_time():\n    import pandas as pd\n    return pd.read_csv(\n        'https://gist.githubusercontent.com/johnburnmurdoch/'\n        '4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations'\n    )\n```\n\nNow we can make a store that simply uses these function names as the keys, and their returned value as the values.\n\n\n```python\nfrom py2store.base import KvReader\nfrom functools import lru_cache\n\nclass FuncReader(KvReader):\n    _getitem_cache_size = 999\n    def __init__(self, funcs):\n        # TODO: assert no free arguments (arguments are allowed but must all have defaults)\n        self.funcs = funcs\n        self._func_of_name = {func.__name__: func for func in funcs}\n\n    def __contains__(self, k):\n        return k in self._func_of_name\n    \n    def __iter__(self):\n        yield from self._func_of_name\n        \n    def __len__(self):\n        return len(self._func_of_name)\n\n    @lru_cache(maxsize=_getitem_cache_size)\n    def __getitem__(self, k):\n        return self._func_of_name[k]()  # call the func\n    \n    def __hash__(self):\n        return 1\n    \n```\n\n\n```python\ndata_sources = FuncReader([country_flag_image_url, kaggle_coronavirus_dataset, city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['city_population_in_time']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>group</th>\n      <th>year</th>\n      <th>value</th>\n      <th>subGroup</th>\n      <th>city_id</th>\n      <th>lastValue</th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1575</td>\n      <td>200.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1576</td>\n      <td>212.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1577</td>\n      <td>224.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>212.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1578</td>\n      <td>236.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>224.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1579</td>\n      <td>248.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>236.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6247</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1561</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6248</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1562</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6249</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1563</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6250</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1564</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6251</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1565</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n  </tbody>\n</table>\n<p>6252 rows \u00d7 9 columns</p>\n</div>\n\n\n\nBut we wanted this all to be cached locally, right? So a few more lines to do that!\n\n\n```python\nfrom py2store.caching import mk_cached_store\nfrom py2store import QuickPickleStore\n    \nmy_local_cache = os.path.expanduser('~/ddir/my_sources')\n\nCachedFuncReader = mk_cached_store(FuncReader, QuickPickleStore(my_local_cache))\n```\n\n\n```python\ndata_sources = CachedFuncReader([country_flag_image_url, kaggle_coronavirus_dataset, city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['city_population_in_time']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>group</th>\n      <th>year</th>\n      <th>value</th>\n      <th>subGroup</th>\n      <th>city_id</th>\n      <th>lastValue</th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1575</td>\n      <td>200.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1576</td>\n      <td>212.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1577</td>\n      <td>224.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>212.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1578</td>\n      <td>236.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>224.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1579</td>\n      <td>248.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>236.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6247</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1561</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6248</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1562</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6249</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1563</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6250</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1564</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6251</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1565</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n  </tbody>\n</table>\n<p>6252 rows \u00d7 9 columns</p>\n</div>\n\n\n\n\n```python\nz = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\nlist(z)\n```\n",
  "long_description_content_type": "text/markdown",
  "description_file": "README.md",
  "root_url": "https://github.com/thorwhalen",
  "description": "A medley of things that got coded because there was an itch to do so",
  "author": "thorwhalen",
  "license": "Apache Software License",
  "description-file": "README.md",
  "install_requires": [],
  "keywords": [
    "documentation",
    "packaging",
    "publishing"
  ]
}
--------------------------------------------------------------------
running dist_info
writing tapyoca.egg-info/PKG-INFO
writing dependency_links to tapyoca.egg-info/dependency_links.txt
writing top-level names to tapyoca.egg-info/top_level.txt
reading manifest file 'tapyoca.egg-info/SOURCES.txt'
adding license file 'LICENSE'
writing manifest file 'tapyoca.egg-info/SOURCES.txt'
creating '/builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/tapyoca-0.0.4.dist-info'
+ cat /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-buildrequires
+ rm -rfv tapyoca-0.0.4.dist-info/
removed 'tapyoca-0.0.4.dist-info/top_level.txt'
removed 'tapyoca-0.0.4.dist-info/METADATA'
removed 'tapyoca-0.0.4.dist-info/LICENSE'
removed directory 'tapyoca-0.0.4.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Wrote: /builddir/build/SRPMS/python-tapyoca-0.0.4-1.fc41.buildreqs.nosrc.rpm
INFO: Going to install missing dynamic buildrequires
Updating and loading repositories:
 fedora                                 100% | 544.4 KiB/s |  30.5 KiB |  00m00s
 updates                                100% | 535.6 KiB/s |  28.9 KiB |  00m00s
 Copr repository                        100% | 128.3 KiB/s |   1.5 KiB |  00m00s
Repositories loaded.
Package "pyproject-rpm-macros-1.17.0-1.fc41.noarch" is already installed.
Package "python3-devel-3.13.2-1.fc41.x86_64" is already installed.
Package "python3-packaging-24.2-3.fc41.noarch" is already installed.
Package "python3-pip-24.2-1.fc41.noarch" is already installed.
Package "python3-setuptools-69.2.0-8.fc41.noarch" is already installed.
Package "python3-wheel-1:0.43.0-4.fc41.noarch" is already installed.

Nothing to do.
Building target platforms: x86_64
Building for target x86_64
setting SOURCE_DATE_EPOCH=1740787200
Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.5hGN9A
+ umask 022
+ cd /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ cd tapyoca-0.0.4
+ echo pyproject-rpm-macros
+ echo python3-devel
+ echo 'python3dist(packaging)'
+ echo 'python3dist(pip) >= 19'
+ '[' -f pyproject.toml ']'
+ '[' -f setup.py ']'
+ echo 'python3dist(setuptools) >= 40.8'
+ rm -rfv '*.dist-info/'
+ '[' -f /usr/bin/python3 ']'
+ mkdir -p /builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/.pyproject-builddir
+ echo -n
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ VALAFLAGS=-g
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes --cap-lints=warn'
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1  '
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ CC=gcc
+ CXX=g++
+ TMPDIR=/builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/.pyproject-builddir
+ RPM_TOXENV=py313
+ HOSTNAME=rpmbuild
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/pyproject-wheeldir --output /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-buildrequires
Handling setuptools >= 40.8 from default build backend
Requirement satisfied: setuptools >= 40.8
   (installed: setuptools 69.2.0)
!!!! containing_folder_name=tapyoca-0.0.4 but setup name is tapyoca
Setup params -------------------------------------------------------
{
  "name": "tapyoca",
  "version": "0.0.4",
  "url": "https://github.com/thorwhalen/tapyoca",
  "packages": [
    "tapyoca",
    "tapyoca.agglutination",
    "tapyoca.covid",
    "tapyoca.darpa",
    "tapyoca.demonyms",
    "tapyoca.indexing_podcasts",
    "tapyoca.parquet_deformations",
    "tapyoca.phoneming"
  ],
  "include_package_data": true,
  "platforms": "any",
  "long_description": "# tapyoca\nA medley of small projects\n\n\n# parquet_deformations\n\nI'm calling these [Parquet deformations](https://www.theguardian.com/artanddesign/alexs-adventures-in-numberland/2014/sep/09/crazy-paving-the-twisted-world-of-parquet-deformations#:~:text=In%20the%201960s%20an%20American,the%20regularity%20of%20the%20tiling.) but purest would lynch me. \n\nReally, I just wanted to transform one word into another word, gradually, as I've seen in some of [Escher's](https://en.wikipedia.org/wiki/M._C._Escher) work, so I looked it up, and saw that it's called parquet deformations. The math looked enticing, but I had no time for that, so I did the first way I could think of: Mapping pixels to pixels (in some fashion -- but nearest neighbors is the method that yields nicest results, under the pixel-level restriction). \n\nOf course, this can be applied to any image (that will be transformed to B/W (not even gray -- I mean actual B/W), and there's several ways you can perform the parquet (I like the gif rendering). \n\nThe main function (exposed as a script) is `mk_deformation_image`. All you need is to specify two images (or words). If you want, of course, you can specify:\n- `n_steps`: Number of steps from start to end image\n- `save_to_file`: path to file to save too (if not given, will just return the image object)\n- `kind`: 'gif', 'horizontal_stack', or 'vertical_stack'\n- `coordinate_mapping_maker`: A function that will return the mapping between start and end. \nThis function should return a pair (`from_coord`, `to_coord`) of aligned matrices whose 2 columns are the the \n`(x, y)` coordinates, and the rows represent aligned positions that should be mapped. \n\n\n\n## Examples\n\n### Two words...\n\n\n```python\nfit_to_size = 400\nstart_im = image_of_text('sensor').rotate(90, expand=1)\nend_im = image_of_text('meaning').rotate(90, expand=1)\nstart_and_end_image(start_im, end_im)\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_5_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 15, kind='h').resize((500,200))\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_6_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im.transpose(4), end_im.transpose(4), 5, kind='v').resize((200,200))\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_7_0.png)\n\n\n\n\n```python\nf = 'sensor_meaning_knn.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_knn.gif?76128495\">\n\n\n\n\n```python\nf = 'sensor_meaning_scan.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f, \n                     coordinate_mapping_maker='scan')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_scan.gif?76996026\">\n\n\n\n\n```python\nf = 'sensor_meaning_random.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f, \n                     coordinate_mapping_maker='random')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_random.gif?80233280\">\n\n\n\n### From a list of words\n\n\n```python\nstart_words = ['sensor', 'vibration', 'tempature']\nend_words = ['sense', 'meaning', 'detection']\nstart_im, end_im = make_start_and_end_images_with_words(\n    start_words, end_words, perm=True, repeat=2, size=150)\nstart_and_end_image(start_im, end_im).resize((600, 200))\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_12_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 5)\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_13_0.png)\n\n\n\n\n```python\nf = 'bunch_of_words.gif'\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"bunch_of_words.gif?7402792\">\n\n\n\n## From files\n\n\n```python\nstart_im = Image.open('sensor_strip_01.png')\nend_im = Image.open('sense_strip_01.png')\nstart_and_end_image(start_im.resize((200, 500)), end_im.resize((200, 500)))\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_16_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 7)\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_17_0.png)\n\n\n\n\n```python\nf = 'medley.gif'\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"medley.gif?39255021\">\n\n\n\n\n```python\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f, coordinate_mapping_maker='scan')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning.gif?41172115\">\n\n\n\n## an image and some text\n\n\n```python\nstart_im = 'img/waveform_01.png'  # will first look for a file, and if not consider as text\nend_im = 'makes sense'\n\nmk_gif_of_deformations(start_im, end_im, n_steps=20, \n                               save_to_file='image_and_text.gif')\ndisplay_gif('image_and_text.gif')  \n```\n\n\n\n\n<img src=\"image_and_text.gif?92524789\">\n\n\n\n\n\n\n# demonys\n\n## What do we think about other peoples?\n\nThis project is meant to get an idea of what people think of people for different nations, as seen by what they ask google about them. \n\nHere I use python code to acquire, clean up, and analyze the data. \n\n### Demonym\n\nIf you're like me and enjoy the false and fleeting impression of superiority that comes when you know a word someone else doesn't. If you're like me and go to parties for the sole purpose of seeking victims to get a one-up on, here's a cool word to add to your arsenal:\n\n**demonym**: a noun used to denote the natives or inhabitants of a particular country, state, city, etc.\n_\"he struggled for the correct demonym for the people of Manchester\"_\n\n### Back-story of this analysis\n \nDuring a discussion (about traveling in Europe) someone said \"why are the swiss so miserable\". Now, I wouldn't say that the swiss were especially miserable (a couple of ex-girlfriends aside), but to be fair he was contrasting with Italians, so perhaps he has a point. I apologize if you are swiss, or one of the two ex-girlfriends -- nothing personal, this is all for effect. \n\nWe googled \"why are the swiss so \", and sure enough, \"why are the swiss so miserable\" came up as one of the suggestions. So we got curious and started googling other peoples: the French, the Germans, etc.\n\nThat's the back-story of this analysis. This analysis is meant to get an idea of what we think of peoples from other countries. Of course, one can rightfully critique the approach I'll take to gauge \"what we think\" -- all three of these words should, but will not, be defined. I'm just going to see what google's *current* auto-suggest comes back with when I enter \"why are the X so \" (where X will be a noun that denotes the natives of inhabitants of a particular country; a *demonym* if you will). \n\n### Warning\n\nAgain, word of warning: All data and analyses are biased. \nTake everything you'll read here (and to be fair, what you read anywhere) with a grain of salt. \nFor simplicitly I'll saying things like \"what we think of...\" or \"who do we most...\", etc.\nBut I don't **really** mean that.\n\n### Resources\n\n* http://www.geography-site.co.uk/pages/countries/demonyms.html for my list of demonyms.\n* google for my suggestion engine, using the url prefix: `http://suggestqueries.google.com/complete/search?client=chrome&q=`\n\n\n## The results\n\n### In a nutshell\n\nBelow is listed 73 demonyms along with words extracted from the very first google suggestion when you type. \n\n`why are the DEMONYM so `\n\n```text\nafghan    \t                eyes beautiful\nalbanian  \t                     beautiful\namerican  \t          girl dolls expensive\naustralian\t                          tall\nbelgian   \t                    fries good\nbhutanese \t                         happy\nbrazilian \t              good at football\nbritish   \t     full of grief and despair\nbulgarian \t              properties cheap\nburmese   \t             cats affectionate\ncambodian \t                   cows skinny\ncanadian  \t                          nice\nchinese   \t                       healthy\ncolombian \t                  avocados big\ncuban     \t                   cigars good\nczech     \t                          tall\ndominican \t  republic and haiti different\negyptian  \t                gods important\nenglish   \t                      reserved\neritrean  \t                     beautiful\nethiopian \t                     beautiful\nfilipino  \t                         proud\nfinn      \t               shoes expensive\nfrench    \t                       healthy\ngerman    \t                          tall\ngreek     \t                gods messed up\nhaitian   \t                parents strict\nhungarian \t                    words long\nindian    \t            tv debates chaotic\nindonesian\t                         smart\niranian   \t                     beautiful\nisraeli   \t           startups successful\nitalian   \t                         short\njamaican  \t                sprinters fast\njapanese  \t                        polite\nkenyan    \t                  runners good\nlebanese  \t                          rich\nmalagasy  \t                    names long\nmalaysian \t                   drivers bad\nmaltese   \t                          rude\nmongolian \t                  horses small\nmoroccan  \t                rugs expensive\nnepalese  \t                     beautiful\nnigerian  \t                          tall\nnorth korean\t                      hats big\nnorwegian \t                 flights cheap\npakistani \t                          fair\nperuvian  \t               blueberries big\npole      \t                  vaulters hot\nportuguese\t                         short\npuerto rican\t       and cuban flags similar\nromanian  \t                     beautiful\nrussian   \t                  good at math\nsamoan    \t                           big\nsaudi     \t                      arrogant\nscottish  \t                        bitter\nsenegalese\t                          tall\nserbian   \t                          tall\nsingaporean\t                          rude\nsomali    \t                parents strict\nsouth african\t                     plugs big\nsouth korean\t                          tall\nsri lankan\t                          dark\nsudanese  \t                          tall\nswiss     \t        good at making watches\nsyrian    \t                families large\ntaiwanese \t                        pretty\nthai      \t                        pretty\ntongan    \t                           big\nukrainian \t                     beautiful\nvietnamese\t        fiercely nationalistic\nwelsh     \t                          dark\nzambian   \t                emeralds cheap\n```\n\n\nNotes:\n* The queries actually have a space after the \"so\", which matters so as to omit suggestions containing words that start with so.\n* Only the tail of the suggestion is shown -- minus prefix (`why are the DEMONYM` or `why are DEMONYM`) as well as the `so`, where ever it lands in the suggestion. \nFor example, the first suggestion for the american demonym was \"why are american dolls so expensive\", which results in the \"dolls expensive\" association. \n\n\n### Who do we most talk/ask about?\n\nThe original list contained 217 demonyms, but many of these yielded no suggestions (to the specific query format I used, that is). \nOnly 73 demonyms gave me at least one suggestion. \nBut within those, number of suggestions range between 1 and 20 (which is probably the default maximum number of suggestions for the API I used). \nSo, pretending that the number of suggestions is an indicator of how much we have to say, or how many different opinions we have, of each of the covered nationalities, \nhere's the top 15 demonyms people talk about, with the corresponding number of suggestions \n(proxy for \"the number of different things people ask about the said nationality). \n\n```text\nfrench         20\nsingaporean    20\ngerman         20\nbritish        20\nswiss          20\nenglish        19\nitalian        18\ncuban          18\ncanadian       18\nwelsh          18\naustralian     17\nmaltese        16\namerican       16\njapanese       14\nscottish       14\n```\n\n### Who do we least talk/ask about?\n\nConversely, here are the 19 demonyms that came back with only one suggestion.\n\n```text\nsomali          1\nbhutanese       1\nsyrian          1\ntongan          1\ncambodian       1\nmalagasy        1\nsaudi           1\nserbian         1\nczech           1\neritrean        1\nfinn            1\npuerto rican    1\npole            1\nhaitian         1\nhungarian       1\nperuvian        1\nmoroccan        1\nmongolian       1\nzambian         1\n```\n\n### What do we think about people?\n\nWhy are the French so...\n\nHow would you (if you're (un)lucky enough to know the French) finish this sentence?\nYou might even have several opinions about the French, and any other group of people you've rubbed shoulders with.\nWhat words would your palette contain to describe different nationalities?\nWhat words would others (at least those that ask questions to google) use?\n\nWell, here's what my auto-suggest search gave me. A set of 357 unique words and expressions to describe the 72 nationalities. \nSo a long tail of words use only for one nationality. But some words occur for more than one nationality. \nHere are the top 12 words/expressions used to describe people of the world. \n\n```text\nbeautiful         11\ntall              11\nshort              9\nnames long         8\nproud              8\nparents strict     8\nsmart              8\nnice               7\nboring             6\nrich               5\ndark               5\nsuccessful         5\n```\n\n### Who is beautiful? Who is tall? Who is short? Who is smart?\n\n```text\nbeautiful      : albanian, eritrean, ethiopian, filipino, iranian, lebanese, nepalese, pakistani, romanian, ukrainian, vietnamese\ntall           : australian, czech, german, nigerian, pakistani, samoan, senegalese, serbian, south korean, sudanese, taiwanese\nshort          : filipino, indonesian, italian, maltese, nepalese, pakistani, portuguese, singaporean, welsh\nnames long     : indian, malagasy, nigerian, portuguese, russian, sri lankan, thai, welsh\nproud          : albanian, ethiopian, filipino, iranian, lebanese, portuguese, scottish, welsh\nparents strict : albanian, ethiopian, haitian, indian, lebanese, pakistani, somali, sri lankan\nsmart          : indonesian, iranian, lebanese, pakistani, romanian, singaporean, taiwanese, vietnamese\nnice           : canadian, english, filipino, nepalese, portuguese, taiwanese, thai\nboring         : british, english, french, german, singaporean, swiss\nrich           : lebanese, pakistani, singaporean, taiwanese, vietnamese\ndark           : filipino, senegalese, sri lankan, vietnamese, welsh\nsuccessful     : chinese, english, japanese, lebanese, swiss\n```\n\n## How did I do it?\n\nI scraped a list of (country, demonym) pairs from a table in http://www.geography-site.co.uk/pages/countries/demonyms.html.\n\nThen I diagnosed these and manually made a mapping to simplify some \"complex\" entries, \nsuch as mapping an entry such as \"Irishman or Irishwoman or Irish\" to \"Irish\".\n\nUsing the google suggest API (http://suggestqueries.google.com/complete/search?client=chrome&q=), I requested what the suggestions \nfor `why are the $demonym so ` query pattern, for `$demonym` running through all 217 demonyms from the list above, \nstoring the results for each if the results were non-empty. \n\nThen, it was just a matter of pulling this data into memory, formatting it a bit, and creating a pandas dataframe that I could then interrogate.\n \n## Resources you can find here\n\nThe code to do this analysis yourself, from scratch here: `data_acquisition.py`.\n\nThe jupyter notebook I actually used when I developed this: `01 - Demonyms and adjectives - why are the french so....ipynb`\n \nNote you'll need to pip install py2store if you haven't already.\n\nIn the `data` folder you'll find\n* country_demonym.p: A pickle of a dataframe of countries and corresponding demonyms\n* country_demonym.xlsx: The same as above, but in excel form\n* demonym_suggested_characteristics.p: A pickle of 73 demonyms and auto-suggestion information, including characteristics. \n* what_we_think_about_demonyns.xlsx: An excel containing various statistics about demonyms and their (perceived) characteristics\n \n\n\n\n\n\n# Agglutinations\n\nInspired from a [tweet](https://twitter.com/raymondh/status/1311003482531401729) from Raymond Hettinger this morning:\n\n_Resist the urge to elide the underscore in multiword function or method names_\n\nSo I wondered...\n\n## Gluglus\n\nThe gluglu of a word is the number of partitions you can make of that word into words (of length at least 2 (so no using a or i)).\n(No \"gluglu\" isn't an actual term -- unless everyone starts using it from now on. \nBut it was inspired from an actual [linguistic term](https://en.wikipedia.org/wiki/Agglutination).)\n\nFor example, the gluglu of ``newspaper`` is 4:\n\n```\nnewspaper\n    new spa per\n    news pa per\n    news paper\n```\n\nEvery (valid) word has gluglu at least 1.\n\n\n## How many standard library names have gluglus at last 2?\n\n108\n\nHere's [the list](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/standard_lib_gluglus.txt) of all of them.\n\nThe winner has a gluglu of 6 (not 7 because formatannotationrelativeto isn't in the dictionary)\n\n```\nformatannotationrelativeto\n\tfor mat an not at ion relative to\n\tfor mat annotation relative to\n\tform at an not at ion relative to\n\tform at annotation relative to\n\tformat an not at ion relative to\n\tformat annotation relative to\n```\n\n## Details\n\n### Dictionary\n\nReally it depends on what dictionary we use. \nHere, I used a very conservative one. \nThe intersection of two lists: The [corncob](http://www.mieliestronk.com/corncob_lowercase.txt) \nand the [google10000](https://raw.githubusercontent.com/first20hours/google-10000-english/master/google-10000-english-usa.txt) word lists.\nAdditionally, I only kept of those, those that had at least 2 letters, and had only letters (no hyphens or disturbing diacritics).\n\nDiacritics. Look it up. Impress your next nerd date.\n\nIm left with 8116 words. You can find them [here](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/words_8116.csv).\n\n### Standard Lib Names\n\nSurprisingly, that was the hardest part. I know I'm missing some, but that's enough rabbit-holing. \n\nWhat I did (modulo some exceptions I won't look into) was to walk the standard lib modules (even that list wasn't a given!) \nextracting (recursively( the names of any (non-underscored) attributes if they were modules or callables, \nas well as extracting the arguments of these callables (when they had signatures).\n\nYou can find the code I used to extract these names [here](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/py_names.py) \nand the actual list [there](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/standard_lib_module_names.csv).\n\n\n\n# covid\n\n## Bar Chart Races (applied to covid-19 spread)\n\nThe module will show is how to make these:\n- Confirmed cases (by country): https://public.flourish.studio/visualisation/1704821/\n- Deaths (by country): https://public.flourish.studio/visualisation/1705644/\n- US Confirmed cases (by state): https://public.flourish.studio/visualisation/1794768/\n- US Deaths (by state): https://public.flourish.studio/visualisation/1794797/\n\n### The script\n\nIf you just want to run this as a script to get the job done, you have one here: \nhttps://raw.githubusercontent.com/thorwhalen/tapyoca/master/covid/covid_bar_chart_race.py\n\nRun like this\n```\n$ python covid_bar_chart_race.py -h\nusage: covid_bar_chart_race.py [-h] {mk-and-save-covid-data,update-covid-data,instructions-to-make-bar-chart-race} ...\n\npositional arguments:\n  {mk-and-save-covid-data,update-covid-data,instructions-to-make-bar-chart-race}\n    mk-and-save-covid-data\n                        :param data_sources: Dirpath or py2store Store where the data is :param kinds: The kinds of data you want to compute and save :param\n                        skip_first_days: :param verbose: :return:\n    update-covid-data   update the coronavirus data\n    instructions-to-make-bar-chart-race\n\noptional arguments:\n  -h, --help            show this help message and exit\n ```\n \n \n### The jupyter notebook\n\nThe notebook (the .ipynb file) shows you how to do it step by step in case you want to reuse the methods for other stuff.\n\n\n\n## Getting and preparing the data\n\nCorona virus data here: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset (direct download: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset/download). It's currently updated daily, so download a fresh copy if you want.\n\nPopulation data here: http://api.worldbank.org/v2/en/indicator/SP.POP.TOTL?downloadformat=csv\n\nIt comes under the form of a zip file (currently named `novel-corona-virus-2019-dataset.zip` with several `.csv` files in them. We use `py2store` (To install: `pip install py2store`. Project lives here: https://github.com/i2mint/py2store) to access and pre-prepare it. It allows us to not have to unzip the file and replace the older folder with it every time we download a new one. It also gives us the csvs as `pandas.DataFrame` already. \n\n\n```python\nimport pandas as pd\nfrom io import BytesIO\nfrom py2store import kv_wrap, ZipReader  # google it and pip install it\nfrom py2store.caching import mk_cached_store\nfrom py2store import QuickPickleStore\nfrom py2store.sources import FuncReader\n\ndef country_flag_image_url():\n    import pandas as pd\n    return pd.read_csv(\n        'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv')\n\ndef kaggle_coronavirus_dataset():\n    import kaggle\n    from io import BytesIO\n    # didn't find the pure binary download function, so using temp dir to emulate\n    from tempfile import mkdtemp  \n    download_dir = mkdtemp()\n    filename = 'novel-corona-virus-2019-dataset.zip'\n    zip_file = os.path.join(download_dir, filename)\n    \n    dataset = 'sudalairajkumar/novel-corona-virus-2019-dataset'\n    kaggle.api.dataset_download_files(dataset, download_dir)\n    with open(zip_file, 'rb') as fp:\n        b = fp.read()\n    return BytesIO(b)\n\ndef city_population_in_time():\n    import pandas as pd\n    return pd.read_csv(\n        'https://gist.githubusercontent.com/johnburnmurdoch/'\n        '4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations'\n    )\n\ndef country_flag_image_url_prep(df: pd.DataFrame):\n    # delete the region col (we don't need it)\n    del df['region']\n    # rewriting a few (not all) of the country names to match those found in kaggle covid data\n    # Note: The list is not complete! Add to it as needed\n    old_and_new = [('USA', 'US'), \n                   ('Iran, Islamic Rep.', 'Iran'), \n                   ('UK', 'United Kingdom'), \n                   ('Korea, Rep.', 'Korea, South')]\n    for old, new in old_and_new:\n        df['country'] = df['country'].replace(old, new)\n\n    return df\n\n\n@kv_wrap.outcoming_vals(lambda x: pd.read_csv(BytesIO(x)))  # this is to format the data as a dataframe\nclass ZippedCsvs(ZipReader):\n    pass\n# equivalent to ZippedCsvs = kv_wrap.outcoming_vals(lambda x: pd.read_csv(BytesIO(x)))(ZipReader)\n```\n\n\n```python\n# Enter here the place you want to cache your data\nmy_local_cache = os.path.expanduser('~/ddir/my_sources')\n```\n\n\n```python\nCachedFuncReader = mk_cached_store(FuncReader, QuickPickleStore(my_local_cache))\n```\n\n\n```python\ndata_sources = CachedFuncReader([country_flag_image_url, \n                                 kaggle_coronavirus_dataset, \n                                 city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ncovid_datasets = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\nlist(covid_datasets)\n```\n\n\n\n\n    ['COVID19_line_list_data.csv',\n     'COVID19_open_line_list.csv',\n     'covid_19_data.csv',\n     'time_series_covid_19_confirmed.csv',\n     'time_series_covid_19_confirmed_US.csv',\n     'time_series_covid_19_deaths.csv',\n     'time_series_covid_19_deaths_US.csv',\n     'time_series_covid_19_recovered.csv']\n\n\n\n\n```python\ncovid_datasets['time_series_covid_19_confirmed.csv'].head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Province/State</th>\n      <th>Country/Region</th>\n      <th>Lat</th>\n      <th>Long</th>\n      <th>1/22/20</th>\n      <th>1/23/20</th>\n      <th>1/24/20</th>\n      <th>1/25/20</th>\n      <th>1/26/20</th>\n      <th>1/27/20</th>\n      <th>...</th>\n      <th>3/24/20</th>\n      <th>3/25/20</th>\n      <th>3/26/20</th>\n      <th>3/27/20</th>\n      <th>3/28/20</th>\n      <th>3/29/20</th>\n      <th>3/30/20</th>\n      <th>3/31/20</th>\n      <th>4/1/20</th>\n      <th>4/2/20</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>Afghanistan</td>\n      <td>33.0000</td>\n      <td>65.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>74</td>\n      <td>84</td>\n      <td>94</td>\n      <td>110</td>\n      <td>110</td>\n      <td>120</td>\n      <td>170</td>\n      <td>174</td>\n      <td>237</td>\n      <td>273</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>Albania</td>\n      <td>41.1533</td>\n      <td>20.1683</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>123</td>\n      <td>146</td>\n      <td>174</td>\n      <td>186</td>\n      <td>197</td>\n      <td>212</td>\n      <td>223</td>\n      <td>243</td>\n      <td>259</td>\n      <td>277</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>Algeria</td>\n      <td>28.0339</td>\n      <td>1.6596</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>264</td>\n      <td>302</td>\n      <td>367</td>\n      <td>409</td>\n      <td>454</td>\n      <td>511</td>\n      <td>584</td>\n      <td>716</td>\n      <td>847</td>\n      <td>986</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>Andorra</td>\n      <td>42.5063</td>\n      <td>1.5218</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>164</td>\n      <td>188</td>\n      <td>224</td>\n      <td>267</td>\n      <td>308</td>\n      <td>334</td>\n      <td>370</td>\n      <td>376</td>\n      <td>390</td>\n      <td>428</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>Angola</td>\n      <td>-11.2027</td>\n      <td>17.8739</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>5</td>\n      <td>7</td>\n      <td>7</td>\n      <td>7</td>\n      <td>8</td>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 76 columns</p>\n</div>\n\n\n\n\n```python\ncountry_flag_image_url = data_sources['country_flag_image_url']\ncountry_flag_image_url.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nfrom IPython.display import Image\nflag_image_url_of_country = country_flag_image_url.set_index('country')['flag_image_url']\nImage(url=flag_image_url_of_country['Australia'])\n```\n\n\n\n\n<img src=\"https://www.countryflags.io/au/flat/64.png\"/>\n\n\n\n### Update coronavirus data\n\n\n```python\n# To update the coronavirus data:\ndef update_covid_data(data_sources):\n    \"\"\"update the coronavirus data\"\"\"\n    if 'kaggle_coronavirus_dataset' in data_sources._caching_store:\n        del data_sources._caching_store['kaggle_coronavirus_dataset']  # delete the cached item\n    _ = data_sources['kaggle_coronavirus_dataset']\n\n# update_covid_data(data_sources)  # uncomment here when you want to update\n```\n\n### Prepare data for flourish upload\n\n\n```python\nimport re\n\ndef print_if_verbose(verbose, *args, **kwargs):\n    if verbose:\n        print(*args, **kwargs)\n        \ndef country_data_for_data_kind(data_sources, kind='confirmed', skip_first_days=0, verbose=False):\n    \"\"\"kind can be 'confirmed', 'deaths', 'confirmed_US', 'confirmed_US', 'recovered'\"\"\"\n    \n    covid_datasets = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\n    \n    df = covid_datasets[f'time_series_covid_19_{kind}.csv']\n    # df = s['time_series_covid_19_deaths.csv']\n    if 'Province/State' in df.columns:\n        df.loc[df['Province/State'].isna(), 'Province/State'] = 'n/a'  # to avoid problems arising from NaNs\n\n    print_if_verbose(verbose, f\"Before data shape: {df.shape}\")\n\n    # drop some columns we don't need\n    p = re.compile('\\d+/\\d+/\\d+')\n\n    assert all(isinstance(x, str) for x in df.columns)\n    date_cols = [x for x in df.columns if p.match(x)]\n    if not kind.endswith('US'):\n        df = df.loc[:, ['Country/Region'] + date_cols]\n        # group countries and sum up the contributions of their states/regions/pargs\n        df['country'] = df.pop('Country/Region')\n        df = df.groupby('country').sum()\n    else:\n        df = df.loc[:, ['Province_State'] + date_cols]\n        df['state'] = df.pop('Province_State')\n        df = df.groupby('state').sum()\n\n    \n    print_if_verbose(verbose, f\"After data shape: {df.shape}\")\n    df = df.iloc[:, skip_first_days:]\n    \n    if not kind.endswith('US'):\n        # Joining with the country image urls and saving as an xls\n        country_image_url = country_flag_image_url_prep(data_sources['country_flag_image_url'])\n        t = df.copy()\n        t.columns = [str(x)[:10] for x in t.columns]\n        t = t.reset_index(drop=False)\n        t = country_image_url.merge(t, how='outer')\n        t = t.set_index('country')\n        df = t\n    else:    \n        pass\n\n    return df\n\n\ndef mk_and_save_country_data_for_data_kind(data_sources, kind='confirmed', skip_first_days=0, verbose=False):\n    t = country_data_for_data_kind(data_sources, kind, skip_first_days, verbose)\n    filepath = f'country_covid_{kind}.xlsx'\n    t.to_excel(filepath)\n    print_if_verbose(verbose, f\"Was saved here: {filepath}\")\n\n```\n\n\n```python\n# for kind in ['confirmed', 'deaths', 'recovered', 'confirmed_US', 'deaths_US']:\nfor kind in ['confirmed', 'deaths', 'recovered', 'confirmed_US', 'deaths_US']:\n    mk_and_save_country_data_for_data_kind(data_sources, kind=kind, skip_first_days=39, verbose=True)\n```\n\n    Before data shape: (262, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_confirmed.xlsx\n    Before data shape: (262, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_deaths.xlsx\n    Before data shape: (248, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_recovered.xlsx\n    Before data shape: (3253, 86)\n    After data shape: (58, 75)\n    Was saved here: country_covid_confirmed_US.xlsx\n    Before data shape: (3253, 87)\n    After data shape: (58, 75)\n    Was saved here: country_covid_deaths_US.xlsx\n\n\n### Upload to Flourish, tune, and publish\n\nGo to https://public.flourish.studio/, get a free account, and play.\n\nGot to https://app.flourish.studio/templates\n\nChoose \"Bar chart race\". At the time of writing this, it was here: https://app.flourish.studio/visualisation/1706060/\n\n... and then play with the settings\n\n\n## Discussion of the methods\n\n\n```python\nfrom py2store import *\nfrom IPython.display import Image\n```\n\n### country flags images\n\nThe manual data prep looks something like this.\n\n\n```python\nimport pandas as pd\n\n# get the csv data from the url\ncountry_image_url_source = \\\n    'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv'\ncountry_image_url = pd.read_csv(country_image_url_source)\n\n# delete the region col (we don't need it)\ndel country_image_url['region']\n\n# rewriting a few (not all) of the country names to match those found in kaggle covid data\n# Note: The list is not complete! Add to it as needed\n# TODO: (Wishful) Using a general smart soft-matching algorithm to do this automatically.\n# TODO:    This could use edit-distance, synonyms, acronym generation, etc.\nold_and_new = [('USA', 'US'), \n               ('Iran, Islamic Rep.', 'Iran'), \n               ('UK', 'United Kingdom'), \n               ('Korea, Rep.', 'Korea, South')]\nfor old, new in old_and_new:\n    country_image_url['country'] = country_image_url['country'].replace(old, new)\n\nimage_url_of_country = country_image_url.set_index('country')['flag_image_url']\n\ncountry_image_url.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nImage(url=image_url_of_country['Australia'])\n```\n\n\n\n\n<img src=\"https://www.countryflags.io/au/flat/64.png\"/>\n\n\n\n### Caching the flag images data\n\nDownloading our data sources every time we need them is not sustainable. What if they're big? What if you're offline or have slow internet (yes, dear future reader, even in the US, during coronavirus times!)?\n\nCaching. A \"cache aside\" read-cache. That's the word. py2store has tools for that (most of which are are caching.py). \n\nSo let's say we're going to have a local folder where we'll store various datas we download. The principle is as follows:\n\n\n```python\nfrom py2store.caching import mk_cached_store\n\nclass TheSource(dict): ...\nthe_cache = {}\nTheCacheSource = mk_cached_store(TheSource, the_cache)\n\nthe_source = TheSource({'green': 'eggs', 'and': 'ham'})\n\nthe_cached_source = TheCacheSource(the_source)\nprint(f\"the_cache: {the_cache}\")\nprint(f\"Getting green...\")\nthe_cached_source['green']\nprint(f\"the_cache: {the_cache}\")\nprint(\"... so the next time the_cached_source will get it's green from that the_cache\")\n```\n\n    the_cache: {}\n    Getting green...\n    the_cache: {'green': 'eggs'}\n    ... so the next time the_cached_source will get it's green from that the_cache\n\n\nBut now, you'll notice a slight problem ahead. What exactly does our source store (or rather reader) looks like? In it's raw form it would take urls as it's keys, and the response of a request as it's value. That store wouldn't have an `__iter__` for sure (unless you're Google). But more to the point here, the `mk_cached_store` tool uses the same key for the source and the cache, and we can't just use the url as is, to be a local file path. \n\nThere's many ways we could solve this. One way is to add a key map layer on the cache store, so externally, it speaks the url key language, but internally it will map that url to a valid local file path. We've been there, we got the T-shirt!\n\nBut what we're going to do is a bit different: We're going to do the key mapping in the source store itself. It seems to make more sense in our context: We have a data source of `name: data` pairs, and if we impose that the name should be a valid file name, we don't need to have a key map in the cache store.\n\nSo let's start by building this `MyDataStore` store. We'll start by defining the functions that get us the data we want. \n\n\n```python\ndef country_flag_image_url():\n    import pandas as pd\n    return pd.read_csv(\n        'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv')\n\ndef kaggle_coronavirus_dataset():\n    import kaggle\n    from io import BytesIO\n    # didn't find the pure binary download function, so using temp dir to emulate\n    from tempfile import mkdtemp  \n    download_dir = mkdtemp()\n    filename = 'novel-corona-virus-2019-dataset.zip'\n    zip_file = os.path.join(download_dir, filename)\n    \n    dataset = 'sudalairajkumar/novel-corona-virus-2019-dataset'\n    kaggle.api.dataset_download_files(dataset, download_dir)\n    with open(zip_file, 'rb') as fp:\n        b = fp.read()\n    return BytesIO(b)\n\ndef city_population_in_time():\n    import pandas as pd\n    return pd.read_csv(\n        'https://gist.githubusercontent.com/johnburnmurdoch/'\n        '4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations'\n    )\n```\n\nNow we can make a store that simply uses these function names as the keys, and their returned value as the values.\n\n\n```python\nfrom py2store.base import KvReader\nfrom functools import lru_cache\n\nclass FuncReader(KvReader):\n    _getitem_cache_size = 999\n    def __init__(self, funcs):\n        # TODO: assert no free arguments (arguments are allowed but must all have defaults)\n        self.funcs = funcs\n        self._func_of_name = {func.__name__: func for func in funcs}\n\n    def __contains__(self, k):\n        return k in self._func_of_name\n    \n    def __iter__(self):\n        yield from self._func_of_name\n        \n    def __len__(self):\n        return len(self._func_of_name)\n\n    @lru_cache(maxsize=_getitem_cache_size)\n    def __getitem__(self, k):\n        return self._func_of_name[k]()  # call the func\n    \n    def __hash__(self):\n        return 1\n    \n```\n\n\n```python\ndata_sources = FuncReader([country_flag_image_url, kaggle_coronavirus_dataset, city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['city_population_in_time']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>group</th>\n      <th>year</th>\n      <th>value</th>\n      <th>subGroup</th>\n      <th>city_id</th>\n      <th>lastValue</th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1575</td>\n      <td>200.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1576</td>\n      <td>212.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1577</td>\n      <td>224.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>212.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1578</td>\n      <td>236.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>224.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1579</td>\n      <td>248.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>236.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6247</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1561</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6248</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1562</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6249</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1563</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6250</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1564</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6251</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1565</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n  </tbody>\n</table>\n<p>6252 rows \u00d7 9 columns</p>\n</div>\n\n\n\nBut we wanted this all to be cached locally, right? So a few more lines to do that!\n\n\n```python\nfrom py2store.caching import mk_cached_store\nfrom py2store import QuickPickleStore\n    \nmy_local_cache = os.path.expanduser('~/ddir/my_sources')\n\nCachedFuncReader = mk_cached_store(FuncReader, QuickPickleStore(my_local_cache))\n```\n\n\n```python\ndata_sources = CachedFuncReader([country_flag_image_url, kaggle_coronavirus_dataset, city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['city_population_in_time']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>group</th>\n      <th>year</th>\n      <th>value</th>\n      <th>subGroup</th>\n      <th>city_id</th>\n      <th>lastValue</th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1575</td>\n      <td>200.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1576</td>\n      <td>212.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1577</td>\n      <td>224.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>212.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1578</td>\n      <td>236.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>224.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1579</td>\n      <td>248.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>236.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6247</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1561</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6248</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1562</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6249</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1563</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6250</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1564</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6251</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1565</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n  </tbody>\n</table>\n<p>6252 rows \u00d7 9 columns</p>\n</div>\n\n\n\n\n```python\nz = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\nlist(z)\n```\n",
  "long_description_content_type": "text/markdown",
  "description_file": "README.md",
  "root_url": "https://github.com/thorwhalen",
  "description": "A medley of things that got coded because there was an itch to do so",
  "author": "thorwhalen",
  "license": "Apache Software License",
  "description-file": "README.md",
  "install_requires": [],
  "keywords": [
    "documentation",
    "packaging",
    "publishing"
  ]
}/usr/lib/python3.13/site-packages/setuptools/dist.py:476: SetuptoolsDeprecationWarning: Invalid dash-separated options
!!

        ********************************************************************************
        Usage of dash-separated 'description-file' will not be supported in future
        versions. Please use the underscore name 'description_file' instead.

        This deprecation is overdue, please update your project and remove deprecated
        calls to avoid build errors in the future.

        See https://setuptools.pypa.io/en/latest/userguide/declarative_config.html for details.
        ********************************************************************************

!!
  opt = self.warn_dash_deprecation(opt, section)
/usr/lib/python3.13/site-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'description_file'
  warnings.warn(msg)
/usr/lib/python3.13/site-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'root_url'
  warnings.warn(msg)
/usr/lib/python3.13/site-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'description-file'
  warnings.warn(msg)

--------------------------------------------------------------------
running egg_info
writing tapyoca.egg-info/PKG-INFO
writing dependency_links to tapyoca.egg-info/dependency_links.txt
writing top-level names to tapyoca.egg-info/top_level.txt
reading manifest file 'tapyoca.egg-info/SOURCES.txt'
adding license file 'LICENSE'
writing manifest file 'tapyoca.egg-info/SOURCES.txt'
Handling wheel from get_requires_for_build_wheel
Requirement satisfied: wheel
   (installed: wheel 0.43.0)
!!!! containing_folder_name=tapyoca-0.0.4 but setup name is tapyoca
Setup params -------------------------------------------------------
{
  "name": "tapyoca",
  "version": "0.0.4",
  "url": "https://github.com/thorwhalen/tapyoca",
  "packages": [
    "tapyoca",
    "tapyoca.agglutination",
    "tapyoca.covid",
    "tapyoca.darpa",
    "tapyoca.demonyms",
    "tapyoca.indexing_podcasts",
    "tapyoca.parquet_deformations",
    "tapyoca.phoneming"
  ],
  "include_package_data": true,
  "platforms": "any",
  "long_description": "# tapyoca\nA medley of small projects\n\n\n# parquet_deformations\n\nI'm calling these [Parquet deformations](https://www.theguardian.com/artanddesign/alexs-adventures-in-numberland/2014/sep/09/crazy-paving-the-twisted-world-of-parquet-deformations#:~:text=In%20the%201960s%20an%20American,the%20regularity%20of%20the%20tiling.) but purest would lynch me. \n\nReally, I just wanted to transform one word into another word, gradually, as I've seen in some of [Escher's](https://en.wikipedia.org/wiki/M._C._Escher) work, so I looked it up, and saw that it's called parquet deformations. The math looked enticing, but I had no time for that, so I did the first way I could think of: Mapping pixels to pixels (in some fashion -- but nearest neighbors is the method that yields nicest results, under the pixel-level restriction). \n\nOf course, this can be applied to any image (that will be transformed to B/W (not even gray -- I mean actual B/W), and there's several ways you can perform the parquet (I like the gif rendering). \n\nThe main function (exposed as a script) is `mk_deformation_image`. All you need is to specify two images (or words). If you want, of course, you can specify:\n- `n_steps`: Number of steps from start to end image\n- `save_to_file`: path to file to save too (if not given, will just return the image object)\n- `kind`: 'gif', 'horizontal_stack', or 'vertical_stack'\n- `coordinate_mapping_maker`: A function that will return the mapping between start and end. \nThis function should return a pair (`from_coord`, `to_coord`) of aligned matrices whose 2 columns are the the \n`(x, y)` coordinates, and the rows represent aligned positions that should be mapped. \n\n\n\n## Examples\n\n### Two words...\n\n\n```python\nfit_to_size = 400\nstart_im = image_of_text('sensor').rotate(90, expand=1)\nend_im = image_of_text('meaning').rotate(90, expand=1)\nstart_and_end_image(start_im, end_im)\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_5_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 15, kind='h').resize((500,200))\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_6_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im.transpose(4), end_im.transpose(4), 5, kind='v').resize((200,200))\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_7_0.png)\n\n\n\n\n```python\nf = 'sensor_meaning_knn.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_knn.gif?76128495\">\n\n\n\n\n```python\nf = 'sensor_meaning_scan.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f, \n                     coordinate_mapping_maker='scan')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_scan.gif?76996026\">\n\n\n\n\n```python\nf = 'sensor_meaning_random.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f, \n                     coordinate_mapping_maker='random')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_random.gif?80233280\">\n\n\n\n### From a list of words\n\n\n```python\nstart_words = ['sensor', 'vibration', 'tempature']\nend_words = ['sense', 'meaning', 'detection']\nstart_im, end_im = make_start_and_end_images_with_words(\n    start_words, end_words, perm=True, repeat=2, size=150)\nstart_and_end_image(start_im, end_im).resize((600, 200))\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_12_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 5)\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_13_0.png)\n\n\n\n\n```python\nf = 'bunch_of_words.gif'\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"bunch_of_words.gif?7402792\">\n\n\n\n## From files\n\n\n```python\nstart_im = Image.open('sensor_strip_01.png')\nend_im = Image.open('sense_strip_01.png')\nstart_and_end_image(start_im.resize((200, 500)), end_im.resize((200, 500)))\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_16_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 7)\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_17_0.png)\n\n\n\n\n```python\nf = 'medley.gif'\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"medley.gif?39255021\">\n\n\n\n\n```python\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f, coordinate_mapping_maker='scan')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning.gif?41172115\">\n\n\n\n## an image and some text\n\n\n```python\nstart_im = 'img/waveform_01.png'  # will first look for a file, and if not consider as text\nend_im = 'makes sense'\n\nmk_gif_of_deformations(start_im, end_im, n_steps=20, \n                               save_to_file='image_and_text.gif')\ndisplay_gif('image_and_text.gif')  \n```\n\n\n\n\n<img src=\"image_and_text.gif?92524789\">\n\n\n\n\n\n\n# demonys\n\n## What do we think about other peoples?\n\nThis project is meant to get an idea of what people think of people for different nations, as seen by what they ask google about them. \n\nHere I use python code to acquire, clean up, and analyze the data. \n\n### Demonym\n\nIf you're like me and enjoy the false and fleeting impression of superiority that comes when you know a word someone else doesn't. If you're like me and go to parties for the sole purpose of seeking victims to get a one-up on, here's a cool word to add to your arsenal:\n\n**demonym**: a noun used to denote the natives or inhabitants of a particular country, state, city, etc.\n_\"he struggled for the correct demonym for the people of Manchester\"_\n\n### Back-story of this analysis\n \nDuring a discussion (about traveling in Europe) someone said \"why are the swiss so miserable\". Now, I wouldn't say that the swiss were especially miserable (a couple of ex-girlfriends aside), but to be fair he was contrasting with Italians, so perhaps he has a point. I apologize if you are swiss, or one of the two ex-girlfriends -- nothing personal, this is all for effect. \n\nWe googled \"why are the swiss so \", and sure enough, \"why are the swiss so miserable\" came up as one of the suggestions. So we got curious and started googling other peoples: the French, the Germans, etc.\n\nThat's the back-story of this analysis. This analysis is meant to get an idea of what we think of peoples from other countries. Of course, one can rightfully critique the approach I'll take to gauge \"what we think\" -- all three of these words should, but will not, be defined. I'm just going to see what google's *current* auto-suggest comes back with when I enter \"why are the X so \" (where X will be a noun that denotes the natives of inhabitants of a particular country; a *demonym* if you will). \n\n### Warning\n\nAgain, word of warning: All data and analyses are biased. \nTake everything you'll read here (and to be fair, what you read anywhere) with a grain of salt. \nFor simplicitly I'll saying things like \"what we think of...\" or \"who do we most...\", etc.\nBut I don't **really** mean that.\n\n### Resources\n\n* http://www.geography-site.co.uk/pages/countries/demonyms.html for my list of demonyms.\n* google for my suggestion engine, using the url prefix: `http://suggestqueries.google.com/complete/search?client=chrome&q=`\n\n\n## The results\n\n### In a nutshell\n\nBelow is listed 73 demonyms along with words extracted from the very first google suggestion when you type. \n\n`why are the DEMONYM so `\n\n```text\nafghan    \t                eyes beautiful\nalbanian  \t                     beautiful\namerican  \t          girl dolls expensive\naustralian\t                          tall\nbelgian   \t                    fries good\nbhutanese \t                         happy\nbrazilian \t              good at football\nbritish   \t     full of grief and despair\nbulgarian \t              properties cheap\nburmese   \t             cats affectionate\ncambodian \t                   cows skinny\ncanadian  \t                          nice\nchinese   \t                       healthy\ncolombian \t                  avocados big\ncuban     \t                   cigars good\nczech     \t                          tall\ndominican \t  republic and haiti different\negyptian  \t                gods important\nenglish   \t                      reserved\neritrean  \t                     beautiful\nethiopian \t                     beautiful\nfilipino  \t                         proud\nfinn      \t               shoes expensive\nfrench    \t                       healthy\ngerman    \t                          tall\ngreek     \t                gods messed up\nhaitian   \t                parents strict\nhungarian \t                    words long\nindian    \t            tv debates chaotic\nindonesian\t                         smart\niranian   \t                     beautiful\nisraeli   \t           startups successful\nitalian   \t                         short\njamaican  \t                sprinters fast\njapanese  \t                        polite\nkenyan    \t                  runners good\nlebanese  \t                          rich\nmalagasy  \t                    names long\nmalaysian \t                   drivers bad\nmaltese   \t                          rude\nmongolian \t                  horses small\nmoroccan  \t                rugs expensive\nnepalese  \t                     beautiful\nnigerian  \t                          tall\nnorth korean\t                      hats big\nnorwegian \t                 flights cheap\npakistani \t                          fair\nperuvian  \t               blueberries big\npole      \t                  vaulters hot\nportuguese\t                         short\npuerto rican\t       and cuban flags similar\nromanian  \t                     beautiful\nrussian   \t                  good at math\nsamoan    \t                           big\nsaudi     \t                      arrogant\nscottish  \t                        bitter\nsenegalese\t                          tall\nserbian   \t                          tall\nsingaporean\t                          rude\nsomali    \t                parents strict\nsouth african\t                     plugs big\nsouth korean\t                          tall\nsri lankan\t                          dark\nsudanese  \t                          tall\nswiss     \t        good at making watches\nsyrian    \t                families large\ntaiwanese \t                        pretty\nthai      \t                        pretty\ntongan    \t                           big\nukrainian \t                     beautiful\nvietnamese\t        fiercely nationalistic\nwelsh     \t                          dark\nzambian   \t                emeralds cheap\n```\n\n\nNotes:\n* The queries actually have a space after the \"so\", which matters so as to omit suggestions containing words that start with so.\n* Only the tail of the suggestion is shown -- minus prefix (`why are the DEMONYM` or `why are DEMONYM`) as well as the `so`, where ever it lands in the suggestion. \nFor example, the first suggestion for the american demonym was \"why are american dolls so expensive\", which results in the \"dolls expensive\" association. \n\n\n### Who do we most talk/ask about?\n\nThe original list contained 217 demonyms, but many of these yielded no suggestions (to the specific query format I used, that is). \nOnly 73 demonyms gave me at least one suggestion. \nBut within those, number of suggestions range between 1 and 20 (which is probably the default maximum number of suggestions for the API I used). \nSo, pretending that the number of suggestions is an indicator of how much we have to say, or how many different opinions we have, of each of the covered nationalities, \nhere's the top 15 demonyms people talk about, with the corresponding number of suggestions \n(proxy for \"the number of different things people ask about the said nationality). \n\n```text\nfrench         20\nsingaporean    20\ngerman         20\nbritish        20\nswiss          20\nenglish        19\nitalian        18\ncuban          18\ncanadian       18\nwelsh          18\naustralian     17\nmaltese        16\namerican       16\njapanese       14\nscottish       14\n```\n\n### Who do we least talk/ask about?\n\nConversely, here are the 19 demonyms that came back with only one suggestion.\n\n```text\nsomali          1\nbhutanese       1\nsyrian          1\ntongan          1\ncambodian       1\nmalagasy        1\nsaudi           1\nserbian         1\nczech           1\neritrean        1\nfinn            1\npuerto rican    1\npole            1\nhaitian         1\nhungarian       1\nperuvian        1\nmoroccan        1\nmongolian       1\nzambian         1\n```\n\n### What do we think about people?\n\nWhy are the French so...\n\nHow would you (if you're (un)lucky enough to know the French) finish this sentence?\nYou might even have several opinions about the French, and any other group of people you've rubbed shoulders with.\nWhat words would your palette contain to describe different nationalities?\nWhat words would others (at least those that ask questions to google) use?\n\nWell, here's what my auto-suggest search gave me. A set of 357 unique words and expressions to describe the 72 nationalities. \nSo a long tail of words use only for one nationality. But some words occur for more than one nationality. \nHere are the top 12 words/expressions used to describe people of the world. \n\n```text\nbeautiful         11\ntall              11\nshort              9\nnames long         8\nproud              8\nparents strict     8\nsmart              8\nnice               7\nboring             6\nrich               5\ndark               5\nsuccessful         5\n```\n\n### Who is beautiful? Who is tall? Who is short? Who is smart?\n\n```text\nbeautiful      : albanian, eritrean, ethiopian, filipino, iranian, lebanese, nepalese, pakistani, romanian, ukrainian, vietnamese\ntall           : australian, czech, german, nigerian, pakistani, samoan, senegalese, serbian, south korean, sudanese, taiwanese\nshort          : filipino, indonesian, italian, maltese, nepalese, pakistani, portuguese, singaporean, welsh\nnames long     : indian, malagasy, nigerian, portuguese, russian, sri lankan, thai, welsh\nproud          : albanian, ethiopian, filipino, iranian, lebanese, portuguese, scottish, welsh\nparents strict : albanian, ethiopian, haitian, indian, lebanese, pakistani, somali, sri lankan\nsmart          : indonesian, iranian, lebanese, pakistani, romanian, singaporean, taiwanese, vietnamese\nnice           : canadian, english, filipino, nepalese, portuguese, taiwanese, thai\nboring         : british, english, french, german, singaporean, swiss\nrich           : lebanese, pakistani, singaporean, taiwanese, vietnamese\ndark           : filipino, senegalese, sri lankan, vietnamese, welsh\nsuccessful     : chinese, english, japanese, lebanese, swiss\n```\n\n## How did I do it?\n\nI scraped a list of (country, demonym) pairs from a table in http://www.geography-site.co.uk/pages/countries/demonyms.html.\n\nThen I diagnosed these and manually made a mapping to simplify some \"complex\" entries, \nsuch as mapping an entry such as \"Irishman or Irishwoman or Irish\" to \"Irish\".\n\nUsing the google suggest API (http://suggestqueries.google.com/complete/search?client=chrome&q=), I requested what the suggestions \nfor `why are the $demonym so ` query pattern, for `$demonym` running through all 217 demonyms from the list above, \nstoring the results for each if the results were non-empty. \n\nThen, it was just a matter of pulling this data into memory, formatting it a bit, and creating a pandas dataframe that I could then interrogate.\n \n## Resources you can find here\n\nThe code to do this analysis yourself, from scratch here: `data_acquisition.py`.\n\nThe jupyter notebook I actually used when I developed this: `01 - Demonyms and adjectives - why are the french so....ipynb`\n \nNote you'll need to pip install py2store if you haven't already.\n\nIn the `data` folder you'll find\n* country_demonym.p: A pickle of a dataframe of countries and corresponding demonyms\n* country_demonym.xlsx: The same as above, but in excel form\n* demonym_suggested_characteristics.p: A pickle of 73 demonyms and auto-suggestion information, including characteristics. \n* what_we_think_about_demonyns.xlsx: An excel containing various statistics about demonyms and their (perceived) characteristics\n \n\n\n\n\n\n# Agglutinations\n\nInspired from a [tweet](https://twitter.com/raymondh/status/1311003482531401729) from Raymond Hettinger this morning:\n\n_Resist the urge to elide the underscore in multiword function or method names_\n\nSo I wondered...\n\n## Gluglus\n\nThe gluglu of a word is the number of partitions you can make of that word into words (of length at least 2 (so no using a or i)).\n(No \"gluglu\" isn't an actual term -- unless everyone starts using it from now on. \nBut it was inspired from an actual [linguistic term](https://en.wikipedia.org/wiki/Agglutination).)\n\nFor example, the gluglu of ``newspaper`` is 4:\n\n```\nnewspaper\n    new spa per\n    news pa per\n    news paper\n```\n\nEvery (valid) word has gluglu at least 1.\n\n\n## How many standard library names have gluglus at last 2?\n\n108\n\nHere's [the list](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/standard_lib_gluglus.txt) of all of them.\n\nThe winner has a gluglu of 6 (not 7 because formatannotationrelativeto isn't in the dictionary)\n\n```\nformatannotationrelativeto\n\tfor mat an not at ion relative to\n\tfor mat annotation relative to\n\tform at an not at ion relative to\n\tform at annotation relative to\n\tformat an not at ion relative to\n\tformat annotation relative to\n```\n\n## Details\n\n### Dictionary\n\nReally it depends on what dictionary we use. \nHere, I used a very conservative one. \nThe intersection of two lists: The [corncob](http://www.mieliestronk.com/corncob_lowercase.txt) \nand the [google10000](https://raw.githubusercontent.com/first20hours/google-10000-english/master/google-10000-english-usa.txt) word lists.\nAdditionally, I only kept of those, those that had at least 2 letters, and had only letters (no hyphens or disturbing diacritics).\n\nDiacritics. Look it up. Impress your next nerd date.\n\nIm left with 8116 words. You can find them [here](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/words_8116.csv).\n\n### Standard Lib Names\n\nSurprisingly, that was the hardest part. I know I'm missing some, but that's enough rabbit-holing. \n\nWhat I did (modulo some exceptions I won't look into) was to walk the standard lib modules (even that list wasn't a given!) \nextracting (recursively( the names of any (non-underscored) attributes if they were modules or callables, \nas well as extracting the arguments of these callables (when they had signatures).\n\nYou can find the code I used to extract these names [here](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/py_names.py) \nand the actual list [there](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/standard_lib_module_names.csv).\n\n\n\n# covid\n\n## Bar Chart Races (applied to covid-19 spread)\n\nThe module will show is how to make these:\n- Confirmed cases (by country): https://public.flourish.studio/visualisation/1704821/\n- Deaths (by country): https://public.flourish.studio/visualisation/1705644/\n- US Confirmed cases (by state): https://public.flourish.studio/visualisation/1794768/\n- US Deaths (by state): https://public.flourish.studio/visualisation/1794797/\n\n### The script\n\nIf you just want to run this as a script to get the job done, you have one here: \nhttps://raw.githubusercontent.com/thorwhalen/tapyoca/master/covid/covid_bar_chart_race.py\n\nRun like this\n```\n$ python covid_bar_chart_race.py -h\nusage: covid_bar_chart_race.py [-h] {mk-and-save-covid-data,update-covid-data,instructions-to-make-bar-chart-race} ...\n\npositional arguments:\n  {mk-and-save-covid-data,update-covid-data,instructions-to-make-bar-chart-race}\n    mk-and-save-covid-data\n                        :param data_sources: Dirpath or py2store Store where the data is :param kinds: The kinds of data you want to compute and save :param\n                        skip_first_days: :param verbose: :return:\n    update-covid-data   update the coronavirus data\n    instructions-to-make-bar-chart-race\n\noptional arguments:\n  -h, --help            show this help message and exit\n ```\n \n \n### The jupyter notebook\n\nThe notebook (the .ipynb file) shows you how to do it step by step in case you want to reuse the methods for other stuff.\n\n\n\n## Getting and preparing the data\n\nCorona virus data here: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset (direct download: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset/download). It's currently updated daily, so download a fresh copy if you want.\n\nPopulation data here: http://api.worldbank.org/v2/en/indicator/SP.POP.TOTL?downloadformat=csv\n\nIt comes under the form of a zip file (currently named `novel-corona-virus-2019-dataset.zip` with several `.csv` files in them. We use `py2store` (To install: `pip install py2store`. Project lives here: https://github.com/i2mint/py2store) to access and pre-prepare it. It allows us to not have to unzip the file and replace the older folder with it every time we download a new one. It also gives us the csvs as `pandas.DataFrame` already. \n\n\n```python\nimport pandas as pd\nfrom io import BytesIO\nfrom py2store import kv_wrap, ZipReader  # google it and pip install it\nfrom py2store.caching import mk_cached_store\nfrom py2store import QuickPickleStore\nfrom py2store.sources import FuncReader\n\ndef country_flag_image_url():\n    import pandas as pd\n    return pd.read_csv(\n        'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv')\n\ndef kaggle_coronavirus_dataset():\n    import kaggle\n    from io import BytesIO\n    # didn't find the pure binary download function, so using temp dir to emulate\n    from tempfile import mkdtemp  \n    download_dir = mkdtemp()\n    filename = 'novel-corona-virus-2019-dataset.zip'\n    zip_file = os.path.join(download_dir, filename)\n    \n    dataset = 'sudalairajkumar/novel-corona-virus-2019-dataset'\n    kaggle.api.dataset_download_files(dataset, download_dir)\n    with open(zip_file, 'rb') as fp:\n        b = fp.read()\n    return BytesIO(b)\n\ndef city_population_in_time():\n    import pandas as pd\n    return pd.read_csv(\n        'https://gist.githubusercontent.com/johnburnmurdoch/'\n        '4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations'\n    )\n\ndef country_flag_image_url_prep(df: pd.DataFrame):\n    # delete the region col (we don't need it)\n    del df['region']\n    # rewriting a few (not all) of the country names to match those found in kaggle covid data\n    # Note: The list is not complete! Add to it as needed\n    old_and_new = [('USA', 'US'), \n                   ('Iran, Islamic Rep.', 'Iran'), \n                   ('UK', 'United Kingdom'), \n                   ('Korea, Rep.', 'Korea, South')]\n    for old, new in old_and_new:\n        df['country'] = df['country'].replace(old, new)\n\n    return df\n\n\n@kv_wrap.outcoming_vals(lambda x: pd.read_csv(BytesIO(x)))  # this is to format the data as a dataframe\nclass ZippedCsvs(ZipReader):\n    pass\n# equivalent to ZippedCsvs = kv_wrap.outcoming_vals(lambda x: pd.read_csv(BytesIO(x)))(ZipReader)\n```\n\n\n```python\n# Enter here the place you want to cache your data\nmy_local_cache = os.path.expanduser('~/ddir/my_sources')\n```\n\n\n```python\nCachedFuncReader = mk_cached_store(FuncReader, QuickPickleStore(my_local_cache))\n```\n\n\n```python\ndata_sources = CachedFuncReader([country_flag_image_url, \n                                 kaggle_coronavirus_dataset, \n                                 city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ncovid_datasets = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\nlist(covid_datasets)\n```\n\n\n\n\n    ['COVID19_line_list_data.csv',\n     'COVID19_open_line_list.csv',\n     'covid_19_data.csv',\n     'time_series_covid_19_confirmed.csv',\n     'time_series_covid_19_confirmed_US.csv',\n     'time_series_covid_19_deaths.csv',\n     'time_series_covid_19_deaths_US.csv',\n     'time_series_covid_19_recovered.csv']\n\n\n\n\n```python\ncovid_datasets['time_series_covid_19_confirmed.csv'].head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Province/State</th>\n      <th>Country/Region</th>\n      <th>Lat</th>\n      <th>Long</th>\n      <th>1/22/20</th>\n      <th>1/23/20</th>\n      <th>1/24/20</th>\n      <th>1/25/20</th>\n      <th>1/26/20</th>\n      <th>1/27/20</th>\n      <th>...</th>\n      <th>3/24/20</th>\n      <th>3/25/20</th>\n      <th>3/26/20</th>\n      <th>3/27/20</th>\n      <th>3/28/20</th>\n      <th>3/29/20</th>\n      <th>3/30/20</th>\n      <th>3/31/20</th>\n      <th>4/1/20</th>\n      <th>4/2/20</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>Afghanistan</td>\n      <td>33.0000</td>\n      <td>65.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>74</td>\n      <td>84</td>\n      <td>94</td>\n      <td>110</td>\n      <td>110</td>\n      <td>120</td>\n      <td>170</td>\n      <td>174</td>\n      <td>237</td>\n      <td>273</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>Albania</td>\n      <td>41.1533</td>\n      <td>20.1683</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>123</td>\n      <td>146</td>\n      <td>174</td>\n      <td>186</td>\n      <td>197</td>\n      <td>212</td>\n      <td>223</td>\n      <td>243</td>\n      <td>259</td>\n      <td>277</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>Algeria</td>\n      <td>28.0339</td>\n      <td>1.6596</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>264</td>\n      <td>302</td>\n      <td>367</td>\n      <td>409</td>\n      <td>454</td>\n      <td>511</td>\n      <td>584</td>\n      <td>716</td>\n      <td>847</td>\n      <td>986</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>Andorra</td>\n      <td>42.5063</td>\n      <td>1.5218</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>164</td>\n      <td>188</td>\n      <td>224</td>\n      <td>267</td>\n      <td>308</td>\n      <td>334</td>\n      <td>370</td>\n      <td>376</td>\n      <td>390</td>\n      <td>428</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>Angola</td>\n      <td>-11.2027</td>\n      <td>17.8739</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>5</td>\n      <td>7</td>\n      <td>7</td>\n      <td>7</td>\n      <td>8</td>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 76 columns</p>\n</div>\n\n\n\n\n```python\ncountry_flag_image_url = data_sources['country_flag_image_url']\ncountry_flag_image_url.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nfrom IPython.display import Image\nflag_image_url_of_country = country_flag_image_url.set_index('country')['flag_image_url']\nImage(url=flag_image_url_of_country['Australia'])\n```\n\n\n\n\n<img src=\"https://www.countryflags.io/au/flat/64.png\"/>\n\n\n\n### Update coronavirus data\n\n\n```python\n# To update the coronavirus data:\ndef update_covid_data(data_sources):\n    \"\"\"update the coronavirus data\"\"\"\n    if 'kaggle_coronavirus_dataset' in data_sources._caching_store:\n        del data_sources._caching_store['kaggle_coronavirus_dataset']  # delete the cached item\n    _ = data_sources['kaggle_coronavirus_dataset']\n\n# update_covid_data(data_sources)  # uncomment here when you want to update\n```\n\n### Prepare data for flourish upload\n\n\n```python\nimport re\n\ndef print_if_verbose(verbose, *args, **kwargs):\n    if verbose:\n        print(*args, **kwargs)\n        \ndef country_data_for_data_kind(data_sources, kind='confirmed', skip_first_days=0, verbose=False):\n    \"\"\"kind can be 'confirmed', 'deaths', 'confirmed_US', 'confirmed_US', 'recovered'\"\"\"\n    \n    covid_datasets = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\n    \n    df = covid_datasets[f'time_series_covid_19_{kind}.csv']\n    # df = s['time_series_covid_19_deaths.csv']\n    if 'Province/State' in df.columns:\n        df.loc[df['Province/State'].isna(), 'Province/State'] = 'n/a'  # to avoid problems arising from NaNs\n\n    print_if_verbose(verbose, f\"Before data shape: {df.shape}\")\n\n    # drop some columns we don't need\n    p = re.compile('\\d+/\\d+/\\d+')\n\n    assert all(isinstance(x, str) for x in df.columns)\n    date_cols = [x for x in df.columns if p.match(x)]\n    if not kind.endswith('US'):\n        df = df.loc[:, ['Country/Region'] + date_cols]\n        # group countries and sum up the contributions of their states/regions/pargs\n        df['country'] = df.pop('Country/Region')\n        df = df.groupby('country').sum()\n    else:\n        df = df.loc[:, ['Province_State'] + date_cols]\n        df['state'] = df.pop('Province_State')\n        df = df.groupby('state').sum()\n\n    \n    print_if_verbose(verbose, f\"After data shape: {df.shape}\")\n    df = df.iloc[:, skip_first_days:]\n    \n    if not kind.endswith('US'):\n        # Joining with the country image urls and saving as an xls\n        country_image_url = country_flag_image_url_prep(data_sources['country_flag_image_url'])\n        t = df.copy()\n        t.columns = [str(x)[:10] for x in t.columns]\n        t = t.reset_index(drop=False)\n        t = country_image_url.merge(t, how='outer')\n        t = t.set_index('country')\n        df = t\n    else:    \n        pass\n\n    return df\n\n\ndef mk_and_save_country_data_for_data_kind(data_sources, kind='confirmed', skip_first_days=0, verbose=False):\n    t = country_data_for_data_kind(data_sources, kind, skip_first_days, verbose)\n    filepath = f'country_covid_{kind}.xlsx'\n    t.to_excel(filepath)\n    print_if_verbose(verbose, f\"Was saved here: {filepath}\")\n\n```\n\n\n```python\n# for kind in ['confirmed', 'deaths', 'recovered', 'confirmed_US', 'deaths_US']:\nfor kind in ['confirmed', 'deaths', 'recovered', 'confirmed_US', 'deaths_US']:\n    mk_and_save_country_data_for_data_kind(data_sources, kind=kind, skip_first_days=39, verbose=True)\n```\n\n    Before data shape: (262, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_confirmed.xlsx\n    Before data shape: (262, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_deaths.xlsx\n    Before data shape: (248, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_recovered.xlsx\n    Before data shape: (3253, 86)\n    After data shape: (58, 75)\n    Was saved here: country_covid_confirmed_US.xlsx\n    Before data shape: (3253, 87)\n    After data shape: (58, 75)\n    Was saved here: country_covid_deaths_US.xlsx\n\n\n### Upload to Flourish, tune, and publish\n\nGo to https://public.flourish.studio/, get a free account, and play.\n\nGot to https://app.flourish.studio/templates\n\nChoose \"Bar chart race\". At the time of writing this, it was here: https://app.flourish.studio/visualisation/1706060/\n\n... and then play with the settings\n\n\n## Discussion of the methods\n\n\n```python\nfrom py2store import *\nfrom IPython.display import Image\n```\n\n### country flags images\n\nThe manual data prep looks something like this.\n\n\n```python\nimport pandas as pd\n\n# get the csv data from the url\ncountry_image_url_source = \\\n    'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv'\ncountry_image_url = pd.read_csv(country_image_url_source)\n\n# delete the region col (we don't need it)\ndel country_image_url['region']\n\n# rewriting a few (not all) of the country names to match those found in kaggle covid data\n# Note: The list is not complete! Add to it as needed\n# TODO: (Wishful) Using a general smart soft-matching algorithm to do this automatically.\n# TODO:    This could use edit-distance, synonyms, acronym generation, etc.\nold_and_new = [('USA', 'US'), \n               ('Iran, Islamic Rep.', 'Iran'), \n               ('UK', 'United Kingdom'), \n               ('Korea, Rep.', 'Korea, South')]\nfor old, new in old_and_new:\n    country_image_url['country'] = country_image_url['country'].replace(old, new)\n\nimage_url_of_country = country_image_url.set_index('country')['flag_image_url']\n\ncountry_image_url.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nImage(url=image_url_of_country['Australia'])\n```\n\n\n\n\n<img src=\"https://www.countryflags.io/au/flat/64.png\"/>\n\n\n\n### Caching the flag images data\n\nDownloading our data sources every time we need them is not sustainable. What if they're big? What if you're offline or have slow internet (yes, dear future reader, even in the US, during coronavirus times!)?\n\nCaching. A \"cache aside\" read-cache. That's the word. py2store has tools for that (most of which are are caching.py). \n\nSo let's say we're going to have a local folder where we'll store various datas we download. The principle is as follows:\n\n\n```python\nfrom py2store.caching import mk_cached_store\n\nclass TheSource(dict): ...\nthe_cache = {}\nTheCacheSource = mk_cached_store(TheSource, the_cache)\n\nthe_source = TheSource({'green': 'eggs', 'and': 'ham'})\n\nthe_cached_source = TheCacheSource(the_source)\nprint(f\"the_cache: {the_cache}\")\nprint(f\"Getting green...\")\nthe_cached_source['green']\nprint(f\"the_cache: {the_cache}\")\nprint(\"... so the next time the_cached_source will get it's green from that the_cache\")\n```\n\n    the_cache: {}\n    Getting green...\n    the_cache: {'green': 'eggs'}\n    ... so the next time the_cached_source will get it's green from that the_cache\n\n\nBut now, you'll notice a slight problem ahead. What exactly does our source store (or rather reader) looks like? In it's raw form it would take urls as it's keys, and the response of a request as it's value. That store wouldn't have an `__iter__` for sure (unless you're Google). But more to the point here, the `mk_cached_store` tool uses the same key for the source and the cache, and we can't just use the url as is, to be a local file path. \n\nThere's many ways we could solve this. One way is to add a key map layer on the cache store, so externally, it speaks the url key language, but internally it will map that url to a valid local file path. We've been there, we got the T-shirt!\n\nBut what we're going to do is a bit different: We're going to do the key mapping in the source store itself. It seems to make more sense in our context: We have a data source of `name: data` pairs, and if we impose that the name should be a valid file name, we don't need to have a key map in the cache store.\n\nSo let's start by building this `MyDataStore` store. We'll start by defining the functions that get us the data we want. \n\n\n```python\ndef country_flag_image_url():\n    import pandas as pd\n    return pd.read_csv(\n        'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv')\n\ndef kaggle_coronavirus_dataset():\n    import kaggle\n    from io import BytesIO\n    # didn't find the pure binary download function, so using temp dir to emulate\n    from tempfile import mkdtemp  \n    download_dir = mkdtemp()\n    filename = 'novel-corona-virus-2019-dataset.zip'\n    zip_file = os.path.join(download_dir, filename)\n    \n    dataset = 'sudalairajkumar/novel-corona-virus-2019-dataset'\n    kaggle.api.dataset_download_files(dataset, download_dir)\n    with open(zip_file, 'rb') as fp:\n        b = fp.read()\n    return BytesIO(b)\n\ndef city_population_in_time():\n    import pandas as pd\n    return pd.read_csv(\n        'https://gist.githubusercontent.com/johnburnmurdoch/'\n        '4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations'\n    )\n```\n\nNow we can make a store that simply uses these function names as the keys, and their returned value as the values.\n\n\n```python\nfrom py2store.base import KvReader\nfrom functools import lru_cache\n\nclass FuncReader(KvReader):\n    _getitem_cache_size = 999\n    def __init__(self, funcs):\n        # TODO: assert no free arguments (arguments are allowed but must all have defaults)\n        self.funcs = funcs\n        self._func_of_name = {func.__name__: func for func in funcs}\n\n    def __contains__(self, k):\n        return k in self._func_of_name\n    \n    def __iter__(self):\n        yield from self._func_of_name\n        \n    def __len__(self):\n        return len(self._func_of_name)\n\n    @lru_cache(maxsize=_getitem_cache_size)\n    def __getitem__(self, k):\n        return self._func_of_name[k]()  # call the func\n    \n    def __hash__(self):\n        return 1\n    \n```\n\n\n```python\ndata_sources = FuncReader([country_flag_image_url, kaggle_coronavirus_dataset, city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['city_population_in_time']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>group</th>\n      <th>year</th>\n      <th>value</th>\n      <th>subGroup</th>\n      <th>city_id</th>\n      <th>lastValue</th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1575</td>\n      <td>200.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1576</td>\n      <td>212.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1577</td>\n      <td>224.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>212.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1578</td>\n      <td>236.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>224.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1579</td>\n      <td>248.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>236.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6247</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1561</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6248</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1562</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6249</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1563</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6250</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1564</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6251</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1565</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n  </tbody>\n</table>\n<p>6252 rows \u00d7 9 columns</p>\n</div>\n\n\n\nBut we wanted this all to be cached locally, right? So a few more lines to do that!\n\n\n```python\nfrom py2store.caching import mk_cached_store\nfrom py2store import QuickPickleStore\n    \nmy_local_cache = os.path.expanduser('~/ddir/my_sources')\n\nCachedFuncReader = mk_cached_store(FuncReader, QuickPickleStore(my_local_cache))\n```\n\n\n```python\ndata_sources = CachedFuncReader([country_flag_image_url, kaggle_coronavirus_dataset, city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['city_population_in_time']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>group</th>\n      <th>year</th>\n      <th>value</th>\n      <th>subGroup</th>\n      <th>city_id</th>\n      <th>lastValue</th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1575</td>\n      <td>200.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1576</td>\n      <td>212.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1577</td>\n      <td>224.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>212.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1578</td>\n      <td>236.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>224.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1579</td>\n      <td>248.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>236.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6247</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1561</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6248</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1562</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6249</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1563</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6250</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1564</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6251</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1565</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n  </tbody>\n</table>\n<p>6252 rows \u00d7 9 columns</p>\n</div>\n\n\n\n\n```python\nz = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\nlist(z)\n```\n",
  "long_description_content_type": "text/markdown",
  "description_file": "README.md",
  "root_url": "https://github.com/thorwhalen",
  "description": "A medley of things that got coded because there was an itch to do so",
  "author": "thorwhalen",
  "license": "Apache Software License",
  "description-file": "README.md",
  "install_requires": [],
  "keywords": [
    "documentation",
    "packaging",
    "publishing"
  ]
}
--------------------------------------------------------------------
running dist_info
writing tapyoca.egg-info/PKG-INFO
writing dependency_links to tapyoca.egg-info/dependency_links.txt
writing top-level names to tapyoca.egg-info/top_level.txt
reading manifest file 'tapyoca.egg-info/SOURCES.txt'
adding license file 'LICENSE'
writing manifest file 'tapyoca.egg-info/SOURCES.txt'
creating '/builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/tapyoca-0.0.4.dist-info'
+ cat /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-buildrequires
+ rm -rfv tapyoca-0.0.4.dist-info/
removed 'tapyoca-0.0.4.dist-info/top_level.txt'
removed 'tapyoca-0.0.4.dist-info/METADATA'
removed 'tapyoca-0.0.4.dist-info/LICENSE'
removed directory 'tapyoca-0.0.4.dist-info/'
+ RPM_EC=0
++ jobs -p
+ exit 0
Executing(%build): /bin/sh -e /var/tmp/rpm-tmp.2cPNXe
+ umask 022
+ cd /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ export RUSTFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ cd tapyoca-0.0.4
+ mkdir -p /builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/.pyproject-builddir
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ VALAFLAGS=-g
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes '
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ CC=gcc
+ CXX=g++
+ TMPDIR=/builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/.pyproject-builddir
+ /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_wheel.py /builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/pyproject-wheeldir
Processing /builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4
  Preparing metadata (pyproject.toml): started
  Running command Preparing metadata (pyproject.toml)
  !!!! containing_folder_name=tapyoca-0.0.4 but setup name is tapyoca
  Setup params -------------------------------------------------------
  {
    "name": "tapyoca",
    "version": "0.0.4",
    "url": "https://github.com/thorwhalen/tapyoca",
    "packages": [
      "tapyoca",
      "tapyoca.agglutination",
      "tapyoca.covid",
      "tapyoca.darpa",
      "tapyoca.demonyms",
      "tapyoca.indexing_podcasts",
      "tapyoca.parquet_deformations",
      "tapyoca.phoneming"
    ],
    "include_package_data": true,
    "platforms": "any",
    "long_description": "# tapyoca\nA medley of small projects\n\n\n# parquet_deformations\n\nI'm calling these [Parquet deformations](https://www.theguardian.com/artanddesign/alexs-adventures-in-numberland/2014/sep/09/crazy-paving-the-twisted-world-of-parquet-deformations#:~:text=In%20the%201960s%20an%20American,the%20regularity%20of%20the%20tiling.) but purest would lynch me. \n\nReally, I just wanted to transform one word into another word, gradually, as I've seen in some of [Escher's](https://en.wikipedia.org/wiki/M._C._Escher) work, so I looked it up, and saw that it's called parquet deformations. The math looked enticing, but I had no time for that, so I did the first way I could think of: Mapping pixels to pixels (in some fashion -- but nearest neighbors is the method that yields nicest results, under the pixel-level restriction). \n\nOf course, this can be applied to any image (that will be transformed to B/W (not even gray -- I mean actual B/W), and there's several ways you can perform the parquet (I like the gif rendering). \n\nThe main function (exposed as a script) is `mk_deformation_image`. All you need is to specify two images (or words). If you want, of course, you can specify:\n- `n_steps`: Number of steps from start to end image\n- `save_to_file`: path to file to save too (if not given, will just return the image object)\n- `kind`: 'gif', 'horizontal_stack', or 'vertical_stack'\n- `coordinate_mapping_maker`: A function that will return the mapping between start and end. \nThis function should return a pair (`from_coord`, `to_coord`) of aligned matrices whose 2 columns are the the \n`(x, y)` coordinates, and the rows represent aligned positions that should be mapped. \n\n\n\n## Examples\n\n### Two words...\n\n\n```python\nfit_to_size = 400\nstart_im = image_of_text('sensor').rotate(90, expand=1)\nend_im = image_of_text('meaning').rotate(90, expand=1)\nstart_and_end_image(start_im, end_im)\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_5_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 15, kind='h').resize((500,200))\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_6_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im.transpose(4), end_im.transpose(4), 5, kind='v').resize((200,200))\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_7_0.png)\n\n\n\n\n```python\nf = 'sensor_meaning_knn.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_knn.gif?76128495\">\n\n\n\n\n```python\nf = 'sensor_meaning_scan.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f, \n                     coordinate_mapping_maker='scan')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_scan.gif?76996026\">\n\n\n\n\n```python\nf = 'sensor_meaning_random.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f, \n                     coordinate_mapping_maker='random')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_random.gif?80233280\">\n\n\n\n### From a list of words\n\n\n```python\nstart_words = ['sensor', 'vibration', 'tempature']\nend_words = ['sense', 'meaning', 'detection']\nstart_im, end_im = make_start_and_end_images_with_words(\n    start_words, end_words, perm=True, repeat=2, size=150)\nstart_and_end_image(start_im, end_im).resize((600, 200))\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_12_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 5)\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_13_0.png)\n\n\n\n\n```python\nf = 'bunch_of_words.gif'\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"bunch_of_words.gif?7402792\">\n\n\n\n## From files\n\n\n```python\nstart_im = Image.open('sensor_strip_01.png')\nend_im = Image.open('sense_strip_01.png')\nstart_and_end_image(start_im.resize((200, 500)), end_im.resize((200, 500)))\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_16_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 7)\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_17_0.png)\n\n\n\n\n```python\nf = 'medley.gif'\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"medley.gif?39255021\">\n\n\n\n\n```python\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f, coordinate_mapping_maker='scan')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning.gif?41172115\">\n\n\n\n## an image and some text\n\n\n```python\nstart_im = 'img/waveform_01.png'  # will first look for a file, and if not consider as text\nend_im = 'makes sense'\n\nmk_gif_of_deformations(start_im, end_im, n_steps=20, \n                               save_to_file='image_and_text.gif')\ndisplay_gif('image_and_text.gif')  \n```\n\n\n\n\n<img src=\"image_and_text.gif?92524789\">\n\n\n\n\n\n\n# demonys\n\n## What do we think about other peoples?\n\nThis project is meant to get an idea of what people think of people for different nations, as seen by what they ask google about them. \n\nHere I use python code to acquire, clean up, and analyze the data. \n\n### Demonym\n\nIf you're like me and enjoy the false and fleeting impression of superiority that comes when you know a word someone else doesn't. If you're like me and go to parties for the sole purpose of seeking victims to get a one-up on, here's a cool word to add to your arsenal:\n\n**demonym**: a noun used to denote the natives or inhabitants of a particular country, state, city, etc.\n_\"he struggled for the correct demonym for the people of Manchester\"_\n\n### Back-story of this analysis\n \nDuring a discussion (about traveling in Europe) someone said \"why are the swiss so miserable\". Now, I wouldn't say that the swiss were especially miserable (a couple of ex-girlfriends aside), but to be fair he was contrasting with Italians, so perhaps he has a point. I apologize if you are swiss, or one of the two ex-girlfriends -- nothing personal, this is all for effect. \n\nWe googled \"why are the swiss so \", and sure enough, \"why are the swiss so miserable\" came up as one of the suggestions. So we got curious and started googling other peoples: the French, the Germans, etc.\n\nThat's the back-story of this analysis. This analysis is meant to get an idea of what we think of peoples from other countries. Of course, one can rightfully critique the approach I'll take to gauge \"what we think\" -- all three of these words should, but will not, be defined. I'm just going to see what google's *current* auto-suggest comes back with when I enter \"why are the X so \" (where X will be a noun that denotes the natives of inhabitants of a particular country; a *demonym* if you will). \n\n### Warning\n\nAgain, word of warning: All data and analyses are biased. \nTake everything you'll read here (and to be fair, what you read anywhere) with a grain of salt. \nFor simplicitly I'll saying things like \"what we think of...\" or \"who do we most...\", etc.\nBut I don't **really** mean that.\n\n### Resources\n\n* http://www.geography-site.co.uk/pages/countries/demonyms.html for my list of demonyms.\n* google for my suggestion engine, using the url prefix: `http://suggestqueries.google.com/complete/search?client=chrome&q=`\n\n\n## The results\n\n### In a nutshell\n\nBelow is listed 73 demonyms along with words extracted from the very first google suggestion when you type. \n\n`why are the DEMONYM so `\n\n```text\nafghan    \t                eyes beautiful\nalbanian  \t                     beautiful\namerican  \t          girl dolls expensive\naustralian\t                          tall\nbelgian   \t                    fries good\nbhutanese \t                         happy\nbrazilian \t              good at football\nbritish   \t     full of grief and despair\nbulgarian \t              properties cheap\nburmese   \t             cats affectionate\ncambodian \t                   cows skinny\ncanadian  \t                          nice\nchinese   \t                       healthy\ncolombian \t                  avocados big\ncuban     \t                   cigars good\nczech     \t                          tall\ndominican \t  republic and haiti different\negyptian  \t                gods important\nenglish   \t                      reserved\neritrean  \t                     beautiful\nethiopian \t                     beautiful\nfilipino  \t                         proud\nfinn      \t               shoes expensive\nfrench    \t                       healthy\ngerman    \t                          tall\ngreek     \t                gods messed up\nhaitian   \t                parents strict\nhungarian \t                    words long\nindian    \t            tv debates chaotic\nindonesian\t                         smart\niranian   \t                     beautiful\nisraeli   \t           startups successful\nitalian   \t                         short\njamaican  \t                sprinters fast\njapanese  \t                        polite\nkenyan    \t                  runners good\nlebanese  \t                          rich\nmalagasy  \t                    names long\nmalaysian \t                   drivers bad\nmaltese   \t                          rude\nmongolian \t                  horses small\nmoroccan  \t                rugs expensive\nnepalese  \t                     beautiful\nnigerian  \t                          tall\nnorth korean\t                      hats big\nnorwegian \t                 flights cheap\npakistani \t                          fair\nperuvian  \t               blueberries big\npole      \t                  vaulters hot\nportuguese\t                         short\npuerto rican\t       and cuban flags similar\nromanian  \t                     beautiful\nrussian   \t                  good at math\nsamoan    \t                           big\nsaudi     \t                      arrogant\nscottish  \t                        bitter\nsenegalese\t                          tall\nserbian   \t                          tall\nsingaporean\t                          rude\nsomali    \t                parents strict\nsouth african\t                     plugs big\nsouth korean\t                          tall\nsri lankan\t                          dark\nsudanese  \t                          tall\nswiss     \t        good at making watches\nsyrian    \t                families large\ntaiwanese \t                        pretty\nthai      \t                        pretty\ntongan    \t                           big\nukrainian \t                     beautiful\nvietnamese\t        fiercely nationalistic\nwelsh     \t                          dark\nzambian   \t                emeralds cheap\n```\n\n\nNotes:\n* The queries actually have a space after the \"so\", which matters so as to omit suggestions containing words that start with so.\n* Only the tail of the suggestion is shown -- minus prefix (`why are the DEMONYM` or `why are DEMONYM`) as well as the `so`, where ever it lands in the suggestion. \nFor example, the first suggestion for the american demonym was \"why are american dolls so expensive\", which results in the \"dolls expensive\" association. \n\n\n### Who do we most talk/ask about?\n\nThe original list contained 217 demonyms, but many of these yielded no suggestions (to the specific query format I used, that is). \nOnly 73 demonyms gave me at least one suggestion. \nBut within those, number of suggestions range between 1 and 20 (which is probably the default maximum number of suggestions for the API I used). \nSo, pretending that the number of suggestions is an indicator of how much we have to say, or how many different opinions we have, of each of the covered nationalities, \nhere's the top 15 demonyms people talk about, with the corresponding number of suggestions \n(proxy for \"the number of different things people ask about the said nationality). \n\n```text\nfrench         20\nsingaporean    20\ngerman         20\nbritish        20\nswiss          20\nenglish        19\nitalian        18\ncuban          18\ncanadian       18\nwelsh          18\naustralian     17\nmaltese        16\namerican       16\njapanese       14\nscottish       14\n```\n\n### Who do we least talk/ask about?\n\nConversely, here are the 19 demonyms that came back with only one suggestion.\n\n```text\nsomali          1\nbhutanese       1\nsyrian          1\ntongan          1\ncambodian       1\nmalagasy        1\nsaudi           1\nserbian         1\nczech           1\neritrean        1\nfinn            1\npuerto rican    1\npole            1\nhaitian         1\nhungarian       1\nperuvian        1\nmoroccan        1\nmongolian       1\nzambian         1\n```\n\n### What do we think about people?\n\nWhy are the French so...\n\nHow would you (if you're (un)lucky enough to know the French) finish this sentence?\nYou might even have several opinions about the French, and any other group of people you've rubbed shoulders with.\nWhat words would your palette contain to describe different nationalities?\nWhat words would others (at least those that ask questions to google) use?\n\nWell, here's what my auto-suggest search gave me. A set of 357 unique words and expressions to describe the 72 nationalities. \nSo a long tail of words use only for one nationality. But some words occur for more than one nationality. \nHere are the top 12 words/expressions used to describe people of the world. \n\n```text\nbeautiful         11\ntall              11\nshort              9\nnames long         8\nproud              8\nparents strict     8\nsmart              8\nnice               7\nboring             6\nrich               5\ndark               5\nsuccessful         5\n```\n\n### Who is beautiful? Who is tall? Who is short? Who is smart?\n\n```text\nbeautiful      : albanian, eritrean, ethiopian, filipino, iranian, lebanese, nepalese, pakistani, romanian, ukrainian, vietnamese\ntall           : australian, czech, german, nigerian, pakistani, samoan, senegalese, serbian, south korean, sudanese, taiwanese\nshort          : filipino, indonesian, italian, maltese, nepalese, pakistani, portuguese, singaporean, welsh\nnames long     : indian, malagasy, nigerian, portuguese, russian, sri lankan, thai, welsh\nproud          : albanian, ethiopian, filipino, iranian, lebanese, portuguese, scottish, welsh\nparents strict : albanian, ethiopian, haitian, indian, lebanese, pakistani, somali, sri lankan\nsmart          : indonesian, iranian, lebanese, pakistani, romanian, singaporean, taiwanese, vietnamese\nnice           : canadian, english, filipino, nepalese, portuguese, taiwanese, thai\nboring         : british, english, french, german, singaporean, swiss\nrich           : lebanese, pakistani, singaporean, taiwanese, vietnamese\ndark           : filipino, senegalese, sri lankan, vietnamese, welsh\nsuccessful     : chinese, english, japanese, lebanese, swiss\n```\n\n## How did I do it?\n\nI scraped a list of (country, demonym) pairs from a table in http://www.geography-site.co.uk/pages/countries/demonyms.html.\n\nThen I diagnosed these and manually made a mapping to simplify some \"complex\" entries, \nsuch as mapping an entry such as \"Irishman or Irishwoman or Irish\" to \"Irish\".\n\nUsing the google suggest API (http://suggestqueries.google.com/complete/search?client=chrome&q=), I requested what the suggestions \nfor `why are the $demonym so ` query pattern, for `$demonym` running through all 217 demonyms from the list above, \nstoring the results for each if the results were non-empty. \n\nThen, it was just a matter of pulling this data into memory, formatting it a bit, and creating a pandas dataframe that I could then interrogate.\n \n## Resources you can find here\n\nThe code to do this analysis yourself, from scratch here: `data_acquisition.py`.\n\nThe jupyter notebook I actually used when I developed this: `01 - Demonyms and adjectives - why are the french so....ipynb`\n \nNote you'll need to pip install py2store if you haven't already.\n\nIn the `data` folder you'll find\n* country_demonym.p: A pickle of a dataframe of countries and corresponding demonyms\n* country_demonym.xlsx: The same as above, but in excel form\n* demonym_suggested_characteristics.p: A pickle of 73 demonyms and auto-suggestion information, including characteristics. \n* what_we_think_about_demonyns.xlsx: An excel containing various statistics about demonyms and their (perceived) characteristics\n \n\n\n\n\n\n# Agglutinations\n\nInspired from a [tweet](https://twitter.com/raymondh/status/1311003482531401729) from Raymond Hettinger this morning:\n\n_Resist the urge to elide the underscore in multiword function or method names_\n\nSo I wondered...\n\n## Gluglus\n\nThe gluglu of a word is the number of partitions you can make of that word into words (of length at least 2 (so no using a or i)).\n(No \"gluglu\" isn't an actual term -- unless everyone starts using it from now on. \nBut it was inspired from an actual [linguistic term](https://en.wikipedia.org/wiki/Agglutination).)\n\nFor example, the gluglu of ``newspaper`` is 4:\n\n```\nnewspaper\n    new spa per\n    news pa per\n    news paper\n```\n\nEvery (valid) word has gluglu at least 1.\n\n\n## How many standard library names have gluglus at last 2?\n\n108\n\nHere's [the list](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/standard_lib_gluglus.txt) of all of them.\n\nThe winner has a gluglu of 6 (not 7 because formatannotationrelativeto isn't in the dictionary)\n\n```\nformatannotationrelativeto\n\tfor mat an not at ion relative to\n\tfor mat annotation relative to\n\tform at an not at ion relative to\n\tform at annotation relative to\n\tformat an not at ion relative to\n\tformat annotation relative to\n```\n\n## Details\n\n### Dictionary\n\nReally it depends on what dictionary we use. \nHere, I used a very conservative one. \nThe intersection of two lists: The [corncob](http://www.mieliestronk.com/corncob_lowercase.txt) \nand the [google10000](https://raw.githubusercontent.com/first20hours/google-10000-english/master/google-10000-english-usa.txt) word lists.\nAdditionally, I only kept of those, those that had at least 2 letters, and had only letters (no hyphens or disturbing diacritics).\n\nDiacritics. Look it up. Impress your next nerd date.\n\nIm left with 8116 words. You can find them [here](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/words_8116.csv).\n\n### Standard Lib Names\n\nSurprisingly, that was the hardest part. I know I'm missing some, but that's enough rabbit-holing. \n\nWhat I did (modulo some exceptions I won't look into) was to walk the standard lib modules (even that list wasn't a given!) \nextracting (recursively( the names of any (non-underscored) attributes if they were modules or callables, \nas well as extracting the arguments of these callables (when they had signatures).\n\nYou can find the code I used to extract these names [here](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/py_names.py) \nand the actual list [there](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/standard_lib_module_names.csv).\n\n\n\n# covid\n\n## Bar Chart Races (applied to covid-19 spread)\n\nThe module will show is how to make these:\n- Confirmed cases (by country): https://public.flourish.studio/visualisation/1704821/\n- Deaths (by country): https://public.flourish.studio/visualisation/1705644/\n- US Confirmed cases (by state): https://public.flourish.studio/visualisation/1794768/\n- US Deaths (by state): https://public.flourish.studio/visualisation/1794797/\n\n### The script\n\nIf you just want to run this as a script to get the job done, you have one here: \nhttps://raw.githubusercontent.com/thorwhalen/tapyoca/master/covid/covid_bar_chart_race.py\n\nRun like this\n```\n$ python covid_bar_chart_race.py -h\nusage: covid_bar_chart_race.py [-h] {mk-and-save-covid-data,update-covid-data,instructions-to-make-bar-chart-race} ...\n\npositional arguments:\n  {mk-and-save-covid-data,update-covid-data,instructions-to-make-bar-chart-race}\n    mk-and-save-covid-data\n                        :param data_sources: Dirpath or py2store Store where the data is :param kinds: The kinds of data you want to compute and save :param\n                        skip_first_days: :param verbose: :return:\n    update-covid-data   update the coronavirus data\n    instructions-to-make-bar-chart-race\n\noptional arguments:\n  -h, --help            show this help message and exit\n ```\n \n \n### The jupyter notebook\n\nThe notebook (the .ipynb file) shows you how to do it step by step in case you want to reuse the methods for other stuff.\n\n\n\n## Getting and preparing the data\n\nCorona virus data here: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset (direct download: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset/download). It's currently updated daily, so download a fresh copy if you want.\n\nPopulation data here: http://api.worldbank.org/v2/en/indicator/SP.POP.TOTL?downloadformat=csv\n\nIt comes under the form of a zip file (currently named `novel-corona-virus-2019-dataset.zip` with several `.csv` files in them. We use `py2store` (To install: `pip install py2store`. Project lives here: https://github.com/i2mint/py2store) to access and pre-prepare it. It allows us to not have to unzip the file and replace the older folder with it every time we download a new one. It also gives us the csvs as `pandas.DataFrame` already. \n\n\n```python\nimport pandas as pd\nfrom io import BytesIO\nfrom py2store import kv_wrap, ZipReader  # google it and pip install it\nfrom py2store.caching import mk_cached_store\nfrom py2store import QuickPickleStore\nfrom py2store.sources import FuncReader\n\ndef country_flag_image_url():\n    import pandas as pd\n    return pd.read_csv(\n        'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv')\n\ndef kaggle_coronavirus_dataset():\n    import kaggle\n    from io import BytesIO\n    # didn't find the pure binary download function, so using temp dir to emulate\n    from tempfile import mkdtemp  \n    download_dir = mkdtemp()\n    filename = 'novel-corona-virus-2019-dataset.zip'\n    zip_file = os.path.join(download_dir, filename)\n    \n    dataset = 'sudalairajkumar/novel-corona-virus-2019-dataset'\n    kaggle.api.dataset_download_files(dataset, download_dir)\n    with open(zip_file, 'rb') as fp:\n        b = fp.read()\n    return BytesIO(b)\n\ndef city_population_in_time():\n    import pandas as pd\n    return pd.read_csv(\n        'https://gist.githubusercontent.com/johnburnmurdoch/'\n        '4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations'\n    )\n\ndef country_flag_image_url_prep(df: pd.DataFrame):\n    # delete the region col (we don't need it)\n    del df['region']\n    # rewriting a few (not all) of the country names to match those found in kaggle covid data\n    # Note: The list is not complete! Add to it as needed\n    old_and_new = [('USA', 'US'), \n                   ('Iran, Islamic Rep.', 'Iran'), \n                   ('UK', 'United Kingdom'), \n                   ('Korea, Rep.', 'Korea, South')]\n    for old, new in old_and_new:\n        df['country'] = df['country'].replace(old, new)\n\n    return df\n\n\n@kv_wrap.outcoming_vals(lambda x: pd.read_csv(BytesIO(x)))  # this is to format the data as a dataframe\nclass ZippedCsvs(ZipReader):\n    pass\n# equivalent to ZippedCsvs = kv_wrap.outcoming_vals(lambda x: pd.read_csv(BytesIO(x)))(ZipReader)\n```\n\n\n```python\n# Enter here the place you want to cache your data\nmy_local_cache = os.path.expanduser('~/ddir/my_sources')\n```\n\n\n```python\nCachedFuncReader = mk_cached_store(FuncReader, QuickPickleStore(my_local_cache))\n```\n\n\n```python\ndata_sources = CachedFuncReader([country_flag_image_url, \n                                 kaggle_coronavirus_dataset, \n                                 city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ncovid_datasets = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\nlist(covid_datasets)\n```\n\n\n\n\n    ['COVID19_line_list_data.csv',\n     'COVID19_open_line_list.csv',\n     'covid_19_data.csv',\n     'time_series_covid_19_confirmed.csv',\n     'time_series_covid_19_confirmed_US.csv',\n     'time_series_covid_19_deaths.csv',\n     'time_series_covid_19_deaths_US.csv',\n     'time_series_covid_19_recovered.csv']\n\n\n\n\n```python\ncovid_datasets['time_series_covid_19_confirmed.csv'].head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Province/State</th>\n      <th>Country/Region</th>\n      <th>Lat</th>\n      <th>Long</th>\n      <th>1/22/20</th>\n      <th>1/23/20</th>\n      <th>1/24/20</th>\n      <th>1/25/20</th>\n      <th>1/26/20</th>\n      <th>1/27/20</th>\n      <th>...</th>\n      <th>3/24/20</th>\n      <th>3/25/20</th>\n      <th>3/26/20</th>\n      <th>3/27/20</th>\n      <th>3/28/20</th>\n      <th>3/29/20</th>\n      <th>3/30/20</th>\n      <th>3/31/20</th>\n      <th>4/1/20</th>\n      <th>4/2/20</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>Afghanistan</td>\n      <td>33.0000</td>\n      <td>65.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>74</td>\n      <td>84</td>\n      <td>94</td>\n      <td>110</td>\n      <td>110</td>\n      <td>120</td>\n      <td>170</td>\n      <td>174</td>\n      <td>237</td>\n      <td>273</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>Albania</td>\n      <td>41.1533</td>\n      <td>20.1683</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>123</td>\n      <td>146</td>\n      <td>174</td>\n      <td>186</td>\n      <td>197</td>\n      <td>212</td>\n      <td>223</td>\n      <td>243</td>\n      <td>259</td>\n      <td>277</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>Algeria</td>\n      <td>28.0339</td>\n      <td>1.6596</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>264</td>\n      <td>302</td>\n      <td>367</td>\n      <td>409</td>\n      <td>454</td>\n      <td>511</td>\n      <td>584</td>\n      <td>716</td>\n      <td>847</td>\n      <td>986</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>Andorra</td>\n      <td>42.5063</td>\n      <td>1.5218</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>164</td>\n      <td>188</td>\n      <td>224</td>\n      <td>267</td>\n      <td>308</td>\n      <td>334</td>\n      <td>370</td>\n      <td>376</td>\n      <td>390</td>\n      <td>428</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>Angola</td>\n      <td>-11.2027</td>\n      <td>17.8739</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>5</td>\n      <td>7</td>\n      <td>7</td>\n      <td>7</td>\n      <td>8</td>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 76 columns</p>\n</div>\n\n\n\n\n```python\ncountry_flag_image_url = data_sources['country_flag_image_url']\ncountry_flag_image_url.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nfrom IPython.display import Image\nflag_image_url_of_country = country_flag_image_url.set_index('country')['flag_image_url']\nImage(url=flag_image_url_of_country['Australia'])\n```\n\n\n\n\n<img src=\"https://www.countryflags.io/au/flat/64.png\"/>\n\n\n\n### Update coronavirus data\n\n\n```python\n# To update the coronavirus data:\ndef update_covid_data(data_sources):\n    \"\"\"update the coronavirus data\"\"\"\n    if 'kaggle_coronavirus_dataset' in data_sources._caching_store:\n        del data_sources._caching_store['kaggle_coronavirus_dataset']  # delete the cached item\n    _ = data_sources['kaggle_coronavirus_dataset']\n\n# update_covid_data(data_sources)  # uncomment here when you want to update\n```\n\n### Prepare data for flourish upload\n\n\n```python\nimport re\n\ndef print_if_verbose(verbose, *args, **kwargs):\n    if verbose:\n        print(*args, **kwargs)\n        \ndef country_data_for_data_kind(data_sources, kind='confirmed', skip_first_days=0, verbose=False):\n    \"\"\"kind can be 'confirmed', 'deaths', 'confirmed_US', 'confirmed_US', 'recovered'\"\"\"\n    \n    covid_datasets = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\n    \n    df = covid_datasets[f'time_series_covid_19_{kind}.csv']\n    # df = s['time_series_covid_19_deaths.csv']\n    if 'Province/State' in df.columns:\n        df.loc[df['Province/State'].isna(), 'Province/State'] = 'n/a'  # to avoid problems arising from NaNs\n\n    print_if_verbose(verbose, f\"Before data shape: {df.shape}\")\n\n    # drop some columns we don't need\n    p = re.compile('\\d+/\\d+/\\d+')\n\n    assert all(isinstance(x, str) for x in df.columns)\n    date_cols = [x for x in df.columns if p.match(x)]\n    if not kind.endswith('US'):\n        df = df.loc[:, ['Country/Region'] + date_cols]\n        # group countries and sum up the contributions of their states/regions/pargs\n        df['country'] = df.pop('Country/Region')\n        df = df.groupby('country').sum()\n    else:\n        df = df.loc[:, ['Province_State'] + date_cols]\n        df['state'] = df.pop('Province_State')\n        df = df.groupby('state').sum()\n\n    \n    print_if_verbose(verbose, f\"After data shape: {df.shape}\")\n    df = df.iloc[:, skip_first_days:]\n    \n    if not kind.endswith('US'):\n        # Joining with the country image urls and saving as an xls\n        country_image_url = country_flag_image_url_prep(data_sources['country_flag_image_url'])\n        t = df.copy()\n        t.columns = [str(x)[:10] for x in t.columns]\n        t = t.reset_index(drop=False)\n        t = country_image_url.merge(t, how='outer')\n        t = t.set_index('country')\n        df = t\n    else:    \n        pass\n\n    return df\n\n\ndef mk_and_save_country_data_for_data_kind(data_sources, kind='confirmed', skip_first_days=0, verbose=False):\n    t = country_data_for_data_kind(data_sources, kind, skip_first_days, verbose)\n    filepath = f'country_covid_{kind}.xlsx'\n    t.to_excel(filepath)\n    print_if_verbose(verbose, f\"Was saved here: {filepath}\")\n\n```\n\n\n```python\n# for kind in ['confirmed', 'deaths', 'recovered', 'confirmed_US', 'deaths_US']:\nfor kind in ['confirmed', 'deaths', 'recovered', 'confirmed_US', 'deaths_US']:\n    mk_and_save_country_data_for_data_kind(data_sources, kind=kind, skip_first_days=39, verbose=True)\n```\n\n    Before data shape: (262, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_confirmed.xlsx\n    Before data shape: (262, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_deaths.xlsx\n    Before data shape: (248, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_recovered.xlsx\n    Before data shape: (3253, 86)\n    After data shape: (58, 75)\n    Was saved here: country_covid_confirmed_US.xlsx\n    Before data shape: (3253, 87)\n    After data shape: (58, 75)\n    Was saved here: country_covid_deaths_US.xlsx\n\n\n### Upload to Flourish, tune, and publish\n\nGo to https://public.flourish.studio/, get a free account, and play.\n\nGot to https://app.flourish.studio/templates\n\nChoose \"Bar chart race\". At the time of writing this, it was here: https://app.flourish.studio/visualisation/1706060/\n\n... and then play with the settings\n\n\n## Discussion of the methods\n\n\n```python\nfrom py2store import *\nfrom IPython.display import Image\n```\n\n### country flags images\n\nThe manual data prep looks something like this.\n\n\n```python\nimport pandas as pd\n\n# get the csv data from the url\ncountry_image_url_source = \\\n    'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv'\ncountry_image_url = pd.read_csv(country_image_url_source)\n\n# delete the region col (we don't need it)\ndel country_image_url['region']\n\n# rewriting a few (not all) of the country names to match those found in kaggle covid data\n# Note: The list is not complete! Add to it as needed\n# TODO: (Wishful) Using a general smart soft-matching algorithm to do this automatically.\n# TODO:    This could use edit-distance, synonyms, acronym generation, etc.\nold_and_new = [('USA', 'US'), \n               ('Iran, Islamic Rep.', 'Iran'), \n               ('UK', 'United Kingdom'), \n               ('Korea, Rep.', 'Korea, South')]\nfor old, new in old_and_new:\n    country_image_url['country'] = country_image_url['country'].replace(old, new)\n\nimage_url_of_country = country_image_url.set_index('country')['flag_image_url']\n\ncountry_image_url.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nImage(url=image_url_of_country['Australia'])\n```\n\n\n\n\n<img src=\"https://www.countryflags.io/au/flat/64.png\"/>\n\n\n\n### Caching the flag images data\n\nDownloading our data sources every time we need them is not sustainable. What if they're big? What if you're offline or have slow internet (yes, dear future reader, even in the US, during coronavirus times!)?\n\nCaching. A \"cache aside\" read-cache. That's the word. py2store has tools for that (most of which are are caching.py). \n\nSo let's say we're going to have a local folder where we'll store various datas we download. The principle is as follows:\n\n\n```python\nfrom py2store.caching import mk_cached_store\n\nclass TheSource(dict): ...\nthe_cache = {}\nTheCacheSource = mk_cached_store(TheSource, the_cache)\n\nthe_source = TheSource({'green': 'eggs', 'and': 'ham'})\n\nthe_cached_source = TheCacheSource(the_source)\nprint(f\"the_cache: {the_cache}\")\nprint(f\"Getting green...\")\nthe_cached_source['green']\nprint(f\"the_cache: {the_cache}\")\nprint(\"... so the next time the_cached_source will get it's green from that the_cache\")\n```\n\n    the_cache: {}\n    Getting green...\n    the_cache: {'green': 'eggs'}\n    ... so the next time the_cached_source will get it's green from that the_cache\n\n\nBut now, you'll notice a slight problem ahead. What exactly does our source store (or rather reader) looks like? In it's raw form it would take urls as it's keys, and the response of a request as it's value. That store wouldn't have an `__iter__` for sure (unless you're Google). But more to the point here, the `mk_cached_store` tool uses the same key for the source and the cache, and we can't just use the url as is, to be a local file path. \n\nThere's many ways we could solve this. One way is to add a key map layer on the cache store, so externally, it speaks the url key language, but internally it will map that url to a valid local file path. We've been there, we got the T-shirt!\n\nBut what we're going to do is a bit different: We're going to do the key mapping in the source store itself. It seems to make more sense in our context: We have a data source of `name: data` pairs, and if we impose that the name should be a valid file name, we don't need to have a key map in the cache store.\n\nSo let's start by building this `MyDataStore` store. We'll start by defining the functions that get us the data we want. \n\n\n```python\ndef country_flag_image_url():\n    import pandas as pd\n    return pd.read_csv(\n        'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv')\n\ndef kaggle_coronavirus_dataset():\n    import kaggle\n    from io import BytesIO\n    # didn't find the pure binary download function, so using temp dir to emulate\n    from tempfile import mkdtemp  \n    download_dir = mkdtemp()\n    filename = 'novel-corona-virus-2019-dataset.zip'\n    zip_file = os.path.join(download_dir, filename)\n    \n    dataset = 'sudalairajkumar/novel-corona-virus-2019-dataset'\n    kaggle.api.dataset_download_files(dataset, download_dir)\n    with open(zip_file, 'rb') as fp:\n        b = fp.read()\n    return BytesIO(b)\n\ndef city_population_in_time():\n    import pandas as pd\n    return pd.read_csv(\n        'https://gist.githubusercontent.com/johnburnmurdoch/'\n        '4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations'\n    )\n```\n\nNow we can make a store that simply uses these function names as the keys, and their returned value as the values.\n\n\n```python\nfrom py2store.base import KvReader\nfrom functools import lru_cache\n\nclass FuncReader(KvReader):\n    _getitem_cache_size = 999\n    def __init__(self, funcs):\n        # TODO: assert no free arguments (arguments are allowed but must all have defaults)\n        self.funcs = funcs\n        self._func_of_name = {func.__name__: func for func in funcs}\n\n    def __contains__(self, k):\n        return k in self._func_of_name\n    \n    def __iter__(self):\n        yield from self._func_of_name\n        \n    def __len__(self):\n        return len(self._func_of_name)\n\n    @lru_cache(maxsize=_getitem_cache_size)\n    def __getitem__(self, k):\n        return self._func_of_name[k]()  # call the func\n    \n    def __hash__(self):\n        return 1\n    \n```\n\n\n```python\ndata_sources = FuncReader([country_flag_image_url, kaggle_coronavirus_dataset, city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['city_population_in_time']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>group</th>\n      <th>year</th>\n      <th>value</th>\n      <th>subGroup</th>\n      <th>city_id</th>\n      <th>lastValue</th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1575</td>\n      <td>200.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1576</td>\n      <td>212.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1577</td>\n      <td>224.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>212.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1578</td>\n      <td>236.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>224.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1579</td>\n      <td>248.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>236.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6247</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1561</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6248</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1562</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6249</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1563</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6250</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1564</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6251</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1565</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n  </tbody>\n</table>\n<p>6252 rows \u00d7 9 columns</p>\n</div>\n\n\n\nBut we wanted this all to be cached locally, right? So a few more lines to do that!\n\n\n```python\nfrom py2store.caching import mk_cached_store\nfrom py2store import QuickPickleStore\n    \nmy_local_cache = os.path.expanduser('~/ddir/my_sources')\n\nCachedFuncReader = mk_cached_store(FuncReader, QuickPickleStore(my_local_cache))\n```\n\n\n```python\ndata_sources = CachedFuncReader([country_flag_image_url, kaggle_coronavirus_dataset, city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['city_population_in_time']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>group</th>\n      <th>year</th>\n      <th>value</th>\n      <th>subGroup</th>\n      <th>city_id</th>\n      <th>lastValue</th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1575</td>\n      <td>200.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1576</td>\n      <td>212.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1577</td>\n      <td>224.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>212.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1578</td>\n      <td>236.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>224.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1579</td>\n      <td>248.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>236.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6247</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1561</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6248</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1562</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6249</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1563</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6250</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1564</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6251</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1565</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n  </tbody>\n</table>\n<p>6252 rows \u00d7 9 columns</p>\n</div>\n\n\n\n\n```python\nz = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\nlist(z)\n```\n",
    "long_description_content_type": "text/markdown",
    "description_file": "README.md",
    "root_url": "https://github.com/thorwhalen",
    "description": "A medley of things that got coded because there was an itch to do so",
    "author": "thorwhalen",
    "license": "Apache Software License",
    "description-file": "README.md",
    "install_requires": [],
    "keywords": [
      "documentation",
      "packaging",
      "publishing"
    ]
  }/usr/lib/python3.13/site-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'description_file'
    warnings.warn(msg)
  /usr/lib/python3.13/site-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'root_url'
    warnings.warn(msg)
  /usr/lib/python3.13/site-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'description-file'
    warnings.warn(msg)
  /usr/lib/python3.13/site-packages/setuptools/dist.py:476: SetuptoolsDeprecationWarning: Invalid dash-separated options
  !!

          ********************************************************************************
          Usage of dash-separated 'description-file' will not be supported in future
          versions. Please use the underscore name 'description_file' instead.

          This deprecation is overdue, please update your project and remove deprecated
          calls to avoid build errors in the future.

          See https://setuptools.pypa.io/en/latest/userguide/declarative_config.html for details.
          ********************************************************************************

  !!
    opt = self.warn_dash_deprecation(opt, section)

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  {
    "name": "tapyoca",
    "version": "0.0.4",
    "url": "https://github.com/thorwhalen/tapyoca",
    "packages": [
      "tapyoca",
      "tapyoca.agglutination",
      "tapyoca.covid",
      "tapyoca.darpa",
      "tapyoca.demonyms",
      "tapyoca.indexing_podcasts",
      "tapyoca.parquet_deformations",
      "tapyoca.phoneming"
    ],
    "include_package_data": true,
    "platforms": "any",
    "long_description": "# tapyoca\nA medley of small projects\n\n\n# parquet_deformations\n\nI'm calling these [Parquet deformations](https://www.theguardian.com/artanddesign/alexs-adventures-in-numberland/2014/sep/09/crazy-paving-the-twisted-world-of-parquet-deformations#:~:text=In%20the%201960s%20an%20American,the%20regularity%20of%20the%20tiling.) but purest would lynch me. \n\nReally, I just wanted to transform one word into another word, gradually, as I've seen in some of [Escher's](https://en.wikipedia.org/wiki/M._C._Escher) work, so I looked it up, and saw that it's called parquet deformations. The math looked enticing, but I had no time for that, so I did the first way I could think of: Mapping pixels to pixels (in some fashion -- but nearest neighbors is the method that yields nicest results, under the pixel-level restriction). \n\nOf course, this can be applied to any image (that will be transformed to B/W (not even gray -- I mean actual B/W), and there's several ways you can perform the parquet (I like the gif rendering). \n\nThe main function (exposed as a script) is `mk_deformation_image`. All you need is to specify two images (or words). If you want, of course, you can specify:\n- `n_steps`: Number of steps from start to end image\n- `save_to_file`: path to file to save too (if not given, will just return the image object)\n- `kind`: 'gif', 'horizontal_stack', or 'vertical_stack'\n- `coordinate_mapping_maker`: A function that will return the mapping between start and end. \nThis function should return a pair (`from_coord`, `to_coord`) of aligned matrices whose 2 columns are the the \n`(x, y)` coordinates, and the rows represent aligned positions that should be mapped. \n\n\n\n## Examples\n\n### Two words...\n\n\n```python\nfit_to_size = 400\nstart_im = image_of_text('sensor').rotate(90, expand=1)\nend_im = image_of_text('meaning').rotate(90, expand=1)\nstart_and_end_image(start_im, end_im)\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_5_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 15, kind='h').resize((500,200))\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_6_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im.transpose(4), end_im.transpose(4), 5, kind='v').resize((200,200))\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_7_0.png)\n\n\n\n\n```python\nf = 'sensor_meaning_knn.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_knn.gif?76128495\">\n\n\n\n\n```python\nf = 'sensor_meaning_scan.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f, \n                     coordinate_mapping_maker='scan')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_scan.gif?76996026\">\n\n\n\n\n```python\nf = 'sensor_meaning_random.gif'\nmk_deformation_image(start_im.transpose(4), end_im.transpose(4), n_steps=20, save_to_file=f, \n                     coordinate_mapping_maker='random')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning_random.gif?80233280\">\n\n\n\n### From a list of words\n\n\n```python\nstart_words = ['sensor', 'vibration', 'tempature']\nend_words = ['sense', 'meaning', 'detection']\nstart_im, end_im = make_start_and_end_images_with_words(\n    start_words, end_words, perm=True, repeat=2, size=150)\nstart_and_end_image(start_im, end_im).resize((600, 200))\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_12_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 5)\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_13_0.png)\n\n\n\n\n```python\nf = 'bunch_of_words.gif'\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"bunch_of_words.gif?7402792\">\n\n\n\n## From files\n\n\n```python\nstart_im = Image.open('sensor_strip_01.png')\nend_im = Image.open('sense_strip_01.png')\nstart_and_end_image(start_im.resize((200, 500)), end_im.resize((200, 500)))\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_16_0.png)\n\n\n\n\n```python\nim = mk_deformation_image(start_im, end_im, 7)\nim\n```\n\n\n\n\n![png](tapyoca/parquet_deformations/img/outputs/output_17_0.png)\n\n\n\n\n```python\nf = 'medley.gif'\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f)\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"medley.gif?39255021\">\n\n\n\n\n```python\nmk_deformation_image(start_im, end_im, n_steps=20, save_to_file=f, coordinate_mapping_maker='scan')\ndisplay_gif(f)\n```\n\n\n\n\n<img src=\"sensor_meaning.gif?41172115\">\n\n\n\n## an image and some text\n\n\n```python\nstart_im = 'img/waveform_01.png'  # will first look for a file, and if not consider as text\nend_im = 'makes sense'\n\nmk_gif_of_deformations(start_im, end_im, n_steps=20, \n                               save_to_file='image_and_text.gif')\ndisplay_gif('image_and_text.gif')  \n```\n\n\n\n\n<img src=\"image_and_text.gif?92524789\">\n\n\n\n\n\n\n# demonys\n\n## What do we think about other peoples?\n\nThis project is meant to get an idea of what people think of people for different nations, as seen by what they ask google about them. \n\nHere I use python code to acquire, clean up, and analyze the data. \n\n### Demonym\n\nIf you're like me and enjoy the false and fleeting impression of superiority that comes when you know a word someone else doesn't. If you're like me and go to parties for the sole purpose of seeking victims to get a one-up on, here's a cool word to add to your arsenal:\n\n**demonym**: a noun used to denote the natives or inhabitants of a particular country, state, city, etc.\n_\"he struggled for the correct demonym for the people of Manchester\"_\n\n### Back-story of this analysis\n \nDuring a discussion (about traveling in Europe) someone said \"why are the swiss so miserable\". Now, I wouldn't say that the swiss were especially miserable (a couple of ex-girlfriends aside), but to be fair he was contrasting with Italians, so perhaps he has a point. I apologize if you are swiss, or one of the two ex-girlfriends -- nothing personal, this is all for effect. \n\nWe googled \"why are the swiss so \", and sure enough, \"why are the swiss so miserable\" came up as one of the suggestions. So we got curious and started googling other peoples: the French, the Germans, etc.\n\nThat's the back-story of this analysis. This analysis is meant to get an idea of what we think of peoples from other countries. Of course, one can rightfully critique the approach I'll take to gauge \"what we think\" -- all three of these words should, but will not, be defined. I'm just going to see what google's *current* auto-suggest comes back with when I enter \"why are the X so \" (where X will be a noun that denotes the natives of inhabitants of a particular country; a *demonym* if you will). \n\n### Warning\n\nAgain, word of warning: All data and analyses are biased. \nTake everything you'll read here (and to be fair, what you read anywhere) with a grain of salt. \nFor simplicitly I'll saying things like \"what we think of...\" or \"who do we most...\", etc.\nBut I don't **really** mean that.\n\n### Resources\n\n* http://www.geography-site.co.uk/pages/countries/demonyms.html for my list of demonyms.\n* google for my suggestion engine, using the url prefix: `http://suggestqueries.google.com/complete/search?client=chrome&q=`\n\n\n## The results\n\n### In a nutshell\n\nBelow is listed 73 demonyms along with words extracted from the very first google suggestion when you type. \n\n`why are the DEMONYM so `\n\n```text\nafghan    \t                eyes beautiful\nalbanian  \t                     beautiful\namerican  \t          girl dolls expensive\naustralian\t                          tall\nbelgian   \t                    fries good\nbhutanese \t                         happy\nbrazilian \t              good at football\nbritish   \t     full of grief and despair\nbulgarian \t              properties cheap\nburmese   \t             cats affectionate\ncambodian \t                   cows skinny\ncanadian  \t                          nice\nchinese   \t                       healthy\ncolombian \t                  avocados big\ncuban     \t                   cigars good\nczech     \t                          tall\ndominican \t  republic and haiti different\negyptian  \t                gods important\nenglish   \t                      reserved\neritrean  \t                     beautiful\nethiopian \t                     beautiful\nfilipino  \t                         proud\nfinn      \t               shoes expensive\nfrench    \t                       healthy\ngerman    \t                          tall\ngreek     \t                gods messed up\nhaitian   \t                parents strict\nhungarian \t                    words long\nindian    \t            tv debates chaotic\nindonesian\t                         smart\niranian   \t                     beautiful\nisraeli   \t           startups successful\nitalian   \t                         short\njamaican  \t                sprinters fast\njapanese  \t                        polite\nkenyan    \t                  runners good\nlebanese  \t                          rich\nmalagasy  \t                    names long\nmalaysian \t                   drivers bad\nmaltese   \t                          rude\nmongolian \t                  horses small\nmoroccan  \t                rugs expensive\nnepalese  \t                     beautiful\nnigerian  \t                          tall\nnorth korean\t                      hats big\nnorwegian \t                 flights cheap\npakistani \t                          fair\nperuvian  \t               blueberries big\npole      \t                  vaulters hot\nportuguese\t                         short\npuerto rican\t       and cuban flags similar\nromanian  \t                     beautiful\nrussian   \t                  good at math\nsamoan    \t                           big\nsaudi     \t                      arrogant\nscottish  \t                        bitter\nsenegalese\t                          tall\nserbian   \t                          tall\nsingaporean\t                          rude\nsomali    \t                parents strict\nsouth african\t                     plugs big\nsouth korean\t                          tall\nsri lankan\t                          dark\nsudanese  \t                          tall\nswiss     \t        good at making watches\nsyrian    \t                families large\ntaiwanese \t                        pretty\nthai      \t                        pretty\ntongan    \t                           big\nukrainian \t                     beautiful\nvietnamese\t        fiercely nationalistic\nwelsh     \t                          dark\nzambian   \t                emeralds cheap\n```\n\n\nNotes:\n* The queries actually have a space after the \"so\", which matters so as to omit suggestions containing words that start with so.\n* Only the tail of the suggestion is shown -- minus prefix (`why are the DEMONYM` or `why are DEMONYM`) as well as the `so`, where ever it lands in the suggestion. \nFor example, the first suggestion for the american demonym was \"why are american dolls so expensive\", which results in the \"dolls expensive\" association. \n\n\n### Who do we most talk/ask about?\n\nThe original list contained 217 demonyms, but many of these yielded no suggestions (to the specific query format I used, that is). \nOnly 73 demonyms gave me at least one suggestion. \nBut within those, number of suggestions range between 1 and 20 (which is probably the default maximum number of suggestions for the API I used). \nSo, pretending that the number of suggestions is an indicator of how much we have to say, or how many different opinions we have, of each of the covered nationalities, \nhere's the top 15 demonyms people talk about, with the corresponding number of suggestions \n(proxy for \"the number of different things people ask about the said nationality). \n\n```text\nfrench         20\nsingaporean    20\ngerman         20\nbritish        20\nswiss          20\nenglish        19\nitalian        18\ncuban          18\ncanadian       18\nwelsh          18\naustralian     17\nmaltese        16\namerican       16\njapanese       14\nscottish       14\n```\n\n### Who do we least talk/ask about?\n\nConversely, here are the 19 demonyms that came back with only one suggestion.\n\n```text\nsomali          1\nbhutanese       1\nsyrian          1\ntongan          1\ncambodian       1\nmalagasy        1\nsaudi           1\nserbian         1\nczech           1\neritrean        1\nfinn            1\npuerto rican    1\npole            1\nhaitian         1\nhungarian       1\nperuvian        1\nmoroccan        1\nmongolian       1\nzambian         1\n```\n\n### What do we think about people?\n\nWhy are the French so...\n\nHow would you (if you're (un)lucky enough to know the French) finish this sentence?\nYou might even have several opinions about the French, and any other group of people you've rubbed shoulders with.\nWhat words would your palette contain to describe different nationalities?\nWhat words would others (at least those that ask questions to google) use?\n\nWell, here's what my auto-suggest search gave me. A set of 357 unique words and expressions to describe the 72 nationalities. \nSo a long tail of words use only for one nationality. But some words occur for more than one nationality. \nHere are the top 12 words/expressions used to describe people of the world. \n\n```text\nbeautiful         11\ntall              11\nshort              9\nnames long         8\nproud              8\nparents strict     8\nsmart              8\nnice               7\nboring             6\nrich               5\ndark               5\nsuccessful         5\n```\n\n### Who is beautiful? Who is tall? Who is short? Who is smart?\n\n```text\nbeautiful      : albanian, eritrean, ethiopian, filipino, iranian, lebanese, nepalese, pakistani, romanian, ukrainian, vietnamese\ntall           : australian, czech, german, nigerian, pakistani, samoan, senegalese, serbian, south korean, sudanese, taiwanese\nshort          : filipino, indonesian, italian, maltese, nepalese, pakistani, portuguese, singaporean, welsh\nnames long     : indian, malagasy, nigerian, portuguese, russian, sri lankan, thai, welsh\nproud          : albanian, ethiopian, filipino, iranian, lebanese, portuguese, scottish, welsh\nparents strict : albanian, ethiopian, haitian, indian, lebanese, pakistani, somali, sri lankan\nsmart          : indonesian, iranian, lebanese, pakistani, romanian, singaporean, taiwanese, vietnamese\nnice           : canadian, english, filipino, nepalese, portuguese, taiwanese, thai\nboring         : british, english, french, german, singaporean, swiss\nrich           : lebanese, pakistani, singaporean, taiwanese, vietnamese\ndark           : filipino, senegalese, sri lankan, vietnamese, welsh\nsuccessful     : chinese, english, japanese, lebanese, swiss\n```\n\n## How did I do it?\n\nI scraped a list of (country, demonym) pairs from a table in http://www.geography-site.co.uk/pages/countries/demonyms.html.\n\nThen I diagnosed these and manually made a mapping to simplify some \"complex\" entries, \nsuch as mapping an entry such as \"Irishman or Irishwoman or Irish\" to \"Irish\".\n\nUsing the google suggest API (http://suggestqueries.google.com/complete/search?client=chrome&q=), I requested what the suggestions \nfor `why are the $demonym so ` query pattern, for `$demonym` running through all 217 demonyms from the list above, \nstoring the results for each if the results were non-empty. \n\nThen, it was just a matter of pulling this data into memory, formatting it a bit, and creating a pandas dataframe that I could then interrogate.\n \n## Resources you can find here\n\nThe code to do this analysis yourself, from scratch here: `data_acquisition.py`.\n\nThe jupyter notebook I actually used when I developed this: `01 - Demonyms and adjectives - why are the french so....ipynb`\n \nNote you'll need to pip install py2store if you haven't already.\n\nIn the `data` folder you'll find\n* country_demonym.p: A pickle of a dataframe of countries and corresponding demonyms\n* country_demonym.xlsx: The same as above, but in excel form\n* demonym_suggested_characteristics.p: A pickle of 73 demonyms and auto-suggestion information, including characteristics. \n* what_we_think_about_demonyns.xlsx: An excel containing various statistics about demonyms and their (perceived) characteristics\n \n\n\n\n\n\n# Agglutinations\n\nInspired from a [tweet](https://twitter.com/raymondh/status/1311003482531401729) from Raymond Hettinger this morning:\n\n_Resist the urge to elide the underscore in multiword function or method names_\n\nSo I wondered...\n\n## Gluglus\n\nThe gluglu of a word is the number of partitions you can make of that word into words (of length at least 2 (so no using a or i)).\n(No \"gluglu\" isn't an actual term -- unless everyone starts using it from now on. \nBut it was inspired from an actual [linguistic term](https://en.wikipedia.org/wiki/Agglutination).)\n\nFor example, the gluglu of ``newspaper`` is 4:\n\n```\nnewspaper\n    new spa per\n    news pa per\n    news paper\n```\n\nEvery (valid) word has gluglu at least 1.\n\n\n## How many standard library names have gluglus at last 2?\n\n108\n\nHere's [the list](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/standard_lib_gluglus.txt) of all of them.\n\nThe winner has a gluglu of 6 (not 7 because formatannotationrelativeto isn't in the dictionary)\n\n```\nformatannotationrelativeto\n\tfor mat an not at ion relative to\n\tfor mat annotation relative to\n\tform at an not at ion relative to\n\tform at annotation relative to\n\tformat an not at ion relative to\n\tformat annotation relative to\n```\n\n## Details\n\n### Dictionary\n\nReally it depends on what dictionary we use. \nHere, I used a very conservative one. \nThe intersection of two lists: The [corncob](http://www.mieliestronk.com/corncob_lowercase.txt) \nand the [google10000](https://raw.githubusercontent.com/first20hours/google-10000-english/master/google-10000-english-usa.txt) word lists.\nAdditionally, I only kept of those, those that had at least 2 letters, and had only letters (no hyphens or disturbing diacritics).\n\nDiacritics. Look it up. Impress your next nerd date.\n\nIm left with 8116 words. You can find them [here](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/words_8116.csv).\n\n### Standard Lib Names\n\nSurprisingly, that was the hardest part. I know I'm missing some, but that's enough rabbit-holing. \n\nWhat I did (modulo some exceptions I won't look into) was to walk the standard lib modules (even that list wasn't a given!) \nextracting (recursively( the names of any (non-underscored) attributes if they were modules or callables, \nas well as extracting the arguments of these callables (when they had signatures).\n\nYou can find the code I used to extract these names [here](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/py_names.py) \nand the actual list [there](https://github.com/thorwhalen/tapyoca/blob/master/tapyoca/agglutination/standard_lib_module_names.csv).\n\n\n\n# covid\n\n## Bar Chart Races (applied to covid-19 spread)\n\nThe module will show is how to make these:\n- Confirmed cases (by country): https://public.flourish.studio/visualisation/1704821/\n- Deaths (by country): https://public.flourish.studio/visualisation/1705644/\n- US Confirmed cases (by state): https://public.flourish.studio/visualisation/1794768/\n- US Deaths (by state): https://public.flourish.studio/visualisation/1794797/\n\n### The script\n\nIf you just want to run this as a script to get the job done, you have one here: \nhttps://raw.githubusercontent.com/thorwhalen/tapyoca/master/covid/covid_bar_chart_race.py\n\nRun like this\n```\n$ python covid_bar_chart_race.py -h\nusage: covid_bar_chart_race.py [-h] {mk-and-save-covid-data,update-covid-data,instructions-to-make-bar-chart-race} ...\n\npositional arguments:\n  {mk-and-save-covid-data,update-covid-data,instructions-to-make-bar-chart-race}\n    mk-and-save-covid-data\n                        :param data_sources: Dirpath or py2store Store where the data is :param kinds: The kinds of data you want to compute and save :param\n                        skip_first_days: :param verbose: :return:\n    update-covid-data   update the coronavirus data\n    instructions-to-make-bar-chart-race\n\noptional arguments:\n  -h, --help            show this help message and exit\n ```\n \n \n### The jupyter notebook\n\nThe notebook (the .ipynb file) shows you how to do it step by step in case you want to reuse the methods for other stuff.\n\n\n\n## Getting and preparing the data\n\nCorona virus data here: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset (direct download: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset/download). It's currently updated daily, so download a fresh copy if you want.\n\nPopulation data here: http://api.worldbank.org/v2/en/indicator/SP.POP.TOTL?downloadformat=csv\n\nIt comes under the form of a zip file (currently named `novel-corona-virus-2019-dataset.zip` with several `.csv` files in them. We use `py2store` (To install: `pip install py2store`. Project lives here: https://github.com/i2mint/py2store) to access and pre-prepare it. It allows us to not have to unzip the file and replace the older folder with it every time we download a new one. It also gives us the csvs as `pandas.DataFrame` already. \n\n\n```python\nimport pandas as pd\nfrom io import BytesIO\nfrom py2store import kv_wrap, ZipReader  # google it and pip install it\nfrom py2store.caching import mk_cached_store\nfrom py2store import QuickPickleStore\nfrom py2store.sources import FuncReader\n\ndef country_flag_image_url():\n    import pandas as pd\n    return pd.read_csv(\n        'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv')\n\ndef kaggle_coronavirus_dataset():\n    import kaggle\n    from io import BytesIO\n    # didn't find the pure binary download function, so using temp dir to emulate\n    from tempfile import mkdtemp  \n    download_dir = mkdtemp()\n    filename = 'novel-corona-virus-2019-dataset.zip'\n    zip_file = os.path.join(download_dir, filename)\n    \n    dataset = 'sudalairajkumar/novel-corona-virus-2019-dataset'\n    kaggle.api.dataset_download_files(dataset, download_dir)\n    with open(zip_file, 'rb') as fp:\n        b = fp.read()\n    return BytesIO(b)\n\ndef city_population_in_time():\n    import pandas as pd\n    return pd.read_csv(\n        'https://gist.githubusercontent.com/johnburnmurdoch/'\n        '4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations'\n    )\n\ndef country_flag_image_url_prep(df: pd.DataFrame):\n    # delete the region col (we don't need it)\n    del df['region']\n    # rewriting a few (not all) of the country names to match those found in kaggle covid data\n    # Note: The list is not complete! Add to it as needed\n    old_and_new = [('USA', 'US'), \n                   ('Iran, Islamic Rep.', 'Iran'), \n                   ('UK', 'United Kingdom'), \n                   ('Korea, Rep.', 'Korea, South')]\n    for old, new in old_and_new:\n        df['country'] = df['country'].replace(old, new)\n\n    return df\n\n\n@kv_wrap.outcoming_vals(lambda x: pd.read_csv(BytesIO(x)))  # this is to format the data as a dataframe\nclass ZippedCsvs(ZipReader):\n    pass\n# equivalent to ZippedCsvs = kv_wrap.outcoming_vals(lambda x: pd.read_csv(BytesIO(x)))(ZipReader)\n```\n\n\n```python\n# Enter here the place you want to cache your data\nmy_local_cache = os.path.expanduser('~/ddir/my_sources')\n```\n\n\n```python\nCachedFuncReader = mk_cached_store(FuncReader, QuickPickleStore(my_local_cache))\n```\n\n\n```python\ndata_sources = CachedFuncReader([country_flag_image_url, \n                                 kaggle_coronavirus_dataset, \n                                 city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ncovid_datasets = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\nlist(covid_datasets)\n```\n\n\n\n\n    ['COVID19_line_list_data.csv',\n     'COVID19_open_line_list.csv',\n     'covid_19_data.csv',\n     'time_series_covid_19_confirmed.csv',\n     'time_series_covid_19_confirmed_US.csv',\n     'time_series_covid_19_deaths.csv',\n     'time_series_covid_19_deaths_US.csv',\n     'time_series_covid_19_recovered.csv']\n\n\n\n\n```python\ncovid_datasets['time_series_covid_19_confirmed.csv'].head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Province/State</th>\n      <th>Country/Region</th>\n      <th>Lat</th>\n      <th>Long</th>\n      <th>1/22/20</th>\n      <th>1/23/20</th>\n      <th>1/24/20</th>\n      <th>1/25/20</th>\n      <th>1/26/20</th>\n      <th>1/27/20</th>\n      <th>...</th>\n      <th>3/24/20</th>\n      <th>3/25/20</th>\n      <th>3/26/20</th>\n      <th>3/27/20</th>\n      <th>3/28/20</th>\n      <th>3/29/20</th>\n      <th>3/30/20</th>\n      <th>3/31/20</th>\n      <th>4/1/20</th>\n      <th>4/2/20</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>NaN</td>\n      <td>Afghanistan</td>\n      <td>33.0000</td>\n      <td>65.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>74</td>\n      <td>84</td>\n      <td>94</td>\n      <td>110</td>\n      <td>110</td>\n      <td>120</td>\n      <td>170</td>\n      <td>174</td>\n      <td>237</td>\n      <td>273</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>Albania</td>\n      <td>41.1533</td>\n      <td>20.1683</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>123</td>\n      <td>146</td>\n      <td>174</td>\n      <td>186</td>\n      <td>197</td>\n      <td>212</td>\n      <td>223</td>\n      <td>243</td>\n      <td>259</td>\n      <td>277</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>Algeria</td>\n      <td>28.0339</td>\n      <td>1.6596</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>264</td>\n      <td>302</td>\n      <td>367</td>\n      <td>409</td>\n      <td>454</td>\n      <td>511</td>\n      <td>584</td>\n      <td>716</td>\n      <td>847</td>\n      <td>986</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>Andorra</td>\n      <td>42.5063</td>\n      <td>1.5218</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>164</td>\n      <td>188</td>\n      <td>224</td>\n      <td>267</td>\n      <td>308</td>\n      <td>334</td>\n      <td>370</td>\n      <td>376</td>\n      <td>390</td>\n      <td>428</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>Angola</td>\n      <td>-11.2027</td>\n      <td>17.8739</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>5</td>\n      <td>7</td>\n      <td>7</td>\n      <td>7</td>\n      <td>8</td>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 76 columns</p>\n</div>\n\n\n\n\n```python\ncountry_flag_image_url = data_sources['country_flag_image_url']\ncountry_flag_image_url.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nfrom IPython.display import Image\nflag_image_url_of_country = country_flag_image_url.set_index('country')['flag_image_url']\nImage(url=flag_image_url_of_country['Australia'])\n```\n\n\n\n\n<img src=\"https://www.countryflags.io/au/flat/64.png\"/>\n\n\n\n### Update coronavirus data\n\n\n```python\n# To update the coronavirus data:\ndef update_covid_data(data_sources):\n    \"\"\"update the coronavirus data\"\"\"\n    if 'kaggle_coronavirus_dataset' in data_sources._caching_store:\n        del data_sources._caching_store['kaggle_coronavirus_dataset']  # delete the cached item\n    _ = data_sources['kaggle_coronavirus_dataset']\n\n# update_covid_data(data_sources)  # uncomment here when you want to update\n```\n\n### Prepare data for flourish upload\n\n\n```python\nimport re\n\ndef print_if_verbose(verbose, *args, **kwargs):\n    if verbose:\n        print(*args, **kwargs)\n        \ndef country_data_for_data_kind(data_sources, kind='confirmed', skip_first_days=0, verbose=False):\n    \"\"\"kind can be 'confirmed', 'deaths', 'confirmed_US', 'confirmed_US', 'recovered'\"\"\"\n    \n    covid_datasets = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\n    \n    df = covid_datasets[f'time_series_covid_19_{kind}.csv']\n    # df = s['time_series_covid_19_deaths.csv']\n    if 'Province/State' in df.columns:\n        df.loc[df['Province/State'].isna(), 'Province/State'] = 'n/a'  # to avoid problems arising from NaNs\n\n    print_if_verbose(verbose, f\"Before data shape: {df.shape}\")\n\n    # drop some columns we don't need\n    p = re.compile('\\d+/\\d+/\\d+')\n\n    assert all(isinstance(x, str) for x in df.columns)\n    date_cols = [x for x in df.columns if p.match(x)]\n    if not kind.endswith('US'):\n        df = df.loc[:, ['Country/Region'] + date_cols]\n        # group countries and sum up the contributions of their states/regions/pargs\n        df['country'] = df.pop('Country/Region')\n        df = df.groupby('country').sum()\n    else:\n        df = df.loc[:, ['Province_State'] + date_cols]\n        df['state'] = df.pop('Province_State')\n        df = df.groupby('state').sum()\n\n    \n    print_if_verbose(verbose, f\"After data shape: {df.shape}\")\n    df = df.iloc[:, skip_first_days:]\n    \n    if not kind.endswith('US'):\n        # Joining with the country image urls and saving as an xls\n        country_image_url = country_flag_image_url_prep(data_sources['country_flag_image_url'])\n        t = df.copy()\n        t.columns = [str(x)[:10] for x in t.columns]\n        t = t.reset_index(drop=False)\n        t = country_image_url.merge(t, how='outer')\n        t = t.set_index('country')\n        df = t\n    else:    \n        pass\n\n    return df\n\n\ndef mk_and_save_country_data_for_data_kind(data_sources, kind='confirmed', skip_first_days=0, verbose=False):\n    t = country_data_for_data_kind(data_sources, kind, skip_first_days, verbose)\n    filepath = f'country_covid_{kind}.xlsx'\n    t.to_excel(filepath)\n    print_if_verbose(verbose, f\"Was saved here: {filepath}\")\n\n```\n\n\n```python\n# for kind in ['confirmed', 'deaths', 'recovered', 'confirmed_US', 'deaths_US']:\nfor kind in ['confirmed', 'deaths', 'recovered', 'confirmed_US', 'deaths_US']:\n    mk_and_save_country_data_for_data_kind(data_sources, kind=kind, skip_first_days=39, verbose=True)\n```\n\n    Before data shape: (262, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_confirmed.xlsx\n    Before data shape: (262, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_deaths.xlsx\n    Before data shape: (248, 79)\n    After data shape: (183, 75)\n    Was saved here: country_covid_recovered.xlsx\n    Before data shape: (3253, 86)\n    After data shape: (58, 75)\n    Was saved here: country_covid_confirmed_US.xlsx\n    Before data shape: (3253, 87)\n    After data shape: (58, 75)\n    Was saved here: country_covid_deaths_US.xlsx\n\n\n### Upload to Flourish, tune, and publish\n\nGo to https://public.flourish.studio/, get a free account, and play.\n\nGot to https://app.flourish.studio/templates\n\nChoose \"Bar chart race\". At the time of writing this, it was here: https://app.flourish.studio/visualisation/1706060/\n\n... and then play with the settings\n\n\n## Discussion of the methods\n\n\n```python\nfrom py2store import *\nfrom IPython.display import Image\n```\n\n### country flags images\n\nThe manual data prep looks something like this.\n\n\n```python\nimport pandas as pd\n\n# get the csv data from the url\ncountry_image_url_source = \\\n    'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv'\ncountry_image_url = pd.read_csv(country_image_url_source)\n\n# delete the region col (we don't need it)\ndel country_image_url['region']\n\n# rewriting a few (not all) of the country names to match those found in kaggle covid data\n# Note: The list is not complete! Add to it as needed\n# TODO: (Wishful) Using a general smart soft-matching algorithm to do this automatically.\n# TODO:    This could use edit-distance, synonyms, acronym generation, etc.\nold_and_new = [('USA', 'US'), \n               ('Iran, Islamic Rep.', 'Iran'), \n               ('UK', 'United Kingdom'), \n               ('Korea, Rep.', 'Korea, South')]\nfor old, new in old_and_new:\n    country_image_url['country'] = country_image_url['country'].replace(old, new)\n\nimage_url_of_country = country_image_url.set_index('country')['flag_image_url']\n\ncountry_image_url.head()\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n\n\n\n\n```python\nImage(url=image_url_of_country['Australia'])\n```\n\n\n\n\n<img src=\"https://www.countryflags.io/au/flat/64.png\"/>\n\n\n\n### Caching the flag images data\n\nDownloading our data sources every time we need them is not sustainable. What if they're big? What if you're offline or have slow internet (yes, dear future reader, even in the US, during coronavirus times!)?\n\nCaching. A \"cache aside\" read-cache. That's the word. py2store has tools for that (most of which are are caching.py). \n\nSo let's say we're going to have a local folder where we'll store various datas we download. The principle is as follows:\n\n\n```python\nfrom py2store.caching import mk_cached_store\n\nclass TheSource(dict): ...\nthe_cache = {}\nTheCacheSource = mk_cached_store(TheSource, the_cache)\n\nthe_source = TheSource({'green': 'eggs', 'and': 'ham'})\n\nthe_cached_source = TheCacheSource(the_source)\nprint(f\"the_cache: {the_cache}\")\nprint(f\"Getting green...\")\nthe_cached_source['green']\nprint(f\"the_cache: {the_cache}\")\nprint(\"... so the next time the_cached_source will get it's green from that the_cache\")\n```\n\n    the_cache: {}\n    Getting green...\n    the_cache: {'green': 'eggs'}\n    ... so the next time the_cached_source will get it's green from that the_cache\n\n\nBut now, you'll notice a slight problem ahead. What exactly does our source store (or rather reader) looks like? In it's raw form it would take urls as it's keys, and the response of a request as it's value. That store wouldn't have an `__iter__` for sure (unless you're Google). But more to the point here, the `mk_cached_store` tool uses the same key for the source and the cache, and we can't just use the url as is, to be a local file path. \n\nThere's many ways we could solve this. One way is to add a key map layer on the cache store, so externally, it speaks the url key language, but internally it will map that url to a valid local file path. We've been there, we got the T-shirt!\n\nBut what we're going to do is a bit different: We're going to do the key mapping in the source store itself. It seems to make more sense in our context: We have a data source of `name: data` pairs, and if we impose that the name should be a valid file name, we don't need to have a key map in the cache store.\n\nSo let's start by building this `MyDataStore` store. We'll start by defining the functions that get us the data we want. \n\n\n```python\ndef country_flag_image_url():\n    import pandas as pd\n    return pd.read_csv(\n        'https://raw.githubusercontent.com/i2mint/examples/master/data/country_flag_image_url.csv')\n\ndef kaggle_coronavirus_dataset():\n    import kaggle\n    from io import BytesIO\n    # didn't find the pure binary download function, so using temp dir to emulate\n    from tempfile import mkdtemp  \n    download_dir = mkdtemp()\n    filename = 'novel-corona-virus-2019-dataset.zip'\n    zip_file = os.path.join(download_dir, filename)\n    \n    dataset = 'sudalairajkumar/novel-corona-virus-2019-dataset'\n    kaggle.api.dataset_download_files(dataset, download_dir)\n    with open(zip_file, 'rb') as fp:\n        b = fp.read()\n    return BytesIO(b)\n\ndef city_population_in_time():\n    import pandas as pd\n    return pd.read_csv(\n        'https://gist.githubusercontent.com/johnburnmurdoch/'\n        '4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations'\n    )\n```\n\nNow we can make a store that simply uses these function names as the keys, and their returned value as the values.\n\n\n```python\nfrom py2store.base import KvReader\nfrom functools import lru_cache\n\nclass FuncReader(KvReader):\n    _getitem_cache_size = 999\n    def __init__(self, funcs):\n        # TODO: assert no free arguments (arguments are allowed but must all have defaults)\n        self.funcs = funcs\n        self._func_of_name = {func.__name__: func for func in funcs}\n\n    def __contains__(self, k):\n        return k in self._func_of_name\n    \n    def __iter__(self):\n        yield from self._func_of_name\n        \n    def __len__(self):\n        return len(self._func_of_name)\n\n    @lru_cache(maxsize=_getitem_cache_size)\n    def __getitem__(self, k):\n        return self._func_of_name[k]()  # call the func\n    \n    def __hash__(self):\n        return 1\n    \n```\n\n\n```python\ndata_sources = FuncReader([country_flag_image_url, kaggle_coronavirus_dataset, city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['city_population_in_time']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>group</th>\n      <th>year</th>\n      <th>value</th>\n      <th>subGroup</th>\n      <th>city_id</th>\n      <th>lastValue</th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1575</td>\n      <td>200.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1576</td>\n      <td>212.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1577</td>\n      <td>224.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>212.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1578</td>\n      <td>236.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>224.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1579</td>\n      <td>248.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>236.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6247</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1561</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6248</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1562</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6249</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1563</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6250</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1564</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6251</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1565</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n  </tbody>\n</table>\n<p>6252 rows \u00d7 9 columns</p>\n</div>\n\n\n\nBut we wanted this all to be cached locally, right? So a few more lines to do that!\n\n\n```python\nfrom py2store.caching import mk_cached_store\nfrom py2store import QuickPickleStore\n    \nmy_local_cache = os.path.expanduser('~/ddir/my_sources')\n\nCachedFuncReader = mk_cached_store(FuncReader, QuickPickleStore(my_local_cache))\n```\n\n\n```python\ndata_sources = CachedFuncReader([country_flag_image_url, kaggle_coronavirus_dataset, city_population_in_time])\nlist(data_sources)\n```\n\n\n\n\n    ['country_flag_image_url',\n     'kaggle_coronavirus_dataset',\n     'city_population_in_time']\n\n\n\n\n```python\ndata_sources['country_flag_image_url']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>region</th>\n      <th>flag_image_url</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Angola</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/ao/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Burundi</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bi/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Benin</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bj/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Burkina Faso</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bf/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Botswana</td>\n      <td>Africa</td>\n      <td>https://www.countryflags.io/bw/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>Solomon Islands</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/sb/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>Tonga</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/to/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>Tuvalu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/tv/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>Vanuatu</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/vu/flat/64.png</td>\n    </tr>\n    <tr>\n      <th>214</th>\n      <td>Samoa</td>\n      <td>Oceania</td>\n      <td>https://www.countryflags.io/ws/flat/64.png</td>\n    </tr>\n  </tbody>\n</table>\n<p>215 rows \u00d7 3 columns</p>\n</div>\n\n\n\n\n```python\ndata_sources['city_population_in_time']\n```\n\n\n\n\n<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>group</th>\n      <th>year</th>\n      <th>value</th>\n      <th>subGroup</th>\n      <th>city_id</th>\n      <th>lastValue</th>\n      <th>lat</th>\n      <th>lon</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1575</td>\n      <td>200.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1576</td>\n      <td>212.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>200.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1577</td>\n      <td>224.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>212.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1578</td>\n      <td>236.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>224.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Agra</td>\n      <td>India</td>\n      <td>1579</td>\n      <td>248.0</td>\n      <td>India</td>\n      <td>Agra - India</td>\n      <td>236.0</td>\n      <td>27.18333</td>\n      <td>78.01667</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6247</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1561</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6248</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1562</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6249</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1563</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6250</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1564</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n    <tr>\n      <th>6251</th>\n      <td>Vijayanagar</td>\n      <td>India</td>\n      <td>1565</td>\n      <td>480.0</td>\n      <td>India</td>\n      <td>Vijayanagar - India</td>\n      <td>480.0</td>\n      <td>15.33500</td>\n      <td>76.46200</td>\n    </tr>\n  </tbody>\n</table>\n<p>6252 rows \u00d7 9 columns</p>\n</div>\n\n\n\n\n```python\nz = ZippedCsvs(data_sources['kaggle_coronavirus_dataset'])\nlist(z)\n```\n",
    "long_description_content_type": "text/markdown",
    "description_file": "README.md",
    "root_url": "https://github.com/thorwhalen",
    "description": "A medley of things that got coded because there was an itch to do so",
    "author": "thorwhalen",
    "license": "Apache Software License",
    "description-file": "README.md",
    "install_requires": [],
    "keywords": [
      "documentation",
      "packaging",
      "publishing"
    ]
  }/usr/lib/python3.13/site-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'description_file'
    warnings.warn(msg)
  /usr/lib/python3.13/site-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'root_url'
    warnings.warn(msg)
  /usr/lib/python3.13/site-packages/setuptools/_distutils/dist.py:265: UserWarning: Unknown distribution option: 'description-file'
    warnings.warn(msg)
  /usr/lib/python3.13/site-packages/setuptools/dist.py:476: SetuptoolsDeprecationWarning: Invalid dash-separated options
  !!

          ********************************************************************************
          Usage of dash-separated 'description-file' will not be supported in future
          versions. Please use the underscore name 'description_file' instead.

          This deprecation is overdue, please update your project and remove deprecated
          calls to avoid build errors in the future.

          See https://setuptools.pypa.io/en/latest/userguide/declarative_config.html for details.
          ********************************************************************************

  !!
    opt = self.warn_dash_deprecation(opt, section)

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  creating build/lib/tapyoca/agglutination
  copying tapyoca/agglutination/__init__.py -> build/lib/tapyoca/agglutination
  copying tapyoca/agglutination/data_acquisition.py -> build/lib/tapyoca/agglutination
  copying tapyoca/agglutination/partitions.py -> build/lib/tapyoca/agglutination
  copying tapyoca/agglutination/py_names.py -> build/lib/tapyoca/agglutination
  creating build/lib/tapyoca/covid
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  copying tapyoca/covid/covid_bar_chart_race.py -> build/lib/tapyoca/covid
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  copying tapyoca/darpa/darpa.py -> build/lib/tapyoca/darpa
  creating build/lib/tapyoca/demonyms
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  copying tapyoca/demonyms/data_acquisition.py -> build/lib/tapyoca/demonyms
  creating build/lib/tapyoca/indexing_podcasts
  copying tapyoca/indexing_podcasts/__init__.py -> build/lib/tapyoca/indexing_podcasts
  copying tapyoca/indexing_podcasts/prep.py -> build/lib/tapyoca/indexing_podcasts
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  copying tapyoca/parquet_deformations/parquet_deformations.py -> build/lib/tapyoca/parquet_deformations
  copying tapyoca/parquet_deformations/py_fonts.py -> build/lib/tapyoca/parquet_deformations
  creating build/lib/tapyoca/phoneming
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  copying tapyoca/phoneming/explore.py -> build/lib/tapyoca/phoneming
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  writing tapyoca.egg-info/PKG-INFO
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  adding license file 'LICENSE'
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  creating build/bdist.linux-x86_64/wheel/tapyoca
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  creating build/bdist.linux-x86_64/wheel/tapyoca/agglutination
  copying build/lib/tapyoca/agglutination/__init__.py -> build/bdist.linux-x86_64/wheel/tapyoca/agglutination
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  creating build/bdist.linux-x86_64/wheel/tapyoca/covid
  copying build/lib/tapyoca/covid/__init__.py -> build/bdist.linux-x86_64/wheel/tapyoca/covid
  copying build/lib/tapyoca/covid/covid_bar_chart_race.py -> build/bdist.linux-x86_64/wheel/tapyoca/covid
  creating build/bdist.linux-x86_64/wheel/tapyoca/darpa
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  copying build/lib/tapyoca/darpa/darpa.py -> build/bdist.linux-x86_64/wheel/tapyoca/darpa
  creating build/bdist.linux-x86_64/wheel/tapyoca/demonyms
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  copying build/lib/tapyoca/demonyms/data_acquisition.py -> build/bdist.linux-x86_64/wheel/tapyoca/demonyms
  creating build/bdist.linux-x86_64/wheel/tapyoca/indexing_podcasts
  copying build/lib/tapyoca/indexing_podcasts/__init__.py -> build/bdist.linux-x86_64/wheel/tapyoca/indexing_podcasts
  copying build/lib/tapyoca/indexing_podcasts/prep.py -> build/bdist.linux-x86_64/wheel/tapyoca/indexing_podcasts
  creating build/bdist.linux-x86_64/wheel/tapyoca/parquet_deformations
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  copying build/lib/tapyoca/parquet_deformations/parquet_deformations.py -> build/bdist.linux-x86_64/wheel/tapyoca/parquet_deformations
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  Copying tapyoca.egg-info to build/bdist.linux-x86_64/wheel/tapyoca-0.0.4-py3.13.egg-info
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  creating '/builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/.pyproject-builddir/pip-wheel-31ti6nph/.tmp-vers3uzr/tapyoca-0.0.4-py3-none-any.whl' and adding 'build/bdist.linux-x86_64/wheel' to it
  adding 'tapyoca/__init__.py'
  adding 'tapyoca/agglutination/__init__.py'
  adding 'tapyoca/agglutination/data_acquisition.py'
  adding 'tapyoca/agglutination/partitions.py'
  adding 'tapyoca/agglutination/py_names.py'
  adding 'tapyoca/covid/__init__.py'
  adding 'tapyoca/covid/covid_bar_chart_race.py'
  adding 'tapyoca/darpa/__init__.py'
  adding 'tapyoca/darpa/darpa.py'
  adding 'tapyoca/demonyms/__init__.py'
  adding 'tapyoca/demonyms/data_acquisition.py'
  adding 'tapyoca/indexing_podcasts/__init__.py'
  adding 'tapyoca/indexing_podcasts/prep.py'
  adding 'tapyoca/parquet_deformations/__init__.py'
  adding 'tapyoca/parquet_deformations/parquet_deformations.py'
  adding 'tapyoca/parquet_deformations/py_fonts.py'
  adding 'tapyoca/phoneming/__init__.py'
  adding 'tapyoca/phoneming/explore.py'
  adding 'tapyoca-0.0.4.dist-info/LICENSE'
  adding 'tapyoca-0.0.4.dist-info/METADATA'
  adding 'tapyoca-0.0.4.dist-info/WHEEL'
  adding 'tapyoca-0.0.4.dist-info/top_level.txt'
  adding 'tapyoca-0.0.4.dist-info/RECORD'
  removing build/bdist.linux-x86_64/wheel
  Building wheel for tapyoca (pyproject.toml): finished with status 'done'
  Created wheel for tapyoca: filename=tapyoca-0.0.4-py3-none-any.whl size=75869 sha256=115c0119420f6bd389f68b9247b0bc2ed5b484d22dfd2198af5536d4fc3ddb72
  Stored in directory: /builddir/.cache/pip/wheels/22/4a/48/94112b8bf255c216f8a8adeee40369fb497047380288ec3da8
Successfully built tapyoca
+ RPM_EC=0
++ jobs -p
+ exit 0
Executing(%install): /bin/sh -e /var/tmp/rpm-tmp.ypRL9R
+ umask 022
+ cd /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ '[' /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT '!=' / ']'
+ rm -rf /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT
++ dirname /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT
+ mkdir -p /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ mkdir /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ export RUSTFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ cd tapyoca-0.0.4
++ ls /builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/pyproject-wheeldir/tapyoca-0.0.4-py3-none-any.whl
++ xargs basename --multiple
++ sed -E 's/([^-]+)-([^-]+)-.+\.whl/\1==\2/'
+ specifier=tapyoca==0.0.4
+ '[' -z tapyoca==0.0.4 ']'
+ TMPDIR=/builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/.pyproject-builddir
+ /usr/bin/python3 -m pip install --root /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT --prefix /usr --no-deps --disable-pip-version-check --progress-bar off --verbose --ignore-installed --no-warn-script-location --no-index --no-cache-dir --find-links /builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/pyproject-wheeldir tapyoca==0.0.4
Using pip 24.2 from /usr/lib/python3.13/site-packages/pip (python 3.13)
Looking in links: /builddir/build/BUILD/python-tapyoca-0.0.4-build/tapyoca-0.0.4/pyproject-wheeldir
Processing ./pyproject-wheeldir/tapyoca-0.0.4-py3-none-any.whl
Installing collected packages: tapyoca
Successfully installed tapyoca-0.0.4
+ '[' -d /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/bin ']'
+ rm -f /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-ghost-distinfo
+ site_dirs=()
+ '[' -d /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages ']'
+ site_dirs+=("/usr/lib/python3.13/site-packages")
+ '[' /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib64/python3.13/site-packages '!=' /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages ']'
+ '[' -d /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib64/python3.13/site-packages ']'
+ for site_dir in ${site_dirs[@]}
+ for distinfo in /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT$site_dir/*.dist-info
+ echo '%ghost /usr/lib/python3.13/site-packages/tapyoca-0.0.4.dist-info'
+ sed -i s/pip/rpm/ /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca-0.0.4.dist-info/INSTALLER
+ PYTHONPATH=/usr/lib/rpm/redhat
+ /usr/bin/python3 -B /usr/lib/rpm/redhat/pyproject_preprocess_record.py --buildroot /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT --record /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca-0.0.4.dist-info/RECORD --output /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-record
+ rm -fv /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca-0.0.4.dist-info/RECORD
removed '/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca-0.0.4.dist-info/RECORD'
+ rm -fv /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca-0.0.4.dist-info/REQUESTED
removed '/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca-0.0.4.dist-info/REQUESTED'
++ wc -l /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-ghost-distinfo
++ cut -f1 '-d '
+ lines=1
+ '[' 1 -ne 1 ']'
+ RPM_FILES_ESCAPE=4.19
+ /usr/bin/python3 /usr/lib/rpm/redhat/pyproject_save_files.py --output-files /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-files --output-modules /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-modules --buildroot /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT --sitelib /usr/lib/python3.13/site-packages --sitearch /usr/lib64/python3.13/site-packages --python-version 3.13 --pyproject-record /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-record --prefix /usr '*' +auto
+ /usr/lib/rpm/check-buildroot
+ /usr/lib/rpm/redhat/brp-ldconfig
+ /usr/lib/rpm/brp-compress
+ /usr/lib/rpm/brp-strip /usr/bin/strip
+ /usr/lib/rpm/brp-strip-comment-note /usr/bin/strip /usr/bin/objdump
+ /usr/lib/rpm/redhat/brp-strip-lto /usr/bin/strip
+ /usr/lib/rpm/brp-strip-static-archive /usr/bin/strip
+ /usr/lib/rpm/check-rpaths
+ /usr/lib/rpm/redhat/brp-mangle-shebangs
+ /usr/lib/rpm/brp-remove-la-files
+ env /usr/lib/rpm/redhat/brp-python-bytecompile '' 1 0 -j4
Bytecompiling .py files below /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13 using python3.13
/usr/lib/python3.13/site-packages/tapyoca/darpa/darpa.py:87: SyntaxWarning: invalid escape sequence '\d'
/usr/lib/python3.13/site-packages/tapyoca/darpa/darpa.py:87: SyntaxWarning: invalid escape sequence '\d'
/usr/lib/python3.13/site-packages/tapyoca/covid/covid_bar_chart_race.py:127: SyntaxWarning: invalid escape sequence '\d'
/usr/lib/python3.13/site-packages/tapyoca/demonyms/data_acquisition.py:17: SyntaxWarning: invalid escape sequence '\w'
/usr/lib/python3.13/site-packages/tapyoca/demonyms/data_acquisition.py:59: SyntaxWarning: invalid escape sequence '\ '
/usr/lib/python3.13/site-packages/tapyoca/demonyms/data_acquisition.py:88: SyntaxWarning: invalid escape sequence '\w'
/usr/lib/python3.13/site-packages/tapyoca/covid/covid_bar_chart_race.py:127: SyntaxWarning: invalid escape sequence '\d'
/usr/lib/python3.13/site-packages/tapyoca/demonyms/data_acquisition.py:184: SyntaxWarning: invalid escape sequence '\ '
/usr/lib/python3.13/site-packages/tapyoca/demonyms/data_acquisition.py:17: SyntaxWarning: invalid escape sequence '\w'
/usr/lib/python3.13/site-packages/tapyoca/demonyms/data_acquisition.py:59: SyntaxWarning: invalid escape sequence '\ '
/usr/lib/python3.13/site-packages/tapyoca/demonyms/data_acquisition.py:88: SyntaxWarning: invalid escape sequence '\w'
/usr/lib/python3.13/site-packages/tapyoca/demonyms/data_acquisition.py:184: SyntaxWarning: invalid escape sequence '\ '
+ /usr/lib/rpm/redhat/brp-python-hardlink
+ /usr/bin/add-determinism --brp -j4 /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/agglutination/__pycache__/__init__.cpython-313.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/agglutination/__pycache__/data_acquisition.cpython-313.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/agglutination/__pycache__/partitions.cpython-313.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/covid/__pycache__/__init__.cpython-313.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/agglutination/__pycache__/py_names.cpython-313.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/darpa/__pycache__/__init__.cpython-313.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/covid/__pycache__/covid_bar_chart_race.cpython-313.pyc: replacing with normalized version
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/darpa/__pycache__/darpa.cpython-313.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/demonyms/__pycache__/__init__.cpython-313.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/covid/__pycache__/covid_bar_chart_race.cpython-313.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/indexing_podcasts/__pycache__/__init__.cpython-313.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/indexing_podcasts/__pycache__/prep.cpython-313.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/parquet_deformations/__pycache__/__init__.cpython-313.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/demonyms/__pycache__/data_acquisition.cpython-313.opt-1.pyc: replacing with normalized version
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/demonyms/__pycache__/data_acquisition.cpython-313.pyc: replacing with normalized version
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/phoneming/__pycache__/__init__.cpython-313.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/parquet_deformations/__pycache__/py_fonts.cpython-313.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/phoneming/__pycache__/explore.cpython-313.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/__pycache__/__init__.cpython-313.pyc: rewriting with normalized contents
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/parquet_deformations/__pycache__/parquet_deformations.cpython-313.pyc: replacing with normalized version
/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages/tapyoca/parquet_deformations/__pycache__/parquet_deformations.cpython-313.opt-1.pyc: replacing with normalized version
Scanned 22 directories and 59 files,
               processed 21 inodes,
               21 modified (6 replaced + 15 rewritten),
               0 unsupported format, 0 errors
Executing(%check): /bin/sh -e /var/tmp/rpm-tmp.y18ufC
+ umask 022
+ cd /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ export CFLAGS
+ CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer '
+ export CXXFLAGS
+ FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ export FFLAGS
+ FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -m64 -march=x86-64 -mtune=generic -fasynchronous-unwind-tables -fstack-clash-protection -fcf-protection -mtls-dialect=gnu2 -fno-omit-frame-pointer -mno-omit-leaf-frame-pointer -I/usr/lib64/gfortran/modules '
+ export FCFLAGS
+ VALAFLAGS=-g
+ export VALAFLAGS
+ RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Cforce-frame-pointers=yes -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn'
+ export RUSTFLAGS
+ LDFLAGS='-Wl,-z,relro -Wl,--as-needed  -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1  -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes '
+ export LDFLAGS
+ LT_SYS_LIBRARY_PATH=/usr/lib64:
+ export LT_SYS_LIBRARY_PATH
+ CC=gcc
+ export CC
+ CXX=g++
+ export CXX
+ cd tapyoca-0.0.4
++ cat /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-modules
+ '[' -z 'tapyoca
tapyoca.agglutination
tapyoca.agglutination.data_acquisition
tapyoca.agglutination.partitions
tapyoca.agglutination.py_names
tapyoca.covid
tapyoca.covid.covid_bar_chart_race
tapyoca.darpa
tapyoca.darpa.darpa
tapyoca.demonyms
tapyoca.demonyms.data_acquisition
tapyoca.indexing_podcasts
tapyoca.indexing_podcasts.prep
tapyoca.parquet_deformations
tapyoca.parquet_deformations.parquet_deformations
tapyoca.parquet_deformations.py_fonts
tapyoca.phoneming
tapyoca.phoneming.explore' ']'
+ '[' '!' -f /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-modules ']'
+ PATH=/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/sbin
+ PYTHONPATH=/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib64/python3.13/site-packages:/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages
+ _PYTHONSITE=/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib64/python3.13/site-packages:/builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT/usr/lib/python3.13/site-packages
+ PYTHONDONTWRITEBYTECODE=1
+ /usr/bin/python3 -sP /usr/lib/rpm/redhat/import_all_modules.py -f /builddir/build/BUILD/python-tapyoca-0.0.4-build/python-tapyoca-0.0.4-1.fc41.x86_64-pyproject-modules -t
Check import: tapyoca
+ RPM_EC=0
++ jobs -p
+ exit 0
Processing files: python3-tapyoca-0.0.4-1.fc41.noarch
Provides: python-tapyoca = 0.0.4-1.fc41 python3-tapyoca = 0.0.4-1.fc41 python3.13-tapyoca = 0.0.4-1.fc41 python3.13dist(tapyoca) = 0.0.4 python3dist(tapyoca) = 0.0.4
Requires(rpmlib): rpmlib(CompressedFileNames) <= 3.0.4-1 rpmlib(FileDigests) <= 4.6.0-1 rpmlib(PartialHardlinkSets) <= 4.0.4-1 rpmlib(PayloadFilesHavePrefix) <= 4.0-1
Requires: python(abi) = 3.13
Checking for unpackaged file(s): /usr/lib/rpm/check-files /builddir/build/BUILD/python-tapyoca-0.0.4-build/BUILDROOT
Wrote: /builddir/build/SRPMS/python-tapyoca-0.0.4-1.fc41.src.rpm
Wrote: /builddir/build/RPMS/python3-tapyoca-0.0.4-1.fc41.noarch.rpm
Executing(rmbuild): /bin/sh -e /var/tmp/rpm-tmp.PO2xYy
+ umask 022
+ cd /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ test -d /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ /usr/bin/chmod -Rf a+rX,u+w,g-w,o-w /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ rm -rf /builddir/build/BUILD/python-tapyoca-0.0.4-build
+ RPM_EC=0
++ jobs -p
+ exit 0
Finish: rpmbuild python-tapyoca-0.0.4-1.fc41.src.rpm
Finish: build phase for python-tapyoca-0.0.4-1.fc41.src.rpm
INFO: chroot_scan: 1 files copied to /var/lib/copr-rpmbuild/results/chroot_scan
INFO: /var/lib/mock/fedora-41-x86_64-1740863228.713984/root/var/log/dnf5.log
INFO: chroot_scan: creating tarball /var/lib/copr-rpmbuild/results/chroot_scan.tar.gz
/bin/tar: Removing leading `/' from member names
INFO: Done(/var/lib/copr-rpmbuild/results/python-tapyoca-0.0.4-1.fc41.src.rpm) Config(child) 0 minutes 13 seconds
INFO: Results and/or logs in: /var/lib/copr-rpmbuild/results
INFO: Cleaning up build root ('cleanup_on_success=True')
Start: clean chroot
INFO: unmounting tmpfs.
Finish: clean chroot
Finish: run
Running RPMResults tool
Package info:
{
    "packages": [
        {
            "name": "python-tapyoca",
            "epoch": null,
            "version": "0.0.4",
            "release": "1.fc41",
            "arch": "src"
        },
        {
            "name": "python3-tapyoca",
            "epoch": null,
            "version": "0.0.4",
            "release": "1.fc41",
            "arch": "noarch"
        }
    ]
}
RPMResults finished