copr-backend src a06ad4515b8c18856c8a8dd12f4bb719133c15823aa304bff18c13b293779574 Backend for Copr COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latest builds. This package contains backend. https://github.com/fedora-copr/copr copr-backend src a4a543ef8dd3e08c62c684702ab69ebd9ff9190da2c58c72d2f8d639abfd8a50 Backend for Copr COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latest builds. This package contains backend. https://github.com/fedora-copr/copr copr-backend src 743f870814e3f0c07f4c0c9de1763ce074a4c68474bd23766180fbdbbedb09e9 Backend for Copr COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latest builds. This package contains backend. https://github.com/fedora-copr/copr copr-backend src 6a633def491decf006ce1465a78d7707f4fb95bbcaabcd82c1900f8b9755103f Backend for Copr COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latest builds. This package contains backend. https://github.com/fedora-copr/copr copr-builder aarch64 4ea7eabae5588d9c710ad1d0a8c5b49b62f9cc36faf22f740be38c45d556a8a3 copr-rpmbuild with all weak dependencies Provides command capable of running COPR build-tasks. Example: copr-rpmbuild 12345-epel-7-x86_64 will locally build build-id 12345 for chroot epel-7-x86_64. This package contains all optional modules for building SRPM. https://github.com/fedora-copr/copr copr-cli src 823500910d43194c170468b71ce82301a349b2c334dabda0abc9079372f2131a Command line interface for COPR COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latests builds. This package contains command line interface. https://github.com/fedora-copr/copr copr-cli noarch 8d07c604611ed79e52e240a44766a3c025ae65af6f408675a0e7fc2c31eb2405 Command line interface for COPR COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latests builds. This package contains command line interface. https://github.com/fedora-copr/copr copr-cli src 931a9c1f8aff55f012bb1deaac408fcabd2c07072f8ee7f977b494333c5d4fd6 Command line interface for COPR COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latests builds. This package contains command line interface. https://github.com/fedora-copr/copr copr-cli src f10361ae8b25bbb6c72e88951718ccd16da53f3f2a992b8950829e028b022d98 Command line interface for COPR COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latests builds. This package contains command line interface. https://github.com/fedora-copr/copr copr-cli src a78cf4787c67ff70b5e0fb2c1bfce21f3fb0804ff85842756f95d8c32c898bfc Command line interface for COPR COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latests builds. This package contains command line interface. https://github.com/fedora-copr/copr copr-dist-git src 7aa0fa0ed41626188413a95b5954a98d32a9fd09ea3fe97bf082685f04436c44 Copr services for Dist Git server COPR is lightweight build system. It allows you to create new project in WebUI and submit new builds and COPR will create yum repository from latest builds. This package contains Copr services for Dist Git server. https://github.com/fedora-copr/copr copr-dist-git src 38d824d02604df5bd53752fda8bc1cdeced8f45357024f77eccbe3b16935a545 Copr services for Dist Git server COPR is lightweight build system. It allows you to create new project in WebUI and submit new builds and COPR will create yum repository from latest builds. This package contains Copr services for Dist Git server. https://github.com/fedora-copr/copr copr-dist-git src 40841387e810cc6eeb9ac8bcdb5ce0c8d4f113961a969b0dfd905486bbc446da Copr services for Dist Git server COPR is lightweight build system. It allows you to create new project in WebUI and submit new builds and COPR will create yum repository from latest builds. This package contains Copr services for Dist Git server. https://github.com/fedora-copr/copr copr-distgit-client aarch64 6f142e5211c9245e0f783df4b76fecbc6f757fbeb768e232dfd80d9b8c24907c Utility to download sources from dist-git A simple, configurable python utility that is able to download sources from various dist-git instances, and generate source RPMs. The utility is able to automatically map the .git/config clone URL into the corresponding dist-git instance configuration. https://github.com/fedora-copr/copr copr-frontend src c672be970318598319fc319a9b44e3af650b91086778aacb742feb859e035c24 Frontend for Copr COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latests builds. This package contains frontend. https://github.com/fedora-copr/copr copr-frontend src acc2d25f3dcdc553aa0cd83ba969ee00fda8e27539dbc2dd082e3edfaa82d89a Frontend for Copr COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latests builds. This package contains frontend. https://github.com/fedora-copr/copr copr-frontend src 2f5672e18c6597afa89193ae3aeea67c64042d8678cf8564d5b50f662e54af45 Frontend for Copr COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latests builds. This package contains frontend. https://github.com/fedora-copr/copr copr-frontend src c29add2ec6658ac5527b6a7c9fe9d6845c318508907af31c1aa76da7258c8d6a Frontend for Copr COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latests builds. This package contains frontend. https://github.com/fedora-copr/copr copr-frontend src 34e837345b5aec79fd4674a96a6814af16fd70ccaeabd6c76b77596212f83892 Frontend for Copr COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latests builds. This package contains frontend. https://github.com/fedora-copr/copr copr-keygen noarch 799a4ae22620e23d010ea179b0b796ef1531c1fe0e82328a6b063e06416fc1eb Part of Copr build system. Aux service that generate keys for signd COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latest builds. This package contains aux service that generate keys for package signing. https://github.com/fedora-copr/copr copr-keygen src 9ad13e20a68bc7a2eeaa5aed7d2e3744dbd876ef2635f4335dd330e83e095981 Part of Copr build system. Aux service that generate keys for signd COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latest builds. This package contains aux service that generate keys for package signing. https://github.com/fedora-copr/copr copr-rpmbuild aarch64 09d1ef0b1e5b6e8d1404f41d0cc992eb613b03875ff8bf91f5e6affbf4c7e688 Run COPR build tasks Provides command capable of running COPR build-tasks. Example: copr-rpmbuild 12345-epel-7-x86_64 will locally build build-id 12345 for chroot epel-7-x86_64. https://github.com/fedora-copr/copr copr-rpmbuild src 5b3221703c0cf5fd2f7a8b60442e8fb452f197184108ceee77a632d5f3d86055 Run COPR build tasks Provides command capable of running COPR build-tasks. Example: copr-rpmbuild 12345-epel-7-x86_64 will locally build build-id 12345 for chroot epel-7-x86_64. https://github.com/fedora-copr/copr copr-rpmbuild src 4e7e43ed339cc15eed0c5bba3aef0ba59ed34bfa408fd6128fa51d1cb46ba905 Run COPR build tasks Provides command capable of running COPR build-tasks. Example: copr-rpmbuild 12345-epel-7-x86_64 will locally build build-id 12345 for chroot epel-7-x86_64. https://github.com/fedora-copr/copr copr-rpmbuild src 35cdf8dacd43700daed3072bae73b74c672a5a4f5ea7973fba657b89aaac1a7e Run COPR build tasks Provides command capable of running COPR build-tasks. Example: copr-rpmbuild 12345-epel-7-x86_64 will locally build build-id 12345 for chroot epel-7-x86_64. https://github.com/fedora-copr/copr dist-git noarch 9e22c8efbe308b6381a775cbff4dc5e38fab07f98eb9c0f9b88b3dc34f0cbf5a Package source version control system DistGit is a Git repository specifically designed to hold RPM package sources. https://github.com/release-engineering/dist-git dist-git src 62c956eea192a5e91c75da584d9be0f33e9f69497d89db818020a2d14b370895 Package source version control system DistGit is a Git repository specifically designed to hold RPM package sources. https://github.com/release-engineering/dist-git dist-git-selinux noarch 2258f6dc8b8a71d1c7fd6e87d3f0d87429328f6d8e62d3f959e7b145fbb4fd1b SELinux support for dist-git Dist Git is a remote Git repository specifically designed to hold RPM package sources. This package includes SELinux support. https://github.com/release-engineering/dist-git distribution-gpg-keys noarch 323cd83a2ee136c5c58905873028fbc17804901114aae9e2ee6ebdbe445fa22f GPG keys of various Linux distributions GPG keys used by various Linux distributions to sign packages. https://github.com/xsuchy/distribution-gpg-keys distribution-gpg-keys src dcce95f3768dde99a1252c1f68267a38b7bd0f16e6a5904a86eb7cf9df66a203 GPG keys of various Linux distributions GPG keys used by various Linux distributions to sign packages. https://github.com/xsuchy/distribution-gpg-keys distribution-gpg-keys-copr noarch 5e6fe2cd7c72ce49473690e45da805c71f27d12a4cca4b078e95869bed6b6de7 GPG keys for Copr projects GPG keys used by Copr projects. https://github.com/xsuchy/distribution-gpg-keys js-jquery-ui noarch 193dfad0a898b597ab5c4a5a599839dfa878ed12e1fab81cb5560139cd26d29c jQuery user interface A curated set of user interface interactions, effects, widgets, and themes built on top of the jQuery JavaScript Library. https://jqueryui.com/ js-jquery-ui src 4a2977746ee5fc52a33c16a71c6eab83139678c42d01e3e3f0765effe4f24337 jQuery user interface A curated set of user interface interactions, effects, widgets, and themes built on top of the jQuery JavaScript Library. https://jqueryui.com/ koji noarch b9ee6dd1bd0aff6f2a4af60d3038175ec25325359dfc5ba4cb9fe30f8d4b45e7 Build system tools Koji is a system for building and tracking RPMS. The base package contains shared libraries and the command-line interface. https://pagure.io/koji/ koji noarch 25b31297b3f7514599139c0e93e815b9e2f0d449723d8352c5a742a64832e580 Build system tools Koji is a system for building and tracking RPMS. The base package contains shared libraries and the command-line interface. https://pagure.io/koji/ koji src 4bf898dffdc431f59b59fd698521abf08db60a7a91e208be88e90ac1d1d62fcd Build system tools Koji is a system for building and tracking RPMS. The base package contains shared libraries and the command-line interface. https://pagure.io/koji/ koji src 53f653189360061b07b0e387a4da98b0d56dc04ce8c3c50ada8dcf935ab21353 Build system tools Koji is a system for building and tracking RPMS. The base package contains shared libraries and the command-line interface. https://pagure.io/koji/ koji-builder-plugin-rpmautospec noarch 156d519d0e8ddc45157a0ea348bed9846cc5c2a61c241b2e771e41c3e26bf676 Koji plugin for generating RPM releases and changelogs A Koji plugin for generating RPM releases and changelogs. https://pagure.io/fedora-infra/rpmautospec koji-builder-plugin-rpmautospec noarch 28e828b890c7377cdad56369e5dbee67f0ef0327a8795196b0acaba153c7a4c0 Koji plugin for generating RPM releases and changelogs A Koji plugin for generating RPM releases and changelogs. https://pagure.io/fedora-infra/rpmautospec mock noarch 603f96f382dfcc9e16cceac524466c214c59280ea9a5a4ed04ffa823195ddf39 Builds packages inside chroots Mock takes an SRPM and builds it in a chroot. https://github.com/rpm-software-management/mock/ mock src 66158ba4365a43b6e399c4a22b4d2c55a3c07f825eb31fa3715777651505323a Builds packages inside chroots Mock takes an SRPM and builds it in a chroot. https://github.com/rpm-software-management/mock/ mock-core-configs noarch a1e2f0762989b4bc3ee838c668698c877f036a3fa7d1825837745b8dc537ad48 Mock core config files basic chroots Config files which allow you to create chroots for: * Fedora * Epel * Mageia * Custom chroot * OpenSuse Tumbleweed and Leap * openEuler https://github.com/rpm-software-management/mock/ mock-core-configs src ded6da4e4a8cbff3f25657fe0537c3b2e87bd6003aadc09686f305a696205d62 Mock core config files basic chroots Config files which allow you to create chroots for: * Fedora * Epel * Mageia * Custom chroot * OpenSuse Tumbleweed and Leap * openEuler https://github.com/rpm-software-management/mock/ mock-core-configs noarch 02c68e391405f9b71267e354dfa213eb5068e67eec5181d5cd1aa332f763cb5e Mock core config files basic chroots Config files which allow you to create chroots for: * Fedora * Epel * Mageia * Custom chroot * OpenSuse Tumbleweed and Leap * openEuler https://github.com/rpm-software-management/mock/ mock-core-configs src bafc003ac51b1b19bf249d02b867bcfd0e2a199fed3c2cb28195cfb7e4aaac82 Mock core config files basic chroots Config files which allow you to create chroots for: * Fedora * Epel * Mageia * Custom chroot * OpenSuse Tumbleweed and Leap * openEuler https://github.com/rpm-software-management/mock/ mock-filesystem noarch 715bc09a35f423abc91851f2470ac7d7822df4eef6fb03da4945abe0898dd713 Mock filesystem layout Filesystem layout and group for Mock. https://github.com/rpm-software-management/mock/ mock-lvm noarch 4b48742a80a4e0b9524f743e455c52d7b4e02bccd1d1ae47ddce950185e6e104 LVM plugin for mock Mock plugin that enables using LVM as a backend and support creating snapshots of the buildroot. https://github.com/rpm-software-management/mock/ mock-scm noarch abc050f3bcbf58b7f1cfaf179edf2012a0e8f1eee0eddd4b79aaf6cec6507af1 Mock SCM integration module Mock SCM integration module. https://github.com/rpm-software-management/mock/ modulemd-tools noarch 7f38fd921030ffd83c59caa019aa7570229e70f9c379c80743f88d0f1b63e3e0 Collection of tools for parsing and generating modulemd YAML files Tools provided by this package: repo2module - Takes a YUM repository on its input and creates modules.yaml containing YAML module definitions generated for each package. dir2module - Generates a module YAML definition based on essential module information provided via command-line parameters. The packages provided by the module are found in a specified directory or a text file containing their list. createrepo_mod - A small wrapper around createrepo_c and modifyrepo_c to provide an easy tool for generating module repositories. modulemd-add-platform - Add a new context configuration for a new platform into a modulemd-packager file. modulemd-merge - Merge several modules.yaml files into one. This is useful for example if you have several yum repositories and want to merge them into one. modulemd-generate-macros - Generate module-build-macros SRPM package, which is a central piece for building modules. It should be present in the buildroot before any other module packages are submitted to be built. bld2repo - Simple tool for dowloading build required RPMs of a modular build from koji. https://github.com/rpm-software-management/modulemd-tools modulemd-tools src 10a3d346bae116fc0c9798ab9a5417aee80fa793bbea2bc2dbe51a59887bb29d Collection of tools for parsing and generating modulemd YAML files Tools provided by this package: repo2module - Takes a YUM repository on its input and creates modules.yaml containing YAML module definitions generated for each package. dir2module - Generates a module YAML definition based on essential module information provided via command-line parameters. The packages provided by the module are found in a specified directory or a text file containing their list. createrepo_mod - A small wrapper around createrepo_c and modifyrepo_c to provide an easy tool for generating module repositories. modulemd-add-platform - Add a new context configuration for a new platform into a modulemd-packager file. modulemd-merge - Merge several modules.yaml files into one. This is useful for example if you have several yum repositories and want to merge them into one. modulemd-generate-macros - Generate module-build-macros SRPM package, which is a central piece for building modules. It should be present in the buildroot before any other module packages are submitted to be built. bld2repo - Simple tool for dowloading build required RPMs of a modular build from koji. https://github.com/rpm-software-management/modulemd-tools mysql-connector-python src 353a69a816b0f9abef46988e89b2af465c9c5749d65dddb47f34c63e4a6d043c MySQL driver written in Python MySQL driver written in Python which does not depend on MySQL C client libraries and implements the DB API v2.0 specification (PEP-249). http://dev.mysql.com/doc/connector-python/en/index.html mysql-connector-python-debuginfo aarch64 40fed69b21607371ee420ea213359c0969d2b113d9c96a6ce228c41f0571c034 Debug information for package mysql-connector-python This package provides debug information for package mysql-connector-python. Debug information is useful when developing applications that use this package or when debugging this package. http://dev.mysql.com/doc/connector-python/en/index.html mysql-connector-python-debugsource aarch64 7b3c9ff3540530d749359d93d685a7912eef124d97dfab6b20f9051d1d8b097a Debug sources for package mysql-connector-python This package provides debug sources for package mysql-connector-python. Debug sources are useful when developing applications that use this package or when debugging this package. http://dev.mysql.com/doc/connector-python/en/index.html mysql-connector-python-help aarch64 a2b32c805d787683c9c7313d115e55a97019fe5488141488e0af30943fbff299 Development documents and examples for mysql-connector-python MySQL driver written in Python which does not depend on MySQL C client libraries and implements the DB API v2.0 specification (PEP-249). http://dev.mysql.com/doc/connector-python/en/index.html mysql-connector-python3 aarch64 18fa4b47cbf8489b9c904904a03e5fcf1bd8073cfb32fa28c3a78c44b18acd22 MySQL driver written in Python MySQL driver written in Python which does not depend on MySQL C client libraries and implements the DB API v2.0 specification (PEP-249). http://dev.mysql.com/doc/connector-python/en/index.html obs-signd aarch64 3ad359ec0bb69af6e88ed23798a8773fe5b793c42e51254773d609f1ac19d5d5 The OBS sign daemon The OpenSUSE Build Service sign client and daemon. This daemon can be used to sign anything via gpg by communicating with a remote server to avoid the need to host the private key on the same server. https://github.com/openSUSE/obs-sign obs-signd src de235cbba5a0d1ffe2f9fccf50759fe5fe14a3e5aab1ff214c4bb2c616e1fef2 The OBS sign daemon The OpenSUSE Build Service sign client and daemon. This daemon can be used to sign anything via gpg by communicating with a remote server to avoid the need to host the private key on the same server. https://github.com/openSUSE/obs-sign obs-signd-debuginfo aarch64 42a616164c6d13d11b117459650be62b861796bccd1941d3fd49b503e30a21f4 Debug information for package obs-signd This package provides debug information for package obs-signd. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/openSUSE/obs-sign obs-signd-debugsource aarch64 e6fbb55effa2bd0e285662374c60da19cfb593bbe36bf477ccacde76346b2b64 Debug sources for package obs-signd This package provides debug sources for package obs-signd. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/openSUSE/obs-sign preproc noarch 12499200c03aabab43e926751d00c292285af408ff5c1124720bc6dd244704b3 Simple text preprocessor Simple text preprocessor implementing a very basic templating language. You can use bash code enclosed in triple braces in a text file and then pipe content of that file to preproc. preproc will replace each of the tags with stdout of the executed code and print the final renderred result to its own stdout. https://pagure.io/rpkg-util.git preproc noarch ab2fa044d2b74c71bb95fcd0004e229aa340be643df8f3ffa8f30fd4b416f24c Simple text preprocessor Simple text preprocessor implementing a very basic templating language. You can use bash code enclosed in triple braces in a text file and then pipe content of that file to preproc. preproc will replace each of the tags with stdout of the executed code and print the final renderred result to its own stdout. https://pagure.io/rpkg-util.git preproc src 16a59445c1e42e8fd30a8612d9ce957c4434a3ea1c44104a2f866ee9119d1635 Simple text preprocessor Simple text preprocessor implementing a very basic templating language. You can use bash code enclosed in triple braces in a text file and then pipe content of that file to preproc. preproc will replace each of the tags with stdout of the executed code and print the final renderred result to its own stdout. https://pagure.io/rpkg-util.git preproc src c258d25488164c94fb194cee3e4a6bef12202c7663d94309dafad99a618acbd1 Simple text preprocessor Simple text preprocessor implementing a very basic templating language. You can use bash code enclosed in triple braces in a text file and then pipe content of that file to preproc. preproc will replace each of the tags with stdout of the executed code and print the final renderred result to its own stdout. https://pagure.io/rpkg-util.git procenv aarch64 94b746439f61743e240b0d4668b558a3b874193d50d18c8df1f866e1ad92342c Utility to show process environment This package contains a command-line tool that displays as much detail about itself and its environment as possible. It can be used as a test tool, to understand the type of environment a process runs in, and for comparing system environments. https://github.com/jamesodhunt/procenv procenv aarch64 7800d73fe9e35a818a50e3fb50ca52c1eb981e0f87e5f11c0134057f28b4ef71 Utility to show process environment This package contains a command-line tool that displays as much detail about itself and its environment as possible. It can be used as a test tool, to understand the type of environment a process runs in, and for comparing system environments. https://github.com/jamesodhunt/procenv procenv aarch64 bc70a9cffe4e2c821e1f95feee10d27d5a5d42218cb39b4d8233d52542685041 Utility to show process environment This package contains a command-line tool that displays as much detail about itself and its environment as possible. It can be used as a test tool, to understand the type of environment a process runs in, and for comparing system environments. https://github.com/jamesodhunt/procenv procenv src 1f5046f1ab116ca70c706d2f634ce9a0336a36044f8608d574359a9d0a2a66a7 Utility to show process environment This package contains a command-line tool that displays as much detail about itself and its environment as possible. It can be used as a test tool, to understand the type of environment a process runs in, and for comparing system environments. https://github.com/jamesodhunt/procenv procenv src 62e641b8f8cb2baf8f679f00a23becae0c41701a81ee72c9893d1d41f9781db1 Utility to show process environment This package contains a command-line tool that displays as much detail about itself and its environment as possible. It can be used as a test tool, to understand the type of environment a process runs in, and for comparing system environments. https://github.com/jamesodhunt/procenv procenv src a1745bddeedf6a79f0060b3b03085091c2404037cfc7c9178fd05f80c24c98c8 Utility to show process environment This package contains a command-line tool that displays as much detail about itself and its environment as possible. It can be used as a test tool, to understand the type of environment a process runs in, and for comparing system environments. https://github.com/jamesodhunt/procenv procenv-debuginfo aarch64 92f8d07a0738f1254dd2ba1cb9cddc33a640d3f027e24b5555f7d438ab5dd6db Debug information for package procenv This package provides debug information for package procenv. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/jamesodhunt/procenv procenv-debuginfo aarch64 f0170ecd63bca28aae8503330b7b9b6a669311c076162219d5e74d74d9d9c078 Debug information for package procenv This package provides debug information for package procenv. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/jamesodhunt/procenv procenv-debuginfo aarch64 b83181ab4bf32271fd2bb195747d0f4a6e201bc90e2ab0a6c2a52cb5fb92e978 Debug information for package procenv This package provides debug information for package procenv. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/jamesodhunt/procenv procenv-debugsource aarch64 53af6432a2d2c57cfc735603809e8733a7c9029a4debe8da716a8e498205371d Debug sources for package procenv This package provides debug sources for package procenv. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/jamesodhunt/procenv procenv-debugsource aarch64 d46a8ee529a976288e55c0d106db1ad2ffae0f79dc52c6e47dd3cf4fdcd9833e Debug sources for package procenv This package provides debug sources for package procenv. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/jamesodhunt/procenv procenv-debugsource aarch64 223d632023bf8db5045ced99f1f7126c3bb0c484099c16747c79593496f07655 Debug sources for package procenv This package provides debug sources for package procenv. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/jamesodhunt/procenv prunerepo noarch e2ee74b7353e63ae135d035c9b221a94511c8ff5a339de00d2dbb6f636b6da70 Remove old packages from rpm-md repository RPM packages that have newer version available in that same repository are deleted from filesystem and the rpm-md metadata are recreated afterwards. If there is a source rpm for a deleted rpm (and they both share the same directory path), then the source rpm will be deleted as well. Support for specific repository structure (e.g. COPR) is also available making it possible to additionally remove build logs and whole build directories associated with a package. After deletion of obsoleted packages, the command "createrepo_c --database --update" is called to recreate the repository metadata. https://pagure.io/prunerepo prunerepo src daaa08e0979f35955d3489a97b58520c3b994cde053641aecccf888c28482aea Remove old packages from rpm-md repository RPM packages that have newer version available in that same repository are deleted from filesystem and the rpm-md metadata are recreated afterwards. If there is a source rpm for a deleted rpm (and they both share the same directory path), then the source rpm will be deleted as well. Support for specific repository structure (e.g. COPR) is also available making it possible to additionally remove build logs and whole build directories associated with a package. After deletion of obsoleted packages, the command "createrepo_c --database --update" is called to recreate the repository metadata. https://pagure.io/prunerepo pyproject-rpm-macros noarch 79034ee3da80979cba49a34b16082f6541c92a80a5972e1bac6b97899db5c3ef RPM macros for PEP 517 Python packages These macros allow projects that follow the Python packaging specifications to be packaged as RPMs. They work for: * traditional Setuptools-based projects that use the setup.py file, * newer Setuptools-based projects that have a setup.cfg file, * general Python projects that use the PEP 517 pyproject.toml file (which allows using any build system, such as setuptools, flit or poetry). These macros replace %py3_build and %py3_install, which only work with setup.py. https://src.fedoraproject.org/rpms/pyproject-rpm-macros pyproject-rpm-macros src e2904c6505abe429eac06ad44898d63688d1fb376e5e8b7d30ec331f5a920800 RPM macros for PEP 517 Python packages These macros allow projects that follow the Python packaging specifications to be packaged as RPMs. They work for: * traditional Setuptools-based projects that use the setup.py file, * newer Setuptools-based projects that have a setup.cfg file, * general Python projects that use the PEP 517 pyproject.toml file (which allows using any build system, such as setuptools, flit or poetry). These macros replace %py3_build and %py3_install, which only work with setup.py. https://src.fedoraproject.org/rpms/pyproject-rpm-macros python-Authlib src a17f1e7778c17b346a54d8b7a6bce0782ea146cc3ea267a745b60c285cd2fb74 The ultimate Python library in building OAuth and OpenID Connect servers and clients. The ultimate Python library in building OAuth and OpenID Connect servers. JWS, JWK, JWA, JWT are included. https://authlib.org/ python-Authlib-help noarch 4914ee9da1809095fc0f68a5fa5920f37549d9a2baed0c586bc7b2d2efb6d864 Development documents and examples for Authlib The ultimate Python library in building OAuth and OpenID Connect servers. JWS, JWK, JWA, JWT are included. https://authlib.org/ python-CCColUtils src f8dcbfa853f19019776ba8600095fa2dbfbcedd1b36265fd542c37e7233e61e0 Kerberos5 Credential Cache Collection Utilities Kerberos5 Credential Cache Collection Utilities. https://pagure.io/cccolutils python-CCColUtils-debuginfo aarch64 69910a1dd0adea8371b45bea7aaed87e3bad087953a6b22cfe3008327fe00992 Debug information for package python-CCColUtils This package provides debug information for package python-CCColUtils. Debug information is useful when developing applications that use this package or when debugging this package. https://pagure.io/cccolutils python-CCColUtils-debugsource aarch64 0ed8b51ac8e92282690a35499d824a1e26a978e404fd86c4839baef1cf66b4ce Debug sources for package python-CCColUtils This package provides debug sources for package python-CCColUtils. Debug sources are useful when developing applications that use this package or when debugging this package. https://pagure.io/cccolutils python-Flask-Caching src 7f333a9092d157169cf9e5b77d880440c858332323669307f75c96232fdd5051 Adds caching support to Flask applications. A fork of the `Flask-cache`_ extension which adds easy cache support to Flask. https://github.com/pallets-eco/flask-caching python-Flask-Caching-help noarch 166c4f135249fc5c3966f61bc3516d48be1493782611a668ea750be6a5797f7f Development documents and examples for Flask-Caching A fork of the `Flask-cache`_ extension which adds easy cache support to Flask. https://github.com/pallets-eco/flask-caching python-Flask-OpenID src 2f3be4cd98a7123969c002410a11a123dd2f86f8822811021ad96698fef00d16 OpenID support for Flask Flask-OpenID adds openid support to flask applications http://github.com/mitsuhiko/flask-openid/ python-Flask-OpenID-help noarch 8d1c8ad5de51aa1c04c10c8d4fe9a42dc6f8589ca0ffb3b389760e0c72c57d38 Development documents and examples for Flask-OpenID Flask-OpenID adds openid support to flask applications http://github.com/mitsuhiko/flask-openid/ python-Flask-WTF src 2f231e42bda399daed6a3d622f146816566b202e083198b395f8097b3b3a10c2 Form rendering, validation, and CSRF protection for Flask with WTForms. Simple integration of Flask and WTForms, including CSRF, file upload, and reCAPTCHA. https://github.com/wtforms/flask-wtf/ python-Flask-WTF-help noarch d60a64baa3afc9ec723cd25e5d984787a047743f2d0bf116c3999b578cf22fb1 Development documents and examples for Flask-WTF Simple integration of Flask and WTForms, including CSRF, file upload, and reCAPTCHA. https://github.com/wtforms/flask-wtf/ python-WTForms src b4a4249b176754325e2dee6d84cf7bbc2a762e48927f396e27f019bac6a5de49 Form validation and rendering for Python web development. WTForms is a flexible forms validation and rendering library for Python web development. It can work with whatever web framework and template engine you choose. It supports data validation, CSRF protection, internationalization (I18N), and more. There are various community libraries that provide closer integration with popular frameworks. https://wtforms.readthedocs.io/ python-WTForms src f84cea390f3e43f58795ff34252afa32028a365f95f5a7679974ffc35879c31d Form validation and rendering for Python web development. WTForms is a flexible forms validation and rendering library for Python web development. It can work with whatever web framework and template engine you choose. It supports data validation, CSRF protection, internationalization (I18N), and more. There are various community libraries that provide closer integration with popular frameworks. https://wtforms.readthedocs.io/ python-XStatic-Bootstrap-SCSS src 77692816f4a13499b80ca1b04ada6de524fc77ccfc9f4a76b29ea56771a4b62d Bootstrap-SCSS 3.4.1 (XStatic packaging standard) Bootstrap style library packaged for setuptools (easy_install) / pip. This package is intended to be used by **any** project that needs these files. It intentionally does **not** provide any extra code except some metadata **nor** has any extra requirements. You MAY use some minimal support code from the XStatic base package, if you like. You can find more info about the xstatic packaging way in the package `XStatic`. https://github.com/twbs/bootstrap-sass python-XStatic-Bootstrap-SCSS-help noarch d944d99f3c7b840680ba650395619c56e79cf38edc5cc16e6d33746939d5775e Development documents and examples for XStatic-Bootstrap-SCSS Bootstrap style library packaged for setuptools (easy_install) / pip. This package is intended to be used by **any** project that needs these files. It intentionally does **not** provide any extra code except some metadata **nor** has any extra requirements. You MAY use some minimal support code from the XStatic base package, if you like. You can find more info about the xstatic packaging way in the package `XStatic`. https://github.com/twbs/bootstrap-sass python-XStatic-DataTables src cd4979cf3b7e160006c6eae777eb9575245914d7f0bd75103e9a980216fcb498 DataTables 1.10.15 (XStatic packaging standard) The DataTables plugin for jQuery packaged for setuptools (easy_install) / pip. This package is intended to be used by **any** project that needs these files. It intentionally does **not** provide any extra code except some metadata **nor** has any extra requirements. You MAY use some minimal support code from the XStatic base package, if you like. You can find more info about the xstatic packaging way in the package `XStatic`. http://www.datatables.net python-XStatic-DataTables-help noarch 3778d01b54a5e1f28efe8f5347d2396fcdfbc56e3134a8582eae0d0132324d72 Development documents and examples for XStatic-DataTables The DataTables plugin for jQuery packaged for setuptools (easy_install) / pip. This package is intended to be used by **any** project that needs these files. It intentionally does **not** provide any extra code except some metadata **nor** has any extra requirements. You MAY use some minimal support code from the XStatic base package, if you like. You can find more info about the xstatic packaging way in the package `XStatic`. http://www.datatables.net python-XStatic-Patternfly src 13d949a924a3fde44c081e7d9c3cac8991323ebb31a3b4ee12da61f1fd17dd14 Patternfly 3.21.0 (XStatic packaging standard) Patternfly style library packaged for setuptools (easy_install) / pip. This package is intended to be used by **any** project that needs these files. It intentionally does **not** provide any extra code except some metadata **nor** has any extra requirements. You MAY use some minimal support code from the XStatic base package, if you like. You can find more info about the xstatic packaging way in the package `XStatic`. https://www.patternfly.org/ python-XStatic-Patternfly-help noarch f58a8ffc46ad8be6f32bae1291e3ec335c64e3b9c1b579bc9abb2548d9bd0d4d Development documents and examples for XStatic-Patternfly Patternfly style library packaged for setuptools (easy_install) / pip. This package is intended to be used by **any** project that needs these files. It intentionally does **not** provide any extra code except some metadata **nor** has any extra requirements. You MAY use some minimal support code from the XStatic base package, if you like. You can find more info about the xstatic packaging way in the package `XStatic`. https://www.patternfly.org/ python-argparse-manpage src b23c1291b5d66626f16455f4d057c00d77ec65572e9ab6bf1731a14021de6171 Build manual page from python's ArgumentParser object. Automatically build manpage from argparse https://github.com/praiskup/argparse-manpage python-argparse-manpage-help noarch 81a4f3cee7977a373c4629f3acc02fff849bfb4a0d4b9a9f4c034a29d91ea1fc Development documents and examples for argparse-manpage Automatically build manpage from argparse https://github.com/praiskup/argparse-manpage python-asttokens src 63ef4e921877ecf0867e4fe426a7a1c30520bfe07c462c0ddf4f38ccaf6ddfd3 Module to annotate Python abstract syntax trees with source code positions The asttokens module annotates Python abstract syntax trees (ASTs) with the positions of tokens and text in the source code that generated them. This makes it possible for tools that work with logical AST nodes to find the particular text that resulted in those nodes, for example for automated refactoring or highlighting. https://github.com/gristlabs/asttokens python-backoff src 370f721b41f43ff83c270229bd8e9582910e5cc73b93690043a893ef3a568203 Function decoration for backoff and retry This module provides function decorators which can be used to wrap a\ function such that it will be retried until some condition is met. It\ is meant to be of use when accessing unreliable resources with the\ potential for intermittent failures i.e. network resources and external\ APIs. Somewhat more generally, it may also be of use for dynamically\ polling resources for externally generated content. https://github.com/litl/backoff python-backoff-help noarch 2051fe5a91c21a0a264bcdcc72731d18946cbec9851e2a1fbab0f4c118a048e0 Development documents and examples for backoff This module provides function decorators which can be used to wrap a\ function such that it will be retried until some condition is met. It\ is meant to be of use when accessing unreliable resources with the\ potential for intermittent failures i.e. network resources and external\ APIs. Somewhat more generally, it may also be of use for dynamically\ polling resources for externally generated content. https://github.com/litl/backoff python-blessed src a2b19c4aedea9b50c39798a5ada1f27b30cd368a1e0efa1105350b301a6fd54e Easy, practical library for making terminal apps, by providing an elegant, well-documented interface to Colors, Keyboard input, and screen Positioning capabilities. Blessed is an easy, practical library for making python terminal apps https://github.com/jquast/blessed python-blessed-help noarch dde023853f22d5355e15a68097a9096185e6c758cbf8cece8861c6ed50b4f6ad Development documents and examples for blessed Blessed is an easy, practical library for making python terminal apps https://github.com/jquast/blessed python-cachelib src 50bd6ae366378f9528a026b9b4a6afda2d421a62daf8f02c62ec32a18159839e A collection of cache libraries in the same API interface. A collection of cache libraries in the same API interface. Extracted from werkzeug. https://github.com/pallets-eco/cachelib python-cachelib-help noarch 86ec77e5e7c64c2c4a3bc37a583de424e9a5edc21cd7ee632068eaed2f5b793e Development documents and examples for cachelib Development documents and examples for cachelib https://github.com/pallets-eco/cachelib python-copr src f4176b14caafc7c9802836ea5fdd539323651a1076c55cae42403cf8bd2edecd Python interface for Copr COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latest builds. This package contains python interface to access Copr service. Mostly useful for developers only. https://github.com/fedora-copr/copr python-copr src e96d60735cc883341f9826cc95e984e3f84b60ba25381cb57b48a0b787f3dba4 Python interface for Copr COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latest builds. This package contains python interface to access Copr service. Mostly useful for developers only. https://github.com/fedora-copr/copr python-copr src daf5c820d861bafe3bdc90ec3ec685d1fac3dd0dc6bcd716d2d7ac09c33b47ae Python interface for Copr COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latest builds. This package contains python interface to access Copr service. Mostly useful for developers only. https://github.com/fedora-copr/copr python-copr-common src f17ae8677df8403e2fef51fe3ecb734cffd91cdbf1552a513de2b7bf6481b9d9 Python code used by Copr COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latest builds. This package contains python code used by other Copr packages. Mostly useful for developers only. https://github.com/fedora-copr/copr python-crudini src fac6a009bcb9342ac3358b6a780de02f249f2962db3e25ef8bf10c336d31f573 A utility for manipulating ini files crudini A utility for manipulating ini files http://github.com/pixelb/crudini python-crudini-help noarch f42921ddd30c7120d69f109f70ac3e1dbe477e6abc169e2156a3e6250adace70 A utility for manipulating ini files Usage: crudini --set [OPTION]... config_file section [param] [value] or: crudini --get [OPTION]... config_file [section] [param] or: crudini --del [OPTION]... config_file section [param] [list value] or: crudini --merge [OPTION]... config_file [section] http://github.com/pixelb/crudini python-debtcollector src ee216c89dc8e92e98c6a89943be86d36420b2c8b34415048f7408d076bfa02a1 A collection of Python deprecation patterns and strategies that help you collect your technical debt in a non-destructive manner. A collection of Python deprecation patterns and strategies that help you collect your technical debt in a non-destructive manner. https://docs.openstack.org/debtcollector/latest python-debtcollector-help noarch e171710d8a9cb4d8f5b1c02f8450b439a35b16360d4add0ed34a8e1cb06e7240 A collection of Python deprecation patterns and strategies that help you collect your technical debt in a non-destructive manner. A collection of Python deprecation patterns and strategies that help you collect your technical debt in a non-destructive manner. https://docs.openstack.org/debtcollector/latest python-email-validator src 75da40f39eade04571aa16b9ef049dac96ba4cd16711b24616f5ad9b3a872911 A robust email address syntax and deliverability validation library. A robust email address syntax and deliverability validation library for Python by [Joshua Tauberer](https://joshdata.me). This library validates that a string is of the form `name@example.com`. This is the sort of validation you would want for an email-based login form on a website. Key features: * Checks that an email address has the correct syntax --- good for login forms or other uses related to identifying users. * Gives friendly error messages when validation fails (appropriate to show to end users). * (optionally) Checks deliverability: Does the domain name resolve? And you can override the default DNS resolver. * Supports internationalized domain names and (optionally) internationalized local parts, but blocks unsafe characters. * Normalizes email addresses (super important for internationalized addresses! see below). The library is NOT for validation of the To: line in an email message (e.g. `My Name <my@address.com>`), which [flanker](https://github.com/mailgun/flanker) is more appropriate for. And this library does NOT permit obsolete forms of email addresses, so if you need strict validation against the email specs exactly, use [pyIsEmail](https://github.com/michaelherold/pyIsEmail). This library is tested with Python 3.6+ but should work in earlier versions: [![Build Status](https://app.travis-ci.com/JoshData/python-email-validator.svg?branch=main)](https://app.travis-ci.com/JoshData/python-email-validator) https://github.com/JoshData/python-email-validator python-email-validator-help noarch 0241b66dd9c98b9adb0c0fdb2b9340bb2ad870596b3de840738260396d77b50d Development documents and examples for email-validator A robust email address syntax and deliverability validation library for Python by [Joshua Tauberer](https://joshdata.me). This library validates that a string is of the form `name@example.com`. This is the sort of validation you would want for an email-based login form on a website. Key features: * Checks that an email address has the correct syntax --- good for login forms or other uses related to identifying users. * Gives friendly error messages when validation fails (appropriate to show to end users). * (optionally) Checks deliverability: Does the domain name resolve? And you can override the default DNS resolver. * Supports internationalized domain names and (optionally) internationalized local parts, but blocks unsafe characters. * Normalizes email addresses (super important for internationalized addresses! see below). The library is NOT for validation of the To: line in an email message (e.g. `My Name <my@address.com>`), which [flanker](https://github.com/mailgun/flanker) is more appropriate for. And this library does NOT permit obsolete forms of email addresses, so if you need strict validation against the email specs exactly, use [pyIsEmail](https://github.com/michaelherold/pyIsEmail). This library is tested with Python 3.6+ but should work in earlier versions: [![Build Status](https://app.travis-ci.com/JoshData/python-email-validator.svg?branch=main)](https://app.travis-ci.com/JoshData/python-email-validator) https://github.com/JoshData/python-email-validator python-executing src 250fa7de0608afef44c55acbffbc8caae65953c58ce36615e6a8f5989bbafd27 Get the currently executing AST node of a frame, and other information [![Build Status](https://github.com/alexmojaki/executing/workflows/Tests/badge.svg?branch=master)](https://github.com/alexmojaki/executing/actions) [![Coverage Status](https://coveralls.io/repos/github/alexmojaki/executing/badge.svg?branch=master)](https://coveralls.io/github/alexmojaki/executing?branch=master) [![Supports Python versions 2.7 and 3.5+, including PyPy](https://img.shields.io/pypi/pyversions/executing.svg)](https://pypi.python.org/pypi/executing) This mini-package lets you get information about what a frame is currently doing, particularly the AST node being executed. * [Usage](#usage) * [Getting the AST node](#getting-the-ast-node) * [Getting the source code of the node](#getting-the-source-code-of-the-node) * [Getting the `__qualname__` of the current function](#getting-the-__qualname__-of-the-current-function) * [The Source class](#the-source-class) * [Installation](#installation) * [How does it work?](#how-does-it-work) * [Is it reliable?](#is-it-reliable) * [Which nodes can it identify?](#which-nodes-can-it-identify) * [Libraries that use this](#libraries-that-use-this) ```python import executing node = executing.Source.executing(frame).node ``` Then `node` will be an AST node (from the `ast` standard library module) or None if the node couldn't be identified (which may happen often and should always be checked). `node` will always be the same instance for multiple calls with frames at the same point of execution. If you have a traceback object, pass it directly to `Source.executing()` rather than the `tb_frame` attribute to get the correct node. For this you will need to separately install the [`asttokens`](https://github.com/gristlabs/asttokens) library, then obtain an `ASTTokens` object: ```python executing.Source.executing(frame).source.asttokens() ``` or: ```python executing.Source.for_frame(frame).asttokens() ``` or use one of the convenience methods: ```python executing.Source.executing(frame).text() executing.Source.executing(frame).text_range() ``` ```python executing.Source.executing(frame).code_qualname() ``` or: ```python executing.Source.for_frame(frame).code_qualname(frame.f_code) ``` Everything goes through the `Source` class. Only one instance of the class is created for each filename. Subclassing it to add more attributes on creation or methods is recommended. The classmethods such as `executing` will respect this. See the source code and docstrings for more detail. pip install executing If you don't like that you can just copy the file `executing.py`, there are no dependencies (but of course you won't get updates). Suppose the frame is executing this line: ```python self.foo(bar.x) ``` and in particular it's currently obtaining the attribute `self.foo`. Looking at the bytecode, specifically `frame.f_code.co_code[frame.f_lasti]`, we can tell that it's loading an attribute, but it's not obvious which one. We can narrow down the statement being executed using `frame.f_lineno` and find the two `ast.Attribute` nodes representing `self.foo` and `bar.x`. How do we find out which one it is, without recreating the entire compiler in Python? The trick is to modify the AST slightly for each candidate expression and observe the changes in the bytecode instructions. We change the AST to this: ```python (self.foo ** 'longuniqueconstant')(bar.x) ``` and compile it, and the bytecode will be almost the same but there will be two new instructions: LOAD_CONST 'longuniqueconstant' BINARY_POWER and just before that will be a `LOAD_ATTR` instruction corresponding to `self.foo`. Seeing that it's in the same position as the original instruction lets us know we've found our match. Yes - if it identifies a node, you can trust that it's identified the correct one. The tests are very thorough - in addition to unit tests which check various situations directly, there are property tests against a large number of files (see the filenames printed in [this build](https://travis-ci.org/alexmojaki/executing/jobs/557970457)) with real code. Specifically, for each file, the tests: 1. Identify as many nodes as possible from all the bytecode instructions in the file, and assert that they are all distinct 2. Find all the nodes that should be identifiable, and assert that they were indeed identified somewhere In other words, it shows that there is a one-to-one mapping between the nodes and the instructions that can be handled. This leaves very little room for a bug to creep in. Furthermore, `executing` checks that the instructions compiled from the modified AST exactly match the original code save for a few small known exceptions. This accounts for all the quirks and optimisations in the interpreter. Currently it works in almost all cases for the following `ast` nodes: - `Call`, e.g. `self.foo(bar)` - `Attribute`, e.g. `point.x` - `Subscript`, e.g. `lst[1]` - `BinOp`, e.g. `x + y` (doesn't include `and` and `or`) - `UnaryOp`, e.g. `-n` (includes `not` but only works sometimes) - `Compare` e.g. `a < b` (not for chains such as `0 < p < 1`) The plan is to extend to more operations in the future. - **[`stack_data`](https://github.com/alexmojaki/stack_data)**: Extracts data from stack frames and tracebacks, particularly to display more useful tracebacks than the default. Also uses another related library of mine: **[`pure_eval`](https://github.com/alexmojaki/pure_eval)**. - **[`futurecoder`](https://futurecoder.io/)**: Highlights the executing node in tracebacks using `executing` via `stack_data`, and provides debugging with `snoop`. - **[`snoop`](https://github.com/alexmojaki/snoop)**: A feature-rich and convenient debugging library. Uses `executing` to show the operation which caused an exception and to allow the `pp` function to display the source of its arguments. - **[`heartrate`](https://github.com/alexmojaki/heartrate)**: A simple real time visualisation of the execution of a Python program. Uses `executing` to highlight currently executing operations, particularly in each frame of the stack trace. - **[`sorcery`](https://github.com/alexmojaki/sorcery)**: Dark magic delights in Python. Uses `executing` to let special callables called spells know where they're being called from. - **[`IPython`](https://github.com/ipython/ipython/pull/12150)**: Highlights the executing node in tracebacks using `executing` via [`stack_data`](https://github.com/alexmojaki/stack_data). - **[`icecream`](https://github.com/gruns/icecream)**: 🍦 Sweet and creamy print debugging. Uses `executing` to identify where `ic` is called and print its arguments. - **[`friendly_traceback`](https://github.com/friendly-traceback/friendly-traceback)**: Uses `stack_data` and `executing` to pinpoint the cause of errors and provide helpful explanations. - **[`python-devtools`](https://github.com/samuelcolvin/python-devtools)**: Uses `executing` for print debugging similar to `icecream`. - **[`sentry_sdk`](https://github.com/getsentry/sentry-python)**: Add the integration `sentry_sdk.integrations.executingExecutingIntegration()` to show the function `__qualname__` in each frame in sentry events. - **[`varname`](https://github.com/pwwang/python-varname)**: Dark magics about variable names in python. Uses `executing` to find where its various magical functions like `varname` and `nameof` are called from. https://github.com/alexmojaki/executing python-executing src bc2bd65f006cc9d459daee7921c495e02efe9fcf5882e57da94380a2427f6deb Get the currently executing AST node of a frame, and other information [![Build Status](https://github.com/alexmojaki/executing/workflows/Tests/badge.svg?branch=master)](https://github.com/alexmojaki/executing/actions) [![Coverage Status](https://coveralls.io/repos/github/alexmojaki/executing/badge.svg?branch=master)](https://coveralls.io/github/alexmojaki/executing?branch=master) [![Supports Python versions 2.7 and 3.5+, including PyPy](https://img.shields.io/pypi/pyversions/executing.svg)](https://pypi.python.org/pypi/executing) This mini-package lets you get information about what a frame is currently doing, particularly the AST node being executed. * [Usage](#usage) * [Getting the AST node](#getting-the-ast-node) * [Getting the source code of the node](#getting-the-source-code-of-the-node) * [Getting the `__qualname__` of the current function](#getting-the-__qualname__-of-the-current-function) * [The Source class](#the-source-class) * [Installation](#installation) * [How does it work?](#how-does-it-work) * [Is it reliable?](#is-it-reliable) * [Which nodes can it identify?](#which-nodes-can-it-identify) * [Libraries that use this](#libraries-that-use-this) ```python import executing node = executing.Source.executing(frame).node ``` Then `node` will be an AST node (from the `ast` standard library module) or None if the node couldn't be identified (which may happen often and should always be checked). `node` will always be the same instance for multiple calls with frames at the same point of execution. If you have a traceback object, pass it directly to `Source.executing()` rather than the `tb_frame` attribute to get the correct node. For this you will need to separately install the [`asttokens`](https://github.com/gristlabs/asttokens) library, then obtain an `ASTTokens` object: ```python executing.Source.executing(frame).source.asttokens() ``` or: ```python executing.Source.for_frame(frame).asttokens() ``` or use one of the convenience methods: ```python executing.Source.executing(frame).text() executing.Source.executing(frame).text_range() ``` ```python executing.Source.executing(frame).code_qualname() ``` or: ```python executing.Source.for_frame(frame).code_qualname(frame.f_code) ``` Everything goes through the `Source` class. Only one instance of the class is created for each filename. Subclassing it to add more attributes on creation or methods is recommended. The classmethods such as `executing` will respect this. See the source code and docstrings for more detail. pip install executing If you don't like that you can just copy the file `executing.py`, there are no dependencies (but of course you won't get updates). Suppose the frame is executing this line: ```python self.foo(bar.x) ``` and in particular it's currently obtaining the attribute `self.foo`. Looking at the bytecode, specifically `frame.f_code.co_code[frame.f_lasti]`, we can tell that it's loading an attribute, but it's not obvious which one. We can narrow down the statement being executed using `frame.f_lineno` and find the two `ast.Attribute` nodes representing `self.foo` and `bar.x`. How do we find out which one it is, without recreating the entire compiler in Python? The trick is to modify the AST slightly for each candidate expression and observe the changes in the bytecode instructions. We change the AST to this: ```python (self.foo ** 'longuniqueconstant')(bar.x) ``` and compile it, and the bytecode will be almost the same but there will be two new instructions: LOAD_CONST 'longuniqueconstant' BINARY_POWER and just before that will be a `LOAD_ATTR` instruction corresponding to `self.foo`. Seeing that it's in the same position as the original instruction lets us know we've found our match. Yes - if it identifies a node, you can trust that it's identified the correct one. The tests are very thorough - in addition to unit tests which check various situations directly, there are property tests against a large number of files (see the filenames printed in [this build](https://travis-ci.org/alexmojaki/executing/jobs/557970457)) with real code. Specifically, for each file, the tests: 1. Identify as many nodes as possible from all the bytecode instructions in the file, and assert that they are all distinct 2. Find all the nodes that should be identifiable, and assert that they were indeed identified somewhere In other words, it shows that there is a one-to-one mapping between the nodes and the instructions that can be handled. This leaves very little room for a bug to creep in. Furthermore, `executing` checks that the instructions compiled from the modified AST exactly match the original code save for a few small known exceptions. This accounts for all the quirks and optimisations in the interpreter. Currently it works in almost all cases for the following `ast` nodes: - `Call`, e.g. `self.foo(bar)` - `Attribute`, e.g. `point.x` - `Subscript`, e.g. `lst[1]` - `BinOp`, e.g. `x + y` (doesn't include `and` and `or`) - `UnaryOp`, e.g. `-n` (includes `not` but only works sometimes) - `Compare` e.g. `a < b` (not for chains such as `0 < p < 1`) The plan is to extend to more operations in the future. - **[`stack_data`](https://github.com/alexmojaki/stack_data)**: Extracts data from stack frames and tracebacks, particularly to display more useful tracebacks than the default. Also uses another related library of mine: **[`pure_eval`](https://github.com/alexmojaki/pure_eval)**. - **[`futurecoder`](https://futurecoder.io/)**: Highlights the executing node in tracebacks using `executing` via `stack_data`, and provides debugging with `snoop`. - **[`snoop`](https://github.com/alexmojaki/snoop)**: A feature-rich and convenient debugging library. Uses `executing` to show the operation which caused an exception and to allow the `pp` function to display the source of its arguments. - **[`heartrate`](https://github.com/alexmojaki/heartrate)**: A simple real time visualisation of the execution of a Python program. Uses `executing` to highlight currently executing operations, particularly in each frame of the stack trace. - **[`sorcery`](https://github.com/alexmojaki/sorcery)**: Dark magic delights in Python. Uses `executing` to let special callables called spells know where they're being called from. - **[`IPython`](https://github.com/ipython/ipython/pull/12150)**: Highlights the executing node in tracebacks using `executing` via [`stack_data`](https://github.com/alexmojaki/stack_data). - **[`icecream`](https://github.com/gruns/icecream)**: 🍦 Sweet and creamy print debugging. Uses `executing` to identify where `ic` is called and print its arguments. - **[`friendly_traceback`](https://github.com/friendly-traceback/friendly-traceback)**: Uses `stack_data` and `executing` to pinpoint the cause of errors and provide helpful explanations. - **[`python-devtools`](https://github.com/samuelcolvin/python-devtools)**: Uses `executing` for print debugging similar to `icecream`. - **[`sentry_sdk`](https://github.com/getsentry/sentry-python)**: Add the integration `sentry_sdk.integrations.executingExecutingIntegration()` to show the function `__qualname__` in each frame in sentry events. - **[`varname`](https://github.com/pwwang/python-varname)**: Dark magics about variable names in python. Uses `executing` to find where its various magical functions like `varname` and `nameof` are called from. https://github.com/alexmojaki/executing python-executing-help noarch 0cd02a3e5275e8d8b46663b5eacea7872554660152fc43bccc997bc42e2753f5 Development documents and examples for executing [![Build Status](https://github.com/alexmojaki/executing/workflows/Tests/badge.svg?branch=master)](https://github.com/alexmojaki/executing/actions) [![Coverage Status](https://coveralls.io/repos/github/alexmojaki/executing/badge.svg?branch=master)](https://coveralls.io/github/alexmojaki/executing?branch=master) [![Supports Python versions 2.7 and 3.5+, including PyPy](https://img.shields.io/pypi/pyversions/executing.svg)](https://pypi.python.org/pypi/executing) This mini-package lets you get information about what a frame is currently doing, particularly the AST node being executed. * [Usage](#usage) * [Getting the AST node](#getting-the-ast-node) * [Getting the source code of the node](#getting-the-source-code-of-the-node) * [Getting the `__qualname__` of the current function](#getting-the-__qualname__-of-the-current-function) * [The Source class](#the-source-class) * [Installation](#installation) * [How does it work?](#how-does-it-work) * [Is it reliable?](#is-it-reliable) * [Which nodes can it identify?](#which-nodes-can-it-identify) * [Libraries that use this](#libraries-that-use-this) ```python import executing node = executing.Source.executing(frame).node ``` Then `node` will be an AST node (from the `ast` standard library module) or None if the node couldn't be identified (which may happen often and should always be checked). `node` will always be the same instance for multiple calls with frames at the same point of execution. If you have a traceback object, pass it directly to `Source.executing()` rather than the `tb_frame` attribute to get the correct node. For this you will need to separately install the [`asttokens`](https://github.com/gristlabs/asttokens) library, then obtain an `ASTTokens` object: ```python executing.Source.executing(frame).source.asttokens() ``` or: ```python executing.Source.for_frame(frame).asttokens() ``` or use one of the convenience methods: ```python executing.Source.executing(frame).text() executing.Source.executing(frame).text_range() ``` ```python executing.Source.executing(frame).code_qualname() ``` or: ```python executing.Source.for_frame(frame).code_qualname(frame.f_code) ``` Everything goes through the `Source` class. Only one instance of the class is created for each filename. Subclassing it to add more attributes on creation or methods is recommended. The classmethods such as `executing` will respect this. See the source code and docstrings for more detail. pip install executing If you don't like that you can just copy the file `executing.py`, there are no dependencies (but of course you won't get updates). Suppose the frame is executing this line: ```python self.foo(bar.x) ``` and in particular it's currently obtaining the attribute `self.foo`. Looking at the bytecode, specifically `frame.f_code.co_code[frame.f_lasti]`, we can tell that it's loading an attribute, but it's not obvious which one. We can narrow down the statement being executed using `frame.f_lineno` and find the two `ast.Attribute` nodes representing `self.foo` and `bar.x`. How do we find out which one it is, without recreating the entire compiler in Python? The trick is to modify the AST slightly for each candidate expression and observe the changes in the bytecode instructions. We change the AST to this: ```python (self.foo ** 'longuniqueconstant')(bar.x) ``` and compile it, and the bytecode will be almost the same but there will be two new instructions: LOAD_CONST 'longuniqueconstant' BINARY_POWER and just before that will be a `LOAD_ATTR` instruction corresponding to `self.foo`. Seeing that it's in the same position as the original instruction lets us know we've found our match. Yes - if it identifies a node, you can trust that it's identified the correct one. The tests are very thorough - in addition to unit tests which check various situations directly, there are property tests against a large number of files (see the filenames printed in [this build](https://travis-ci.org/alexmojaki/executing/jobs/557970457)) with real code. Specifically, for each file, the tests: 1. Identify as many nodes as possible from all the bytecode instructions in the file, and assert that they are all distinct 2. Find all the nodes that should be identifiable, and assert that they were indeed identified somewhere In other words, it shows that there is a one-to-one mapping between the nodes and the instructions that can be handled. This leaves very little room for a bug to creep in. Furthermore, `executing` checks that the instructions compiled from the modified AST exactly match the original code save for a few small known exceptions. This accounts for all the quirks and optimisations in the interpreter. Currently it works in almost all cases for the following `ast` nodes: - `Call`, e.g. `self.foo(bar)` - `Attribute`, e.g. `point.x` - `Subscript`, e.g. `lst[1]` - `BinOp`, e.g. `x + y` (doesn't include `and` and `or`) - `UnaryOp`, e.g. `-n` (includes `not` but only works sometimes) - `Compare` e.g. `a < b` (not for chains such as `0 < p < 1`) The plan is to extend to more operations in the future. - **[`stack_data`](https://github.com/alexmojaki/stack_data)**: Extracts data from stack frames and tracebacks, particularly to display more useful tracebacks than the default. Also uses another related library of mine: **[`pure_eval`](https://github.com/alexmojaki/pure_eval)**. - **[`futurecoder`](https://futurecoder.io/)**: Highlights the executing node in tracebacks using `executing` via `stack_data`, and provides debugging with `snoop`. - **[`snoop`](https://github.com/alexmojaki/snoop)**: A feature-rich and convenient debugging library. Uses `executing` to show the operation which caused an exception and to allow the `pp` function to display the source of its arguments. - **[`heartrate`](https://github.com/alexmojaki/heartrate)**: A simple real time visualisation of the execution of a Python program. Uses `executing` to highlight currently executing operations, particularly in each frame of the stack trace. - **[`sorcery`](https://github.com/alexmojaki/sorcery)**: Dark magic delights in Python. Uses `executing` to let special callables called spells know where they're being called from. - **[`IPython`](https://github.com/ipython/ipython/pull/12150)**: Highlights the executing node in tracebacks using `executing` via [`stack_data`](https://github.com/alexmojaki/stack_data). - **[`icecream`](https://github.com/gruns/icecream)**: 🍦 Sweet and creamy print debugging. Uses `executing` to identify where `ic` is called and print its arguments. - **[`friendly_traceback`](https://github.com/friendly-traceback/friendly-traceback)**: Uses `stack_data` and `executing` to pinpoint the cause of errors and provide helpful explanations. - **[`python-devtools`](https://github.com/samuelcolvin/python-devtools)**: Uses `executing` for print debugging similar to `icecream`. - **[`sentry_sdk`](https://github.com/getsentry/sentry-python)**: Add the integration `sentry_sdk.integrations.executingExecutingIntegration()` to show the function `__qualname__` in each frame in sentry events. - **[`varname`](https://github.com/pwwang/python-varname)**: Dark magics about variable names in python. Uses `executing` to find where its various magical functions like `varname` and `nameof` are called from. https://github.com/alexmojaki/executing python-executing-help noarch bc406203b44c04bea0180abd5cec42ef7279ec0c2f9f184c3d35a6ef6e7393c4 Development documents and examples for executing [![Build Status](https://github.com/alexmojaki/executing/workflows/Tests/badge.svg?branch=master)](https://github.com/alexmojaki/executing/actions) [![Coverage Status](https://coveralls.io/repos/github/alexmojaki/executing/badge.svg?branch=master)](https://coveralls.io/github/alexmojaki/executing?branch=master) [![Supports Python versions 2.7 and 3.5+, including PyPy](https://img.shields.io/pypi/pyversions/executing.svg)](https://pypi.python.org/pypi/executing) This mini-package lets you get information about what a frame is currently doing, particularly the AST node being executed. * [Usage](#usage) * [Getting the AST node](#getting-the-ast-node) * [Getting the source code of the node](#getting-the-source-code-of-the-node) * [Getting the `__qualname__` of the current function](#getting-the-__qualname__-of-the-current-function) * [The Source class](#the-source-class) * [Installation](#installation) * [How does it work?](#how-does-it-work) * [Is it reliable?](#is-it-reliable) * [Which nodes can it identify?](#which-nodes-can-it-identify) * [Libraries that use this](#libraries-that-use-this) ```python import executing node = executing.Source.executing(frame).node ``` Then `node` will be an AST node (from the `ast` standard library module) or None if the node couldn't be identified (which may happen often and should always be checked). `node` will always be the same instance for multiple calls with frames at the same point of execution. If you have a traceback object, pass it directly to `Source.executing()` rather than the `tb_frame` attribute to get the correct node. For this you will need to separately install the [`asttokens`](https://github.com/gristlabs/asttokens) library, then obtain an `ASTTokens` object: ```python executing.Source.executing(frame).source.asttokens() ``` or: ```python executing.Source.for_frame(frame).asttokens() ``` or use one of the convenience methods: ```python executing.Source.executing(frame).text() executing.Source.executing(frame).text_range() ``` ```python executing.Source.executing(frame).code_qualname() ``` or: ```python executing.Source.for_frame(frame).code_qualname(frame.f_code) ``` Everything goes through the `Source` class. Only one instance of the class is created for each filename. Subclassing it to add more attributes on creation or methods is recommended. The classmethods such as `executing` will respect this. See the source code and docstrings for more detail. pip install executing If you don't like that you can just copy the file `executing.py`, there are no dependencies (but of course you won't get updates). Suppose the frame is executing this line: ```python self.foo(bar.x) ``` and in particular it's currently obtaining the attribute `self.foo`. Looking at the bytecode, specifically `frame.f_code.co_code[frame.f_lasti]`, we can tell that it's loading an attribute, but it's not obvious which one. We can narrow down the statement being executed using `frame.f_lineno` and find the two `ast.Attribute` nodes representing `self.foo` and `bar.x`. How do we find out which one it is, without recreating the entire compiler in Python? The trick is to modify the AST slightly for each candidate expression and observe the changes in the bytecode instructions. We change the AST to this: ```python (self.foo ** 'longuniqueconstant')(bar.x) ``` and compile it, and the bytecode will be almost the same but there will be two new instructions: LOAD_CONST 'longuniqueconstant' BINARY_POWER and just before that will be a `LOAD_ATTR` instruction corresponding to `self.foo`. Seeing that it's in the same position as the original instruction lets us know we've found our match. Yes - if it identifies a node, you can trust that it's identified the correct one. The tests are very thorough - in addition to unit tests which check various situations directly, there are property tests against a large number of files (see the filenames printed in [this build](https://travis-ci.org/alexmojaki/executing/jobs/557970457)) with real code. Specifically, for each file, the tests: 1. Identify as many nodes as possible from all the bytecode instructions in the file, and assert that they are all distinct 2. Find all the nodes that should be identifiable, and assert that they were indeed identified somewhere In other words, it shows that there is a one-to-one mapping between the nodes and the instructions that can be handled. This leaves very little room for a bug to creep in. Furthermore, `executing` checks that the instructions compiled from the modified AST exactly match the original code save for a few small known exceptions. This accounts for all the quirks and optimisations in the interpreter. Currently it works in almost all cases for the following `ast` nodes: - `Call`, e.g. `self.foo(bar)` - `Attribute`, e.g. `point.x` - `Subscript`, e.g. `lst[1]` - `BinOp`, e.g. `x + y` (doesn't include `and` and `or`) - `UnaryOp`, e.g. `-n` (includes `not` but only works sometimes) - `Compare` e.g. `a < b` (not for chains such as `0 < p < 1`) The plan is to extend to more operations in the future. - **[`stack_data`](https://github.com/alexmojaki/stack_data)**: Extracts data from stack frames and tracebacks, particularly to display more useful tracebacks than the default. Also uses another related library of mine: **[`pure_eval`](https://github.com/alexmojaki/pure_eval)**. - **[`futurecoder`](https://futurecoder.io/)**: Highlights the executing node in tracebacks using `executing` via `stack_data`, and provides debugging with `snoop`. - **[`snoop`](https://github.com/alexmojaki/snoop)**: A feature-rich and convenient debugging library. Uses `executing` to show the operation which caused an exception and to allow the `pp` function to display the source of its arguments. - **[`heartrate`](https://github.com/alexmojaki/heartrate)**: A simple real time visualisation of the execution of a Python program. Uses `executing` to highlight currently executing operations, particularly in each frame of the stack trace. - **[`sorcery`](https://github.com/alexmojaki/sorcery)**: Dark magic delights in Python. Uses `executing` to let special callables called spells know where they're being called from. - **[`IPython`](https://github.com/ipython/ipython/pull/12150)**: Highlights the executing node in tracebacks using `executing` via [`stack_data`](https://github.com/alexmojaki/stack_data). - **[`icecream`](https://github.com/gruns/icecream)**: 🍦 Sweet and creamy print debugging. Uses `executing` to identify where `ic` is called and print its arguments. - **[`friendly_traceback`](https://github.com/friendly-traceback/friendly-traceback)**: Uses `stack_data` and `executing` to pinpoint the cause of errors and provide helpful explanations. - **[`python-devtools`](https://github.com/samuelcolvin/python-devtools)**: Uses `executing` for print debugging similar to `icecream`. - **[`sentry_sdk`](https://github.com/getsentry/sentry-python)**: Add the integration `sentry_sdk.integrations.executingExecutingIntegration()` to show the function `__qualname__` in each frame in sentry events. - **[`varname`](https://github.com/pwwang/python-varname)**: Dark magics about variable names in python. Uses `executing` to find where its various magical functions like `varname` and `nameof` are called from. https://github.com/alexmojaki/executing python-flask-whooshee src 706275da76f63bbec2c5eee584f3be6345e345dcfabfd54b20e0a740039ed5ab Flask-SQLAlchemy - Whoosh Integration Customizable Flask - SQLAlchemy - Whoosh integration https://github.com/bkabrda/flask-whooshee python-flask-whooshee-help noarch c978af6e2f756c7db006ab49983e5f47a6ace9422d64d6446dd3d659402d83ca Development documents and examples for flask-whooshee Customizable Flask - SQLAlchemy - Whoosh integration https://github.com/bkabrda/flask-whooshee python-html2text src 288f5a0e08d47c33d1e179b5e2df1d6d89e075aad5fc92daeb28141121c87d87 Turn HTML into equivalent Markdown-structured text. Convert HTML to Markdown-formatted text. https://github.com/Alir3z4/html2text/ python-html2text-help noarch 9007c028d5c2fcdb3ff4210e4224ef8f8642fcf2065e3e6b7fde14993dd5524a Development documents and examples for html2text Convert HTML to Markdown-formatted text. https://github.com/Alir3z4/html2text/ python-html5-parser src 3fe2d1f3e72ec3df7ea62bd5a7910d84ec4e21ff2bf38ff21584c5e128425885 A fast, standards compliant, C based, HTML 5 parser for python A fast, standards compliant, C based, HTML 5 parser for python https://pypi.python.org/pypi/html5-parser python-html5-parser src 85e084df474c87aaed8092b4b5c431c198a06f68513e654a18024b21cbd92fd3 A fast, standards compliant, C based, HTML 5 parser for python A fast, standards compliant, C based, HTML 5 parser for python https://pypi.python.org/pypi/html5-parser python-html5-parser-debuginfo aarch64 c5f7448709cba5e2d4d2b21db9d79bcc80bb2dd51f3ca884995c3e2604638d46 Debug information for package python-html5-parser This package provides debug information for package python-html5-parser. Debug information is useful when developing applications that use this package or when debugging this package. https://pypi.python.org/pypi/html5-parser python-html5-parser-debuginfo aarch64 29d7c9307a7b1791980814e03d196f346b625bbe4d57bbd76b10825ea8248921 Debug information for package python-html5-parser This package provides debug information for package python-html5-parser. Debug information is useful when developing applications that use this package or when debugging this package. https://pypi.python.org/pypi/html5-parser python-html5-parser-debugsource aarch64 46f4d3f3b566d9814bd680bd3abe5b430dd813bae576979cc93f1c9f2ad2057a Debug sources for package python-html5-parser This package provides debug sources for package python-html5-parser. Debug sources are useful when developing applications that use this package or when debugging this package. https://pypi.python.org/pypi/html5-parser python-html5-parser-debugsource aarch64 67b5dbde44b4386d33bf88ccee709947c2a4dfadca5fcd45ad739a374e46913a Debug sources for package python-html5-parser This package provides debug sources for package python-html5-parser. Debug sources are useful when developing applications that use this package or when debugging this package. https://pypi.python.org/pypi/html5-parser python-ipdb src c76487bb04b6e1867aa6a36a36ea493cbf9d0f416e971e12437a3ea07c6f7f76 IPython-enabled pdb https://github.com/gotcha/ipdb python-ipdb-help noarch e02079fbf70874873220db1f85859109b303ae18c1e87a7e4f9e902c360c618b Development documents and examples for ipdb https://github.com/gotcha/ipdb python-ipython src 1dffa7b29a67c5daeebaded10d781e86c3fdaae4fc7a00a791c046954719d7bc IPython: Productive Interactive Computing IPython provides a rich toolkit to help you make the most out of using Python interactively. Its main components are: * A powerful interactive Python shell * A `Jupyter <https://jupyter.org/>`_ kernel to work with Python code in Jupyter notebooks and other interactive frontends. The enhanced interactive Python shells have the following main features: * Comprehensive object introspection. * Input history, persistent across sessions. * Caching of output results during a session with automatically generated references. * Extensible tab completion, with support by default for completion of python variables and keywords, filenames and function keywords. * Extensible system of 'magic' commands for controlling the environment and performing many tasks related either to IPython or the operating system. * A rich configuration system with easy switching between different setups (simpler than changing $PYTHONSTARTUP environment variables every time). * Session logging and reloading. * Extensible syntax processing for special purpose situations. * Access to the system shell with user-extensible alias system. * Easily embeddable in other Python programs and GUIs. * Integrated access to the pdb debugger and the Python profiler. The latest development version is always available from IPython's `GitHub site <http://github.com/ipython>`_. https://ipython.org python-ipython src 8d6d32c437e19840d796334c1584a07cf0ef03732cbcd5f57f1b324dab33df61 IPython: Productive Interactive Computing IPython provides a rich toolkit to help you make the most out of using Python interactively. Its main components are: * A powerful interactive Python shell * A `Jupyter <https://jupyter.org/>`_ kernel to work with Python code in Jupyter notebooks and other interactive frontends. The enhanced interactive Python shells have the following main features: * Comprehensive object introspection. * Input history, persistent across sessions. * Caching of output results during a session with automatically generated references. * Extensible tab completion, with support by default for completion of python variables and keywords, filenames and function keywords. * Extensible system of 'magic' commands for controlling the environment and performing many tasks related either to IPython or the operating system. * A rich configuration system with easy switching between different setups (simpler than changing $PYTHONSTARTUP environment variables every time). * Session logging and reloading. * Extensible syntax processing for special purpose situations. * Access to the system shell with user-extensible alias system. * Easily embeddable in other Python programs and GUIs. * Integrated access to the pdb debugger and the Python profiler. The latest development version is always available from IPython's `GitHub site <http://github.com/ipython>`_. https://ipython.org python-ipython-help noarch 03a04bfa42b056abe7ed8a3d05250cb8f9b0fde0573ae954d18d18926227338d Development documents and examples for ipython IPython provides a rich toolkit to help you make the most out of using Python interactively. Its main components are: * A powerful interactive Python shell * A `Jupyter <https://jupyter.org/>`_ kernel to work with Python code in Jupyter notebooks and other interactive frontends. The enhanced interactive Python shells have the following main features: * Comprehensive object introspection. * Input history, persistent across sessions. * Caching of output results during a session with automatically generated references. * Extensible tab completion, with support by default for completion of python variables and keywords, filenames and function keywords. * Extensible system of 'magic' commands for controlling the environment and performing many tasks related either to IPython or the operating system. * A rich configuration system with easy switching between different setups (simpler than changing $PYTHONSTARTUP environment variables every time). * Session logging and reloading. * Extensible syntax processing for special purpose situations. * Access to the system shell with user-extensible alias system. * Easily embeddable in other Python programs and GUIs. * Integrated access to the pdb debugger and the Python profiler. The latest development version is always available from IPython's `GitHub site <http://github.com/ipython>`_. https://ipython.org python-ipython-help noarch a8163e211a55ff0e016becbccdc8c6f372d83368ed48bb2fc9fb2109461d6d8f Development documents and examples for ipython IPython provides a rich toolkit to help you make the most out of using Python interactively. Its main components are: * A powerful interactive Python shell * A `Jupyter <https://jupyter.org/>`_ kernel to work with Python code in Jupyter notebooks and other interactive frontends. The enhanced interactive Python shells have the following main features: * Comprehensive object introspection. * Input history, persistent across sessions. * Caching of output results during a session with automatically generated references. * Extensible tab completion, with support by default for completion of python variables and keywords, filenames and function keywords. * Extensible system of 'magic' commands for controlling the environment and performing many tasks related either to IPython or the operating system. * A rich configuration system with easy switching between different setups (simpler than changing $PYTHONSTARTUP environment variables every time). * Session logging and reloading. * Extensible syntax processing for special purpose situations. * Access to the system shell with user-extensible alias system. * Easily embeddable in other Python programs and GUIs. * Integrated access to the pdb debugger and the Python profiler. The latest development version is always available from IPython's `GitHub site <http://github.com/ipython>`_. https://ipython.org python-jedi src 6b25952aed553b4c56caf03b9afd01e7820437d8a4f41be8ec4d6f5f0df252f6 A static analysis tool for Python that is typically used in IDEs/editors plugins Jedi is a static analysis tool for Python that is typically used in IDEs/editors plugins. It has a focus on autocompletion and goto functionality. Other features include refactoring, code search and finding references. https://github.com/davidhalter/jedi python-jedi src b26ef3fba86b1531936a9cf93d19f70d6f43425c975312e40dfd69d2b5c149b0 A static analysis tool for Python that is typically used in IDEs/editors plugins Jedi is a static analysis tool for Python that is typically used in IDEs/editors plugins. It has a focus on autocompletion and goto functionality. Other features include refactoring, code search and finding references. https://github.com/davidhalter/jedi python-jedi-help noarch b211f89593393a83ca7412320ee0bf561c806fdb4264017a6818e5433bdb2f90 Development documents and examples for jedi Jedi is a static analysis tool for Python that is typically used in IDEs/editors plugins. It has a focus on autocompletion and goto functionality. Other features include refactoring, code search and finding references. https://github.com/davidhalter/jedi python-jedi-help noarch 3b9e07525cd329e5f3dfc9f3fd052c9e64b98b6727033e2367b97e81d677c268 Development documents and examples for jedi Jedi is a static analysis tool for Python that is typically used in IDEs/editors plugins. It has a focus on autocompletion and goto functionality. Other features include refactoring, code search and finding references. https://github.com/davidhalter/jedi python-keystoneauth1 src 683ee2410c6e0dda423cc272ed4cd4d6a96be1d433a789932ef7081e95925f06 Authentication Library for OpenStack Identity Keystoneauth provides a standard way to do authentication and service requests \ within the OpenStack ecosystem. It is designed for use in conjunction with \ the existing OpenStack clients and for simplifying the process of writing \ new clients. https://docs.openstack.org/keystoneauth/latest/ python-littleutils src d05cf5a5c548e27901cccd5c8509f1734b610c46a3a411d514d21d72fc748b22 Small collection of Python utilities Small collection of Python utilities. https://pypi.org/pypi/littleutils python-matplotlib-inline src b08dd7dd8ff9e2a1aa0a1953bf3c2b461401c048a2d496b4b090280c5cd43156 Inline Matplotlib backend for Jupyter This package provides support for matplotlib to display figures directly inline in the Jupyter notebook and related clients, as shown below. With conda: ```bash conda install -c conda-forge matplotlib-inline ``` With pip: ```bash pip install matplotlib-inline ``` Note that in current versions of JupyterLab and Jupyter Notebook, the explicit use of the `%matplotlib inline` directive is not needed anymore, though other third-party clients may still require it. This will produce a figure immediately below: ```python %matplotlib inline import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 3*np.pi, 500) plt.plot(x, np.sin(x**2)) plt.title('A simple chirp'); ``` Licensed under the terms of the BSD 3-Clause License, by the IPython Development Team (see `LICENSE` file). BSD 3-Clause License Copyright (c) 2019-2022, IPython Development Team. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. https://github.com/ipython/matplotlib-inline python-matplotlib-inline-help noarch 5dea3c4b1a0279c8a92959481c3f7c0e0728a1094d198727f4317911512378b7 Development documents and examples for matplotlib-inline This package provides support for matplotlib to display figures directly inline in the Jupyter notebook and related clients, as shown below. With conda: ```bash conda install -c conda-forge matplotlib-inline ``` With pip: ```bash pip install matplotlib-inline ``` Note that in current versions of JupyterLab and Jupyter Notebook, the explicit use of the `%matplotlib inline` directive is not needed anymore, though other third-party clients may still require it. This will produce a figure immediately below: ```python %matplotlib inline import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 3*np.pi, 500) plt.plot(x, np.sin(x**2)) plt.title('A simple chirp'); ``` Licensed under the terms of the BSD 3-Clause License, by the IPython Development Team (see `LICENSE` file). BSD 3-Clause License Copyright (c) 2019-2022, IPython Development Team. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. https://github.com/ipython/matplotlib-inline python-novaclient src 933c50c208443abd638b446415c68e18ea25c2ba4ae3110ec59fd1eefefb6fde Client library for OpenStack Compute API This is a client for the OpenStack Nova API. There's a Python API (the novaclient module), and a command-line script (nova). Each implements 100% of the OpenStack Nova API. https://docs.openstack.org/python-novaclient/latest python-openid-teams src 5eea28f412a8d81859a049a52ee6979709ce45d29c9f386784dab2013395ba50 This is an implementation of the OpenID teams extension for python-openid UNKNOWN http://www.github.com/puiterwijk/python-openid-teams/ python-openid-teams src c1518c025dfdbe47300da27af827340cad679e1bb2f39bfc0bf465ab90b048d8 This is an implementation of the OpenID teams extension for python-openid UNKNOWN http://www.github.com/puiterwijk/python-openid-teams/ python-openid-teams-help noarch cf25bd933d28c9def4e81a507f46c469b6d44a7ae92be8c5d702eaf0ceaa61fa Development documents and examples for python-openid-teams UNKNOWN http://www.github.com/puiterwijk/python-openid-teams/ python-openid-teams-help noarch 8f2c15792573eb6f2d392fa3c9c0789387e8b730a1c62e650d0be3863ebf1029 Development documents and examples for python-openid-teams UNKNOWN http://www.github.com/puiterwijk/python-openid-teams/ python-openidc-client src ef1981ab5d63a64516428c25fb802566225db85db62dfd50efe394e8c280f6ec Python OpenID Connect client with token caching and management Python OpenID Connect client with token caching and management. python-os-service-types src 1bb0b7e0a3f847ef5950c80a9d87adcb35813a52649bb7e58444f1a9a487d0bd Python library for consuming OpenStack sevice-types-authority data Python library for consuming OpenStack sevice-types-authority data https://pypi.org/project/os-service-types/ python-os-service-types-help noarch 99f78fac633c99cd5ad1409dd45ced9836e69ef213b3149e3d3b2340804e01f9 Development documents and examples for os-service-types https://pypi.org/project/os-service-types/ python-oslo-concurrency src 5e798e6190f35bcacc03ec8239e470c159e1601e4dd7762eb3d1a2d0d64b95a6 Oslo Concurrency library OpenStack library for all concurrency-related code https://docs.openstack.org/oslo.concurrency/latest/ python-oslo-concurrency src 28d557f1feff1b3cf3db568bd21b3e7c49927a0b74fcfcf64c9da3e205585e79 Oslo Concurrency library OpenStack library for all concurrency-related code https://docs.openstack.org/oslo.concurrency/latest/ python-oslo-concurrency-help noarch 6bb909b03ad6a9310f4173ae826bdf63dc92a95e666294aea33638661d2aa8d8 Oslo Concurrency library OpenStack library for all concurrency-related code https://docs.openstack.org/oslo.concurrency/latest/ python-oslo-config src f3319854d4115eebb509731c407a91a920e68ed9a2700ca6f165facb6137111f Oslo Configuration API The Oslo configuration API supports parsing command line arguments and .ini style configuration files. https://docs.openstack.org/oslo.config/latest/ python-oslo-config src b6d05dae9ccb83146c4d3719f2b2f18b781b345f68e9ab799fb7c3a033653bc5 Oslo Configuration API The Oslo configuration API supports parsing command line arguments and .ini style configuration files. https://docs.openstack.org/oslo.config/latest/ python-oslo-config-help noarch 8d0e9b6aa668c856d45c1a4a4b21ca991603569c08ea51f1d8dfe2f6a0f5737b Oslo Configuration API The Oslo configuration API supports parsing command line arguments and .ini style configuration files. https://docs.openstack.org/oslo.config/latest/ python-oslo-i18n src 484f11fed0ca3994529aacc7bc22828b01582a05ab5cb4c78cc7581f1d277c91 Oslo i18n library Internationalization and translation library https://docs.openstack.org/oslo.i18n/latest python-oslo-i18n src ce6f45f54f4f30b9958f47f16d365f19c5728a855901e45e07b99261fc5be7f0 Oslo i18n library Internationalization and translation library https://docs.openstack.org/oslo.i18n/latest python-oslo-i18n-help noarch e4037ec6758f73750cd3234a571d7efeb1751829aba772db147801d75a00f8bb Oslo i18n library Internationalization and translation library https://docs.openstack.org/oslo.i18n/latest python-oslo-serialization src a04bfa95f9dd033f589f99b6723676e7808e7ce938bec714071d2687f5d22fa0 Oslo Serialization library The oslo.serialization library provides support for representing objects in transmittable and storable formats, such as Base64, JSON and MessagePack. https://docs.openstack.org/oslo.serialization/latest/ python-oslo-serialization src 8afc8a068312d3e2eff6f45e58afb7bdcbc2325feb91326c6a21de18aa3ec445 Oslo Serialization library The oslo.serialization library provides support for representing objects in transmittable and storable formats, such as Base64, JSON and MessagePack. https://docs.openstack.org/oslo.serialization/latest/ python-oslo-serialization-help noarch c1eee28d9b7ac8a359fac3a6ec8c047da5601524b30ca84a8b900dbe918b409a Oslo Serialization library The oslo.serialization library provides support for representing objects in transmittable and storable formats, such as Base64, JSON and MessagePack. https://docs.openstack.org/oslo.serialization/latest/ python-oslo-utils src 577a439679456eae8daa0a23bb9426317ebf28005e095c6ab22cfe2330e8cf86 Oslo Utility library The oslo.utils library provides support for common utility type functions, such as encoding, exception handling, string manipulation, and time handling. https://docs.openstack.org/oslo.utils/latest/ python-oslo-utils src 2153dedfcc38c2d54f0cd774ba4cb71a3204c59d21506a3c44a10b0f47c3c8ec Oslo Utility library The oslo.utils library provides support for common utility type functions, such as encoding, exception handling, string manipulation, and time handling. https://docs.openstack.org/oslo.utils/latest/ python-oslo-utils-help noarch af67e43899100e4c6580a315190c432f8e24665ec738ab0640b5f79f5980e42c Oslo Utility library The oslo.utils library provides support for common utility type functions, such as encoding, exception handling, string manipulation, and time handling. https://docs.openstack.org/oslo.utils/latest/ python-parso src 0f7943073a7398d2e4cdec457580aaaa978ccfe3ee00b79699c270768fbd497a A Python Parser Parso is a Python parser that supports error recovery and round-trip parsing for different Python versions. Parso consists of a small API to parse Python and analyse the syntax tree. https://github.com/davidhalter/parso python-parso src 8ee26409d13eabc5d802c4caf7636c1ba0348158cb70412b1285d8e22a53484e A Python Parser Parso is a Python parser that supports error recovery and round-trip parsing for different Python versions. Parso consists of a small API to parse Python and analyse the syntax tree. https://github.com/davidhalter/parso python-parso-help noarch 8825f251f180e17313efb94b16db80973b859c4e09b2e6e7885568468f775581 Development documents and examples for parso Parso is a Python parser that supports error recovery and round-trip parsing for different Python versions. Parso consists of a small API to parse Python and analyse the syntax tree. https://github.com/davidhalter/parso python-parso-help noarch 96f262eebdc138413ac18675b0a8bc3b0434942fcec55123f8bb5be23cfdd8f7 Development documents and examples for parso Parso is a Python parser that supports error recovery and round-trip parsing for different Python versions. Parso consists of a small API to parse Python and analyse the syntax tree. https://github.com/davidhalter/parso python-pickleshare src e92f63634cd33dc3b03aa73024c93ff7b1979621a8108827672f3043a48e8030 Tiny 'shelve'-like database with concurrency support PickleShare - a small 'shelve' like datastore with concurrency support Like shelve, a PickleShareDB object acts like a normal dictionary. Unlike shelve, many processes can access the database simultaneously. Changing a value in database is immediately visible to other processes accessing the same database. Concurrency is possible because the values are stored in separate files. Hence the "database" is a directory where *all* files are governed by PickleShare. Example usage:: from pickleshare import * db = PickleShareDB('~/testpickleshare') db.clear() print("Should be empty:",db.items()) db['hello'] = 15 db['aku ankka'] = [1,2,313] db['paths/are/ok/key'] = [1,(5,46)] print(db.keys()) This module is certainly not ZODB, but can be used for low-load (non-mission-critical) situations where tiny code size trumps the advanced features of a "real" object database. Installation guide: pip install pickleshare https://github.com/pickleshare/pickleshare python-pickleshare-help noarch 67a73cea939693174be0fa25f63f7428d34c857a1d0b1e0b3c4bd892d5494af8 Development documents and examples for pickleshare PickleShare - a small 'shelve' like datastore with concurrency support Like shelve, a PickleShareDB object acts like a normal dictionary. Unlike shelve, many processes can access the database simultaneously. Changing a value in database is immediately visible to other processes accessing the same database. Concurrency is possible because the values are stored in separate files. Hence the "database" is a directory where *all* files are governed by PickleShare. Example usage:: from pickleshare import * db = PickleShareDB('~/testpickleshare') db.clear() print("Should be empty:",db.items()) db['hello'] = 15 db['aku ankka'] = [1,2,313] db['paths/are/ok/key'] = [1,(5,46)] print(db.keys()) This module is certainly not ZODB, but can be used for low-load (non-mission-critical) situations where tiny code size trumps the advanced features of a "real" object database. Installation guide: pip install pickleshare https://github.com/pickleshare/pickleshare python-prompt-toolkit src 2afb5b6633abab24a546e2e967691121445e2f861e1c277818e7a359ebc39450 Library for building powerful interactive command lines in Python prompt_toolkit is a library for building powerful interactive command lines and terminal applications in Python. https://github.com/prompt-toolkit/python-prompt-toolkit python-prompt-toolkit src 293188ca1cce4140ec7675b07836965939c81223bb9a7708a7402b3cf0e49903 Library for building powerful interactive command lines in Python prompt_toolkit is a library for building powerful interactive command lines and terminal applications in Python. https://github.com/prompt-toolkit/python-prompt-toolkit python-prompt-toolkit-help noarch 482742022fd138c514dda81541eb4d7b4469c2a2f8638edf9b99a9d06f488341 Development documents and examples for prompt-toolkit prompt_toolkit is a library for building powerful interactive command lines and terminal applications in Python. https://github.com/prompt-toolkit/python-prompt-toolkit python-prompt-toolkit-help noarch e215824c18cd79b726b21d94b0ad6d6288dc19b88e72b513ae9fd2cd7a87d0a2 Development documents and examples for prompt-toolkit prompt_toolkit is a library for building powerful interactive command lines and terminal applications in Python. https://github.com/prompt-toolkit/python-prompt-toolkit python-pure-eval src c9098783e37cdb4bd9015c3aaa73e026720272678f7f22a36f14e77cd49c35f2 Safely evaluate AST nodes without side effects [![Build Status](https://travis-ci.org/alexmojaki/pure_eval.svg?branch=master)](https://travis-ci.org/alexmojaki/pure_eval) [![Coverage Status](https://coveralls.io/repos/github/alexmojaki/pure_eval/badge.svg?branch=master)](https://coveralls.io/github/alexmojaki/pure_eval?branch=master) [![Supports Python versions 3.5+](https://img.shields.io/pypi/pyversions/pure_eval.svg)](https://pypi.python.org/pypi/pure_eval) This is a Python package that lets you safely evaluate certain AST nodes without triggering arbitrary code that may have unwanted side effects. It can be installed from PyPI: pip install pure_eval To demonstrate usage, suppose we have an object defined as follows: ```python class Rectangle: def __init__(self, width, height): self.width = width self.height = height @property def area(self): print("Calculating area...") return self.width * self.height rect = Rectangle(3, 5) ``` Given the `rect` object, we want to evaluate whatever expressions we can in this source code: ```python source = "(rect.width, rect.height, rect.area)" ``` This library works with the AST, so let's parse the source code and peek inside: ```python import ast tree = ast.parse(source) the_tuple = tree.body[0].value for node in the_tuple.elts: print(ast.dump(node)) ``` Output: ```python Attribute(value=Name(id='rect', ctx=Load()), attr='width', ctx=Load()) Attribute(value=Name(id='rect', ctx=Load()), attr='height', ctx=Load()) Attribute(value=Name(id='rect', ctx=Load()), attr='area', ctx=Load()) ``` Now to actually use the library. First construct an Evaluator: ```python from pure_eval import Evaluator evaluator = Evaluator({"rect": rect}) ``` The argument to `Evaluator` should be a mapping from variable names to their values. Or if you have access to the stack frame where `rect` is defined, you can instead use: ```python evaluator = Evaluator.from_frame(frame) ``` Now to evaluate some nodes, using `evaluator[node]`: ```python print("rect.width:", evaluator[the_tuple.elts[0]]) print("rect:", evaluator[the_tuple.elts[0].value]) ``` Output: ``` rect.width: 3 rect: <__main__.Rectangle object at 0x105b0dd30> ``` OK, but you could have done the same thing with `eval`. The useful part is that it will refuse to evaluate the property `rect.area` because that would trigger unknown code. If we try, it'll raise a `CannotEval` exception. ```python from pure_eval import CannotEval try: print("rect.area:", evaluator[the_tuple.elts[2]]) # fails except CannotEval as e: print(e) # prints CannotEval ``` To find all the expressions that can be evaluated in a tree: ```python for node, value in evaluator.find_expressions(tree): print(ast.dump(node), value) ``` Output: ```python Attribute(value=Name(id='rect', ctx=Load()), attr='width', ctx=Load()) 3 Attribute(value=Name(id='rect', ctx=Load()), attr='height', ctx=Load()) 5 Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> ``` Note that this includes `rect` three times, once for each appearance in the source code. Since all these nodes are equivalent, we can group them together: ```python from pure_eval import group_expressions for nodes, values in group_expressions(evaluator.find_expressions(tree)): print(len(nodes), "nodes with value:", values) ``` Output: ``` 1 nodes with value: 3 1 nodes with value: 5 3 nodes with value: <__main__.Rectangle object at 0x10d374d30> ``` If we want to list all the expressions in a tree, we may want to filter out certain expressions whose values are obvious. For example, suppose we have a function `foo`: ```python def foo(): pass ``` If we refer to `foo` by its name as usual, then that's not interesting: ```python from pure_eval import is_expression_interesting node = ast.parse('foo').body[0].value print(ast.dump(node)) print(is_expression_interesting(node, foo)) ``` Output: ```python Name(id='foo', ctx=Load()) False ``` But if we refer to it by a different name, then it's interesting: ```python node = ast.parse('bar').body[0].value print(ast.dump(node)) print(is_expression_interesting(node, foo)) ``` Output: ```python Name(id='bar', ctx=Load()) True ``` In general `is_expression_interesting` returns False for the following values: - Literals (e.g. `123`, `'abc'`, `[1, 2, 3]`, `{'a': (), 'b': ([1, 2], [3])}`) - Variables or attributes whose name is equal to the value's `__name__`, such as `foo` above or `self.foo` if it was a method. - Builtins (e.g. `len`) referred to by their usual name. To make things easier, you can combine finding expressions, grouping them, and filtering out the obvious ones with: ```python evaluator.interesting_expressions_grouped(root) ``` To get the source code of an AST node, I recommend [asttokens](https://github.com/gristlabs/asttokens). Here's a complete example that brings it all together: ```python from asttokens import ASTTokens from pure_eval import Evaluator source = """ x = 1 d = {x: 2} y = d[x] """ names = {} exec(source, names) atok = ASTTokens(source, parse=True) for nodes, value in Evaluator(names).interesting_expressions_grouped(atok.tree): print(atok.get_text(nodes[0]), "=", value) ``` Output: ```python x = 1 d = {1: 2} y = 2 d[x] = 2 ``` http://github.com/alexmojaki/pure_eval python-pure-eval src f112d71b6990f0bc8664c686486ff2f797de2f3dcf474a8756626e328583dfc6 Safely evaluate AST nodes without side effects [![Build Status](https://travis-ci.org/alexmojaki/pure_eval.svg?branch=master)](https://travis-ci.org/alexmojaki/pure_eval) [![Coverage Status](https://coveralls.io/repos/github/alexmojaki/pure_eval/badge.svg?branch=master)](https://coveralls.io/github/alexmojaki/pure_eval?branch=master) [![Supports Python versions 3.5+](https://img.shields.io/pypi/pyversions/pure_eval.svg)](https://pypi.python.org/pypi/pure_eval) This is a Python package that lets you safely evaluate certain AST nodes without triggering arbitrary code that may have unwanted side effects. It can be installed from PyPI: pip install pure_eval To demonstrate usage, suppose we have an object defined as follows: ```python class Rectangle: def __init__(self, width, height): self.width = width self.height = height @property def area(self): print("Calculating area...") return self.width * self.height rect = Rectangle(3, 5) ``` Given the `rect` object, we want to evaluate whatever expressions we can in this source code: ```python source = "(rect.width, rect.height, rect.area)" ``` This library works with the AST, so let's parse the source code and peek inside: ```python import ast tree = ast.parse(source) the_tuple = tree.body[0].value for node in the_tuple.elts: print(ast.dump(node)) ``` Output: ```python Attribute(value=Name(id='rect', ctx=Load()), attr='width', ctx=Load()) Attribute(value=Name(id='rect', ctx=Load()), attr='height', ctx=Load()) Attribute(value=Name(id='rect', ctx=Load()), attr='area', ctx=Load()) ``` Now to actually use the library. First construct an Evaluator: ```python from pure_eval import Evaluator evaluator = Evaluator({"rect": rect}) ``` The argument to `Evaluator` should be a mapping from variable names to their values. Or if you have access to the stack frame where `rect` is defined, you can instead use: ```python evaluator = Evaluator.from_frame(frame) ``` Now to evaluate some nodes, using `evaluator[node]`: ```python print("rect.width:", evaluator[the_tuple.elts[0]]) print("rect:", evaluator[the_tuple.elts[0].value]) ``` Output: ``` rect.width: 3 rect: <__main__.Rectangle object at 0x105b0dd30> ``` OK, but you could have done the same thing with `eval`. The useful part is that it will refuse to evaluate the property `rect.area` because that would trigger unknown code. If we try, it'll raise a `CannotEval` exception. ```python from pure_eval import CannotEval try: print("rect.area:", evaluator[the_tuple.elts[2]]) # fails except CannotEval as e: print(e) # prints CannotEval ``` To find all the expressions that can be evaluated in a tree: ```python for node, value in evaluator.find_expressions(tree): print(ast.dump(node), value) ``` Output: ```python Attribute(value=Name(id='rect', ctx=Load()), attr='width', ctx=Load()) 3 Attribute(value=Name(id='rect', ctx=Load()), attr='height', ctx=Load()) 5 Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> ``` Note that this includes `rect` three times, once for each appearance in the source code. Since all these nodes are equivalent, we can group them together: ```python from pure_eval import group_expressions for nodes, values in group_expressions(evaluator.find_expressions(tree)): print(len(nodes), "nodes with value:", values) ``` Output: ``` 1 nodes with value: 3 1 nodes with value: 5 3 nodes with value: <__main__.Rectangle object at 0x10d374d30> ``` If we want to list all the expressions in a tree, we may want to filter out certain expressions whose values are obvious. For example, suppose we have a function `foo`: ```python def foo(): pass ``` If we refer to `foo` by its name as usual, then that's not interesting: ```python from pure_eval import is_expression_interesting node = ast.parse('foo').body[0].value print(ast.dump(node)) print(is_expression_interesting(node, foo)) ``` Output: ```python Name(id='foo', ctx=Load()) False ``` But if we refer to it by a different name, then it's interesting: ```python node = ast.parse('bar').body[0].value print(ast.dump(node)) print(is_expression_interesting(node, foo)) ``` Output: ```python Name(id='bar', ctx=Load()) True ``` In general `is_expression_interesting` returns False for the following values: - Literals (e.g. `123`, `'abc'`, `[1, 2, 3]`, `{'a': (), 'b': ([1, 2], [3])}`) - Variables or attributes whose name is equal to the value's `__name__`, such as `foo` above or `self.foo` if it was a method. - Builtins (e.g. `len`) referred to by their usual name. To make things easier, you can combine finding expressions, grouping them, and filtering out the obvious ones with: ```python evaluator.interesting_expressions_grouped(root) ``` To get the source code of an AST node, I recommend [asttokens](https://github.com/gristlabs/asttokens). Here's a complete example that brings it all together: ```python from asttokens import ASTTokens from pure_eval import Evaluator source = """ x = 1 d = {x: 2} y = d[x] """ names = {} exec(source, names) atok = ASTTokens(source, parse=True) for nodes, value in Evaluator(names).interesting_expressions_grouped(atok.tree): print(atok.get_text(nodes[0]), "=", value) ``` Output: ```python x = 1 d = {1: 2} y = 2 d[x] = 2 ``` http://github.com/alexmojaki/pure_eval python-pure-eval src f889c5db6af66dcd4c950959711b968e6ef5f9adc9b97aa3def0d0199e0b2519 Safely evaluate AST nodes without side effects [![Build Status](https://travis-ci.org/alexmojaki/pure_eval.svg?branch=master)](https://travis-ci.org/alexmojaki/pure_eval) [![Coverage Status](https://coveralls.io/repos/github/alexmojaki/pure_eval/badge.svg?branch=master)](https://coveralls.io/github/alexmojaki/pure_eval?branch=master) [![Supports Python versions 3.5+](https://img.shields.io/pypi/pyversions/pure_eval.svg)](https://pypi.python.org/pypi/pure_eval) This is a Python package that lets you safely evaluate certain AST nodes without triggering arbitrary code that may have unwanted side effects. It can be installed from PyPI: pip install pure_eval To demonstrate usage, suppose we have an object defined as follows: ```python class Rectangle: def __init__(self, width, height): self.width = width self.height = height @property def area(self): print("Calculating area...") return self.width * self.height rect = Rectangle(3, 5) ``` Given the `rect` object, we want to evaluate whatever expressions we can in this source code: ```python source = "(rect.width, rect.height, rect.area)" ``` This library works with the AST, so let's parse the source code and peek inside: ```python import ast tree = ast.parse(source) the_tuple = tree.body[0].value for node in the_tuple.elts: print(ast.dump(node)) ``` Output: ```python Attribute(value=Name(id='rect', ctx=Load()), attr='width', ctx=Load()) Attribute(value=Name(id='rect', ctx=Load()), attr='height', ctx=Load()) Attribute(value=Name(id='rect', ctx=Load()), attr='area', ctx=Load()) ``` Now to actually use the library. First construct an Evaluator: ```python from pure_eval import Evaluator evaluator = Evaluator({"rect": rect}) ``` The argument to `Evaluator` should be a mapping from variable names to their values. Or if you have access to the stack frame where `rect` is defined, you can instead use: ```python evaluator = Evaluator.from_frame(frame) ``` Now to evaluate some nodes, using `evaluator[node]`: ```python print("rect.width:", evaluator[the_tuple.elts[0]]) print("rect:", evaluator[the_tuple.elts[0].value]) ``` Output: ``` rect.width: 3 rect: <__main__.Rectangle object at 0x105b0dd30> ``` OK, but you could have done the same thing with `eval`. The useful part is that it will refuse to evaluate the property `rect.area` because that would trigger unknown code. If we try, it'll raise a `CannotEval` exception. ```python from pure_eval import CannotEval try: print("rect.area:", evaluator[the_tuple.elts[2]]) # fails except CannotEval as e: print(e) # prints CannotEval ``` To find all the expressions that can be evaluated in a tree: ```python for node, value in evaluator.find_expressions(tree): print(ast.dump(node), value) ``` Output: ```python Attribute(value=Name(id='rect', ctx=Load()), attr='width', ctx=Load()) 3 Attribute(value=Name(id='rect', ctx=Load()), attr='height', ctx=Load()) 5 Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> ``` Note that this includes `rect` three times, once for each appearance in the source code. Since all these nodes are equivalent, we can group them together: ```python from pure_eval import group_expressions for nodes, values in group_expressions(evaluator.find_expressions(tree)): print(len(nodes), "nodes with value:", values) ``` Output: ``` 1 nodes with value: 3 1 nodes with value: 5 3 nodes with value: <__main__.Rectangle object at 0x10d374d30> ``` If we want to list all the expressions in a tree, we may want to filter out certain expressions whose values are obvious. For example, suppose we have a function `foo`: ```python def foo(): pass ``` If we refer to `foo` by its name as usual, then that's not interesting: ```python from pure_eval import is_expression_interesting node = ast.parse('foo').body[0].value print(ast.dump(node)) print(is_expression_interesting(node, foo)) ``` Output: ```python Name(id='foo', ctx=Load()) False ``` But if we refer to it by a different name, then it's interesting: ```python node = ast.parse('bar').body[0].value print(ast.dump(node)) print(is_expression_interesting(node, foo)) ``` Output: ```python Name(id='bar', ctx=Load()) True ``` In general `is_expression_interesting` returns False for the following values: - Literals (e.g. `123`, `'abc'`, `[1, 2, 3]`, `{'a': (), 'b': ([1, 2], [3])}`) - Variables or attributes whose name is equal to the value's `__name__`, such as `foo` above or `self.foo` if it was a method. - Builtins (e.g. `len`) referred to by their usual name. To make things easier, you can combine finding expressions, grouping them, and filtering out the obvious ones with: ```python evaluator.interesting_expressions_grouped(root) ``` To get the source code of an AST node, I recommend [asttokens](https://github.com/gristlabs/asttokens). Here's a complete example that brings it all together: ```python from asttokens import ASTTokens from pure_eval import Evaluator source = """ x = 1 d = {x: 2} y = d[x] """ names = {} exec(source, names) atok = ASTTokens(source, parse=True) for nodes, value in Evaluator(names).interesting_expressions_grouped(atok.tree): print(atok.get_text(nodes[0]), "=", value) ``` Output: ```python x = 1 d = {1: 2} y = 2 d[x] = 2 ``` http://github.com/alexmojaki/pure_eval python-pure-eval-help noarch c3c4c0ca3717be5c370c2e66ccf2ff6eb710d71c36355bffc411998d4f0e33a4 Development documents and examples for pure-eval [![Build Status](https://travis-ci.org/alexmojaki/pure_eval.svg?branch=master)](https://travis-ci.org/alexmojaki/pure_eval) [![Coverage Status](https://coveralls.io/repos/github/alexmojaki/pure_eval/badge.svg?branch=master)](https://coveralls.io/github/alexmojaki/pure_eval?branch=master) [![Supports Python versions 3.5+](https://img.shields.io/pypi/pyversions/pure_eval.svg)](https://pypi.python.org/pypi/pure_eval) This is a Python package that lets you safely evaluate certain AST nodes without triggering arbitrary code that may have unwanted side effects. It can be installed from PyPI: pip install pure_eval To demonstrate usage, suppose we have an object defined as follows: ```python class Rectangle: def __init__(self, width, height): self.width = width self.height = height @property def area(self): print("Calculating area...") return self.width * self.height rect = Rectangle(3, 5) ``` Given the `rect` object, we want to evaluate whatever expressions we can in this source code: ```python source = "(rect.width, rect.height, rect.area)" ``` This library works with the AST, so let's parse the source code and peek inside: ```python import ast tree = ast.parse(source) the_tuple = tree.body[0].value for node in the_tuple.elts: print(ast.dump(node)) ``` Output: ```python Attribute(value=Name(id='rect', ctx=Load()), attr='width', ctx=Load()) Attribute(value=Name(id='rect', ctx=Load()), attr='height', ctx=Load()) Attribute(value=Name(id='rect', ctx=Load()), attr='area', ctx=Load()) ``` Now to actually use the library. First construct an Evaluator: ```python from pure_eval import Evaluator evaluator = Evaluator({"rect": rect}) ``` The argument to `Evaluator` should be a mapping from variable names to their values. Or if you have access to the stack frame where `rect` is defined, you can instead use: ```python evaluator = Evaluator.from_frame(frame) ``` Now to evaluate some nodes, using `evaluator[node]`: ```python print("rect.width:", evaluator[the_tuple.elts[0]]) print("rect:", evaluator[the_tuple.elts[0].value]) ``` Output: ``` rect.width: 3 rect: <__main__.Rectangle object at 0x105b0dd30> ``` OK, but you could have done the same thing with `eval`. The useful part is that it will refuse to evaluate the property `rect.area` because that would trigger unknown code. If we try, it'll raise a `CannotEval` exception. ```python from pure_eval import CannotEval try: print("rect.area:", evaluator[the_tuple.elts[2]]) # fails except CannotEval as e: print(e) # prints CannotEval ``` To find all the expressions that can be evaluated in a tree: ```python for node, value in evaluator.find_expressions(tree): print(ast.dump(node), value) ``` Output: ```python Attribute(value=Name(id='rect', ctx=Load()), attr='width', ctx=Load()) 3 Attribute(value=Name(id='rect', ctx=Load()), attr='height', ctx=Load()) 5 Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> ``` Note that this includes `rect` three times, once for each appearance in the source code. Since all these nodes are equivalent, we can group them together: ```python from pure_eval import group_expressions for nodes, values in group_expressions(evaluator.find_expressions(tree)): print(len(nodes), "nodes with value:", values) ``` Output: ``` 1 nodes with value: 3 1 nodes with value: 5 3 nodes with value: <__main__.Rectangle object at 0x10d374d30> ``` If we want to list all the expressions in a tree, we may want to filter out certain expressions whose values are obvious. For example, suppose we have a function `foo`: ```python def foo(): pass ``` If we refer to `foo` by its name as usual, then that's not interesting: ```python from pure_eval import is_expression_interesting node = ast.parse('foo').body[0].value print(ast.dump(node)) print(is_expression_interesting(node, foo)) ``` Output: ```python Name(id='foo', ctx=Load()) False ``` But if we refer to it by a different name, then it's interesting: ```python node = ast.parse('bar').body[0].value print(ast.dump(node)) print(is_expression_interesting(node, foo)) ``` Output: ```python Name(id='bar', ctx=Load()) True ``` In general `is_expression_interesting` returns False for the following values: - Literals (e.g. `123`, `'abc'`, `[1, 2, 3]`, `{'a': (), 'b': ([1, 2], [3])}`) - Variables or attributes whose name is equal to the value's `__name__`, such as `foo` above or `self.foo` if it was a method. - Builtins (e.g. `len`) referred to by their usual name. To make things easier, you can combine finding expressions, grouping them, and filtering out the obvious ones with: ```python evaluator.interesting_expressions_grouped(root) ``` To get the source code of an AST node, I recommend [asttokens](https://github.com/gristlabs/asttokens). Here's a complete example that brings it all together: ```python from asttokens import ASTTokens from pure_eval import Evaluator source = """ x = 1 d = {x: 2} y = d[x] """ names = {} exec(source, names) atok = ASTTokens(source, parse=True) for nodes, value in Evaluator(names).interesting_expressions_grouped(atok.tree): print(atok.get_text(nodes[0]), "=", value) ``` Output: ```python x = 1 d = {1: 2} y = 2 d[x] = 2 ``` http://github.com/alexmojaki/pure_eval python-pure-eval-help noarch 6e9c07df49e2fbcfa2b554c0d633a256c5349a693b23deecd154baa5b3b87d17 Development documents and examples for pure-eval [![Build Status](https://travis-ci.org/alexmojaki/pure_eval.svg?branch=master)](https://travis-ci.org/alexmojaki/pure_eval) [![Coverage Status](https://coveralls.io/repos/github/alexmojaki/pure_eval/badge.svg?branch=master)](https://coveralls.io/github/alexmojaki/pure_eval?branch=master) [![Supports Python versions 3.5+](https://img.shields.io/pypi/pyversions/pure_eval.svg)](https://pypi.python.org/pypi/pure_eval) This is a Python package that lets you safely evaluate certain AST nodes without triggering arbitrary code that may have unwanted side effects. It can be installed from PyPI: pip install pure_eval To demonstrate usage, suppose we have an object defined as follows: ```python class Rectangle: def __init__(self, width, height): self.width = width self.height = height @property def area(self): print("Calculating area...") return self.width * self.height rect = Rectangle(3, 5) ``` Given the `rect` object, we want to evaluate whatever expressions we can in this source code: ```python source = "(rect.width, rect.height, rect.area)" ``` This library works with the AST, so let's parse the source code and peek inside: ```python import ast tree = ast.parse(source) the_tuple = tree.body[0].value for node in the_tuple.elts: print(ast.dump(node)) ``` Output: ```python Attribute(value=Name(id='rect', ctx=Load()), attr='width', ctx=Load()) Attribute(value=Name(id='rect', ctx=Load()), attr='height', ctx=Load()) Attribute(value=Name(id='rect', ctx=Load()), attr='area', ctx=Load()) ``` Now to actually use the library. First construct an Evaluator: ```python from pure_eval import Evaluator evaluator = Evaluator({"rect": rect}) ``` The argument to `Evaluator` should be a mapping from variable names to their values. Or if you have access to the stack frame where `rect` is defined, you can instead use: ```python evaluator = Evaluator.from_frame(frame) ``` Now to evaluate some nodes, using `evaluator[node]`: ```python print("rect.width:", evaluator[the_tuple.elts[0]]) print("rect:", evaluator[the_tuple.elts[0].value]) ``` Output: ``` rect.width: 3 rect: <__main__.Rectangle object at 0x105b0dd30> ``` OK, but you could have done the same thing with `eval`. The useful part is that it will refuse to evaluate the property `rect.area` because that would trigger unknown code. If we try, it'll raise a `CannotEval` exception. ```python from pure_eval import CannotEval try: print("rect.area:", evaluator[the_tuple.elts[2]]) # fails except CannotEval as e: print(e) # prints CannotEval ``` To find all the expressions that can be evaluated in a tree: ```python for node, value in evaluator.find_expressions(tree): print(ast.dump(node), value) ``` Output: ```python Attribute(value=Name(id='rect', ctx=Load()), attr='width', ctx=Load()) 3 Attribute(value=Name(id='rect', ctx=Load()), attr='height', ctx=Load()) 5 Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> ``` Note that this includes `rect` three times, once for each appearance in the source code. Since all these nodes are equivalent, we can group them together: ```python from pure_eval import group_expressions for nodes, values in group_expressions(evaluator.find_expressions(tree)): print(len(nodes), "nodes with value:", values) ``` Output: ``` 1 nodes with value: 3 1 nodes with value: 5 3 nodes with value: <__main__.Rectangle object at 0x10d374d30> ``` If we want to list all the expressions in a tree, we may want to filter out certain expressions whose values are obvious. For example, suppose we have a function `foo`: ```python def foo(): pass ``` If we refer to `foo` by its name as usual, then that's not interesting: ```python from pure_eval import is_expression_interesting node = ast.parse('foo').body[0].value print(ast.dump(node)) print(is_expression_interesting(node, foo)) ``` Output: ```python Name(id='foo', ctx=Load()) False ``` But if we refer to it by a different name, then it's interesting: ```python node = ast.parse('bar').body[0].value print(ast.dump(node)) print(is_expression_interesting(node, foo)) ``` Output: ```python Name(id='bar', ctx=Load()) True ``` In general `is_expression_interesting` returns False for the following values: - Literals (e.g. `123`, `'abc'`, `[1, 2, 3]`, `{'a': (), 'b': ([1, 2], [3])}`) - Variables or attributes whose name is equal to the value's `__name__`, such as `foo` above or `self.foo` if it was a method. - Builtins (e.g. `len`) referred to by their usual name. To make things easier, you can combine finding expressions, grouping them, and filtering out the obvious ones with: ```python evaluator.interesting_expressions_grouped(root) ``` To get the source code of an AST node, I recommend [asttokens](https://github.com/gristlabs/asttokens). Here's a complete example that brings it all together: ```python from asttokens import ASTTokens from pure_eval import Evaluator source = """ x = 1 d = {x: 2} y = d[x] """ names = {} exec(source, names) atok = ASTTokens(source, parse=True) for nodes, value in Evaluator(names).interesting_expressions_grouped(atok.tree): print(atok.get_text(nodes[0]), "=", value) ``` Output: ```python x = 1 d = {1: 2} y = 2 d[x] = 2 ``` http://github.com/alexmojaki/pure_eval python-py3dns src 52076b88600779ecd2b7e25c7ec477e5819f8adc8a07301b905a224fb42c435e Python 3 DNS library Python 3 DNS library: https://launchpad.net/py3dns python-py3dns src eafc22946c8b7ad060163f26861e587f10d5fdb675d31a88444283ad5d18a55f Python 3 DNS library Python 3 DNS library: https://launchpad.net/py3dns python-py3dns-help noarch 07a82dfe486403e8230cfc1febeefd7706550fe7eafb8e78663cd6cc57478e39 Development documents and examples for py3dns Python 3 DNS library: https://launchpad.net/py3dns python-py3dns-help noarch bcc435931c0491d40ee55d1551a34ecdc8149ed588f825cecbbbd5ccbddfe7f4 Development documents and examples for py3dns Python 3 DNS library: https://launchpad.net/py3dns python-pyLibravatar src 67be37c70b80fcc5d5a9d539a988128553a78985e12f543edefb86c00e22c580 Python module for Libravatar PyLibravatar is an easy way to make use of the federated Libravatar_ avatar hosting service from within your Python applications. https://launchpad.net/pylibravatar python-pyLibravatar src 72a6bc8c39e5f30a1df6f4beca65248e69bf13ad8da1c686271b8d4fa3955fcc Python module for Libravatar PyLibravatar is an easy way to make use of the federated Libravatar_ avatar hosting service from within your Python applications. https://launchpad.net/pylibravatar python-pyLibravatar src b53a800108b3969fd4a67f9a2474176838acda232759e52dbdb709af19e5cfc1 Python module for Libravatar PyLibravatar is an easy way to make use of the federated Libravatar_ avatar hosting service from within your Python applications. https://launchpad.net/pylibravatar python-pygal src 0723756e0cbb30051c58ab01f0afa5d7120e15e902cfa85c4d199d9212cd645a A Python svg graph plotting library A Python svg graph plotting library. https://www.pygal.org/ python-pygal src 0f865af50e8e81958290b7b32fdfff9036e38ceb1681f6478939b4c6897b4e7f A Python svg graph plotting library A Python svg graph plotting library. https://www.pygal.org/ python-pygal-help noarch 4fda83c12e665720fe26a57c43bd0732158e3ec65d8317607c5b289e801b485a Development documents and examples for pygal https://www.pygal.org/ python-pygal-help noarch 1e343f3f8e93ddd5ca766e489d58f8905dde34ebac7fdd98bfedeb9389d23a18 Development documents and examples for pygal https://www.pygal.org/ python-pygit2 src 70b79ac72af0bfc28491cab3ea237bdd7d26c978d1b94c2734c8c8487afc0001 Python bindings for libgit2. - Documentation - http://www.pygit2.org/ - Install - http://www.pygit2.org/install.html - Download - https://pypi.python.org/pypi/pygit2 - Source code and issue tracker - https://github.com/libgit2/pygit2 - Changelog - https://github.com/libgit2/pygit2/blob/master/CHANGELOG.rst - Authors - https://github.com/libgit2/pygit2/blob/master/AUTHORS.rst https://github.com/libgit2/pygit2 python-pygit2 src e9c795d2aed8417f44ffb3a344b9e76be5267cfaaa788d036eab13d409be8522 Python bindings for libgit2. - Documentation - http://www.pygit2.org/ - Install - http://www.pygit2.org/install.html - Download - https://pypi.python.org/pypi/pygit2 - Source code and issue tracker - https://github.com/libgit2/pygit2 - Changelog - https://github.com/libgit2/pygit2/blob/master/CHANGELOG.rst - Authors - https://github.com/libgit2/pygit2/blob/master/AUTHORS.rst https://github.com/libgit2/pygit2 python-pygit2-debuginfo aarch64 579c7dc00679d3059d7ae6bb2302d24871fe1ede3e4d6b955c1d87921b400148 Debug information for package python-pygit2 This package provides debug information for package python-pygit2. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/libgit2/pygit2 python-pygit2-debuginfo aarch64 e9af0b466ab00bf5a57d1521ea78fcc9f77a19b8ef39a292415e0578ec90d18a Debug information for package python-pygit2 This package provides debug information for package python-pygit2. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/libgit2/pygit2 python-pygit2-debugsource aarch64 8d76b7cf94da47c5f75a82bb909597c2a33a250707518b13714ab3043430d5f6 Debug sources for package python-pygit2 This package provides debug sources for package python-pygit2. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/libgit2/pygit2 python-pygit2-debugsource aarch64 8743ba7a04cbf3783ac41639394f71a3579ae4287a0a8714fcb0a928d00ea8f3 Debug sources for package python-pygit2 This package provides debug sources for package python-pygit2. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/libgit2/pygit2 python-pygit2-help aarch64 8f73bf20c6fabe8ed15495112887ab345780af9764de5dcc39fd596f11a980db Development documents and examples for pygit2 - Documentation - http://www.pygit2.org/ - Install - http://www.pygit2.org/install.html - Download - https://pypi.python.org/pypi/pygit2 - Source code and issue tracker - https://github.com/libgit2/pygit2 - Changelog - https://github.com/libgit2/pygit2/blob/master/CHANGELOG.rst - Authors - https://github.com/libgit2/pygit2/blob/master/AUTHORS.rst https://github.com/libgit2/pygit2 python-pygit2-help aarch64 f14110c2615ceec5f53085dadd93312e925c7b1b0772f023106f336ff9840073 Development documents and examples for pygit2 - Documentation - http://www.pygit2.org/ - Install - http://www.pygit2.org/install.html - Download - https://pypi.python.org/pypi/pygit2 - Source code and issue tracker - https://github.com/libgit2/pygit2 - Changelog - https://github.com/libgit2/pygit2/blob/master/CHANGELOG.rst - Authors - https://github.com/libgit2/pygit2/blob/master/AUTHORS.rst https://github.com/libgit2/pygit2 python-pytest-xdist src 04f0290619999d1abe0728cd5098aa0477e84c4ce25f464cb83836516f45ab88 pytest xdist plugin for distributed testing and loop-on-failing modes pytest xdist plugin for distributed testing and loop-on-failing modes. https://github.com/pytest-dev/pytest-xdist python-responses src 44eab42989842ffb53c2fd1e09def42421d23004bb26c46f0a54b2465943ba53 A utility library for mocking out the `requests` Python library. A utility library for mocking out the requests Python library. https://github.com/getsentry/responses python-responses-help noarch 35ca3ef60ee809c4cdcbba7d4d767ad6e68c417a528f119a0049e7bc438c83d7 A utility library for mocking out the `requests` Python library. A utility library for mocking out the requests Python library. https://github.com/getsentry/responses python-retask src d67a77f8c00213c5b386f314b8ff4a6f5450a886c0058559e89c3e1c1460c452 Python module to create and manage distributed task queues Python module to create and manage distributed task queues using redis. http://retask.readthedocs.org/en/latest/index.html python-rich src 71e4748c91e171ea377b34128bcbe35f6cfb8883a3bab2d7ede8e5fa47f6e525 Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal Rich is a Python library for rich text and beautiful formatting in the terminal https://github.com/willmcgugan/rich python-rich-help noarch 120d6a0b3ba3d0bb3777138711c2a10ce2f5ab5c8f3e59daef2c4d11e63aa136 Development documents and examples for rich Rich is a Python library for rich text and beautiful formatting in the terminal https://github.com/willmcgugan/rich python-rpmautospec src 8a6b76ac0b52cc9f67ae5e0876ec01e19e0ccba4cd308fd6320597cf92521419 Package and CLI tool to generate release fields and changelogs A package and CLI tool to generate RPM release fields and changelogs. https://pagure.io/fedora-infra/rpmautospec python-rpmautospec src 2e3c56d43af9499d25df876b2e7b7ac7a2ffc7d1979dd231e18f86e762784797 Package and CLI tool to generate release fields and changelogs A package and CLI tool to generate RPM release fields and changelogs. https://pagure.io/fedora-infra/rpmautospec python-stack-data src 740221132776ace27a35769e4677c21d491d705b160d52cf59004a581be56577 Extract data from python stack frames and tracebacks for informative displays 6 | for i in range(5): 7 | row = [] 8 | result.append(row) --> 9 | print_stack() 10 | for j in range(5): ``` The code for `print_stack()` is fairly self-explanatory. If you want to learn more details about a particular class or method I suggest looking through some docstrings. `FrameInfo` is a class that accepts either a frame or a traceback object and provides a bunch of nice attributes and properties (which are cached so you don't need to worry about performance). In particular `frame_info.lines` is a list of `Line` objects. `line.render()` returns the source code of that line suitable for display. Without any arguments it simply strips any common leading indentation. Later on we'll see a more powerful use for it. You can see that `frame_info.lines` includes some lines of surrounding context. By default it includes 3 pieces of context before the main line and 1 piece after. We can configure the amount of context by passing options: ```python options = stack_data.Options(before=1, after=0) frame_info = stack_data.FrameInfo(frame, options) ``` Then the output looks like: ``` http://github.com/alexmojaki/stack_data python-stack-data src 06e2caaf87a6d678787ae4211ebd345f8a6552004103388b997d45b8641bae9e Extract data from python stack frames and tracebacks for informative displays 6 | for i in range(5): 7 | row = [] 8 | result.append(row) --> 9 | print_stack() 10 | for j in range(5): ``` The code for `print_stack()` is fairly self-explanatory. If you want to learn more details about a particular class or method I suggest looking through some docstrings. `FrameInfo` is a class that accepts either a frame or a traceback object and provides a bunch of nice attributes and properties (which are cached so you don't need to worry about performance). In particular `frame_info.lines` is a list of `Line` objects. `line.render()` returns the source code of that line suitable for display. Without any arguments it simply strips any common leading indentation. Later on we'll see a more powerful use for it. You can see that `frame_info.lines` includes some lines of surrounding context. By default it includes 3 pieces of context before the main line and 1 piece after. We can configure the amount of context by passing options: ```python options = stack_data.Options(before=1, after=0) frame_info = stack_data.FrameInfo(frame, options) ``` Then the output looks like: ``` http://github.com/alexmojaki/stack_data python-stack-data src 64d0b88a70611ea0e15f986f420bd7165b3826ed914035cc64d3a6cb782ed32f Extract data from python stack frames and tracebacks for informative displays 6 | for i in range(5): 7 | row = [] 8 | result.append(row) --> 9 | print_stack() 10 | for j in range(5): ``` The code for `print_stack()` is fairly self-explanatory. If you want to learn more details about a particular class or method I suggest looking through some docstrings. `FrameInfo` is a class that accepts either a frame or a traceback object and provides a bunch of nice attributes and properties (which are cached so you don't need to worry about performance). In particular `frame_info.lines` is a list of `Line` objects. `line.render()` returns the source code of that line suitable for display. Without any arguments it simply strips any common leading indentation. Later on we'll see a more powerful use for it. You can see that `frame_info.lines` includes some lines of surrounding context. By default it includes 3 pieces of context before the main line and 1 piece after. We can configure the amount of context by passing options: ```python options = stack_data.Options(before=1, after=0) frame_info = stack_data.FrameInfo(frame, options) ``` Then the output looks like: ``` http://github.com/alexmojaki/stack_data python-stack-data-help noarch e6cd3ee87ea24db83da124778bbc3426c1c25e551be420ec40c7d57aca5e1c2e Development documents and examples for stack-data 6 | for i in range(5): 7 | row = [] 8 | result.append(row) --> 9 | print_stack() 10 | for j in range(5): ``` The code for `print_stack()` is fairly self-explanatory. If you want to learn more details about a particular class or method I suggest looking through some docstrings. `FrameInfo` is a class that accepts either a frame or a traceback object and provides a bunch of nice attributes and properties (which are cached so you don't need to worry about performance). In particular `frame_info.lines` is a list of `Line` objects. `line.render()` returns the source code of that line suitable for display. Without any arguments it simply strips any common leading indentation. Later on we'll see a more powerful use for it. You can see that `frame_info.lines` includes some lines of surrounding context. By default it includes 3 pieces of context before the main line and 1 piece after. We can configure the amount of context by passing options: ```python options = stack_data.Options(before=1, after=0) frame_info = stack_data.FrameInfo(frame, options) ``` Then the output looks like: ``` http://github.com/alexmojaki/stack_data python-stack-data-help noarch a7010563c5b593209b33897bf593d2be176832aa15c270d33b10df4a61b9f6fd Development documents and examples for stack-data 6 | for i in range(5): 7 | row = [] 8 | result.append(row) --> 9 | print_stack() 10 | for j in range(5): ``` The code for `print_stack()` is fairly self-explanatory. If you want to learn more details about a particular class or method I suggest looking through some docstrings. `FrameInfo` is a class that accepts either a frame or a traceback object and provides a bunch of nice attributes and properties (which are cached so you don't need to worry about performance). In particular `frame_info.lines` is a list of `Line` objects. `line.render()` returns the source code of that line suitable for display. Without any arguments it simply strips any common leading indentation. Later on we'll see a more powerful use for it. You can see that `frame_info.lines` includes some lines of surrounding context. By default it includes 3 pieces of context before the main line and 1 piece after. We can configure the amount of context by passing options: ```python options = stack_data.Options(before=1, after=0) frame_info = stack_data.FrameInfo(frame, options) ``` Then the output looks like: ``` http://github.com/alexmojaki/stack_data python-templated-dictionary src 0005719f933dfb08d47d2fc8752dde5001d4d21882b57c55a684793cf825e710 Dictionary with Jinja2 expansion Dictionary where __getitem__() is run through Jinja2 template. https://github.com/xsuchy/templated-dictionary python-templated-dictionary-help noarch 76b68c560a4386cb5429487e040dce38b465c929c6735da4570ad4fc62805c4d Development documents and examples for templated-dictionary Dictionary where __getitem__() is run through Jinja2 template. https://github.com/xsuchy/templated-dictionary python3-Authlib noarch 6524885535d99c1f7e0cc3be4e540d3549a51eba4d5b28e42ca8d3d996f9bcdc The ultimate Python library in building OAuth and OpenID Connect servers and clients. The ultimate Python library in building OAuth and OpenID Connect servers. JWS, JWK, JWA, JWT are included. https://authlib.org/ python3-CCColUtils aarch64 eed4df872dcfbe8a690bcab7c9a31ea0a7f6287afb15f1f6431f5ce0cc3a67e2 Kerberos5 Credential Cache Collection Utilities Kerberos5 Credential Cache Collection Utilities. https://pagure.io/cccolutils python3-Flask-Caching noarch 709b86fe7ce1e96227ea45e25fef7ce0613528ee5e1c9b253ec0b55f301d645a Adds caching support to Flask applications. A fork of the `Flask-cache`_ extension which adds easy cache support to Flask. https://github.com/pallets-eco/flask-caching python3-Flask-OpenID noarch 7c4c2fc4b6be45b313a102ac686aa74775bf58d9159641e8943eeb52c75f1915 OpenID support for Flask Flask-OpenID adds openid support to flask applications http://github.com/mitsuhiko/flask-openid/ python3-Flask-WTF noarch 6016dc88fb0030e8b0938b441b17930899d13d0d3e88487d62c83e933ffffb95 Form rendering, validation, and CSRF protection for Flask with WTForms. Simple integration of Flask and WTForms, including CSRF, file upload, and reCAPTCHA. https://github.com/wtforms/flask-wtf/ python3-XStatic-Bootstrap-SCSS noarch 21d62d2a1873adf7a42c3e1f45ecc5183fe527e624dc38854171c10fc20078d2 Bootstrap-SCSS 3.4.1 (XStatic packaging standard) Bootstrap style library packaged for setuptools (easy_install) / pip. This package is intended to be used by **any** project that needs these files. It intentionally does **not** provide any extra code except some metadata **nor** has any extra requirements. You MAY use some minimal support code from the XStatic base package, if you like. You can find more info about the xstatic packaging way in the package `XStatic`. https://github.com/twbs/bootstrap-sass python3-XStatic-DataTables noarch dcaed3bd1dbb373a60edfd814a2d7730748dfec23baf513b6f6963c653abe69f DataTables 1.10.15 (XStatic packaging standard) The DataTables plugin for jQuery packaged for setuptools (easy_install) / pip. This package is intended to be used by **any** project that needs these files. It intentionally does **not** provide any extra code except some metadata **nor** has any extra requirements. You MAY use some minimal support code from the XStatic base package, if you like. You can find more info about the xstatic packaging way in the package `XStatic`. http://www.datatables.net python3-XStatic-Patternfly noarch 9f10c7fa22b648b3c624e56cf7713ef3bb07e015768293e1d5fae8c5aec163d5 Patternfly 3.21.0 (XStatic packaging standard) Patternfly style library packaged for setuptools (easy_install) / pip. This package is intended to be used by **any** project that needs these files. It intentionally does **not** provide any extra code except some metadata **nor** has any extra requirements. You MAY use some minimal support code from the XStatic base package, if you like. You can find more info about the xstatic packaging way in the package `XStatic`. https://www.patternfly.org/ python3-argparse-manpage noarch 28bc2e53a5736a1c3c2f4a03304c589dbb11cdd9bcc5c078b4b18a4c83dc7d50 Build manual page from python's ArgumentParser object. Automatically build manpage from argparse https://github.com/praiskup/argparse-manpage python3-asttokens noarch 5fbf3e3ed07c7c1b4d95244efa40fbff0e59158c8920f2f0899cd9824f6f0661 Module to annotate Python abstract syntax trees with source code positions The asttokens module annotates Python abstract syntax trees (ASTs) with the positions of tokens and text in the source code that generated them. This makes it possible for tools that work with logical AST nodes to find the particular text that resulted in those nodes, for example for automated refactoring or highlighting. https://github.com/gristlabs/asttokens python3-backoff noarch 48a4d73785f3d0e4ccaf572b1fb46849db08b60605dc2df0e2b614ab292b74d2 Function decoration for backoff and retry This module provides function decorators which can be used to wrap a\ function such that it will be retried until some condition is met. It\ is meant to be of use when accessing unreliable resources with the\ potential for intermittent failures i.e. network resources and external\ APIs. Somewhat more generally, it may also be of use for dynamically\ polling resources for externally generated content. https://github.com/litl/backoff python3-blessed noarch 9d84641e1d5b55789129747f4b2d50903398dc7036c3122373bb9b0ea26de75e Easy, practical library for making terminal apps, by providing an elegant, well-documented interface to Colors, Keyboard input, and screen Positioning capabilities. Blessed is an easy, practical library for making python terminal apps https://github.com/jquast/blessed python3-cachelib noarch f8ddf2339238205de14346fbe8adafb291f4c2f443ef99191778b530c14da80f A collection of cache libraries in the same API interface. A collection of cache libraries in the same API interface. Extracted from werkzeug. https://github.com/pallets-eco/cachelib python3-copr-common noarch 104f7903c47f3714b87433e5b8f463314a79650fc7a4de76f59b20d6ad8a6294 Python code used by Copr COPR is lightweight build system. It allows you to create new project in WebUI, and submit new builds and COPR will create yum repository from latest builds. This package contains python code used by other Copr packages. Mostly useful for developers only. https://github.com/fedora-copr/copr python3-crudini noarch 5f7967a8b581585d7155ab40842880f089806d730604001a0a970cd4259d9091 A utility for manipulating ini files crudini A utility for manipulating ini files http://github.com/pixelb/crudini python3-debtcollector noarch 1588715e3a7c8a2bc1892933b7a0c9a6f7942e39c6017f5ba958707b52ec4f26 A collection of Python deprecation patterns and strategies that help you collect your technical debt in a non-destructive manner. A collection of Python deprecation patterns and strategies that help you collect your technical debt in a non-destructive manner. https://docs.openstack.org/debtcollector/latest python3-email-validator noarch d3526bdf831e04a551a0a3820f946d6d849509ece93f4bc43b9594b446161c66 A robust email address syntax and deliverability validation library. A robust email address syntax and deliverability validation library for Python by [Joshua Tauberer](https://joshdata.me). This library validates that a string is of the form `name@example.com`. This is the sort of validation you would want for an email-based login form on a website. Key features: * Checks that an email address has the correct syntax --- good for login forms or other uses related to identifying users. * Gives friendly error messages when validation fails (appropriate to show to end users). * (optionally) Checks deliverability: Does the domain name resolve? And you can override the default DNS resolver. * Supports internationalized domain names and (optionally) internationalized local parts, but blocks unsafe characters. * Normalizes email addresses (super important for internationalized addresses! see below). The library is NOT for validation of the To: line in an email message (e.g. `My Name <my@address.com>`), which [flanker](https://github.com/mailgun/flanker) is more appropriate for. And this library does NOT permit obsolete forms of email addresses, so if you need strict validation against the email specs exactly, use [pyIsEmail](https://github.com/michaelherold/pyIsEmail). This library is tested with Python 3.6+ but should work in earlier versions: [![Build Status](https://app.travis-ci.com/JoshData/python-email-validator.svg?branch=main)](https://app.travis-ci.com/JoshData/python-email-validator) https://github.com/JoshData/python-email-validator python3-executing noarch 4e08bf91af0405f9be44d37506560c208dc4c83be84402861dd81a4e5031cc4e Get the currently executing AST node of a frame, and other information [![Build Status](https://github.com/alexmojaki/executing/workflows/Tests/badge.svg?branch=master)](https://github.com/alexmojaki/executing/actions) [![Coverage Status](https://coveralls.io/repos/github/alexmojaki/executing/badge.svg?branch=master)](https://coveralls.io/github/alexmojaki/executing?branch=master) [![Supports Python versions 2.7 and 3.5+, including PyPy](https://img.shields.io/pypi/pyversions/executing.svg)](https://pypi.python.org/pypi/executing) This mini-package lets you get information about what a frame is currently doing, particularly the AST node being executed. * [Usage](#usage) * [Getting the AST node](#getting-the-ast-node) * [Getting the source code of the node](#getting-the-source-code-of-the-node) * [Getting the `__qualname__` of the current function](#getting-the-__qualname__-of-the-current-function) * [The Source class](#the-source-class) * [Installation](#installation) * [How does it work?](#how-does-it-work) * [Is it reliable?](#is-it-reliable) * [Which nodes can it identify?](#which-nodes-can-it-identify) * [Libraries that use this](#libraries-that-use-this) ```python import executing node = executing.Source.executing(frame).node ``` Then `node` will be an AST node (from the `ast` standard library module) or None if the node couldn't be identified (which may happen often and should always be checked). `node` will always be the same instance for multiple calls with frames at the same point of execution. If you have a traceback object, pass it directly to `Source.executing()` rather than the `tb_frame` attribute to get the correct node. For this you will need to separately install the [`asttokens`](https://github.com/gristlabs/asttokens) library, then obtain an `ASTTokens` object: ```python executing.Source.executing(frame).source.asttokens() ``` or: ```python executing.Source.for_frame(frame).asttokens() ``` or use one of the convenience methods: ```python executing.Source.executing(frame).text() executing.Source.executing(frame).text_range() ``` ```python executing.Source.executing(frame).code_qualname() ``` or: ```python executing.Source.for_frame(frame).code_qualname(frame.f_code) ``` Everything goes through the `Source` class. Only one instance of the class is created for each filename. Subclassing it to add more attributes on creation or methods is recommended. The classmethods such as `executing` will respect this. See the source code and docstrings for more detail. pip install executing If you don't like that you can just copy the file `executing.py`, there are no dependencies (but of course you won't get updates). Suppose the frame is executing this line: ```python self.foo(bar.x) ``` and in particular it's currently obtaining the attribute `self.foo`. Looking at the bytecode, specifically `frame.f_code.co_code[frame.f_lasti]`, we can tell that it's loading an attribute, but it's not obvious which one. We can narrow down the statement being executed using `frame.f_lineno` and find the two `ast.Attribute` nodes representing `self.foo` and `bar.x`. How do we find out which one it is, without recreating the entire compiler in Python? The trick is to modify the AST slightly for each candidate expression and observe the changes in the bytecode instructions. We change the AST to this: ```python (self.foo ** 'longuniqueconstant')(bar.x) ``` and compile it, and the bytecode will be almost the same but there will be two new instructions: LOAD_CONST 'longuniqueconstant' BINARY_POWER and just before that will be a `LOAD_ATTR` instruction corresponding to `self.foo`. Seeing that it's in the same position as the original instruction lets us know we've found our match. Yes - if it identifies a node, you can trust that it's identified the correct one. The tests are very thorough - in addition to unit tests which check various situations directly, there are property tests against a large number of files (see the filenames printed in [this build](https://travis-ci.org/alexmojaki/executing/jobs/557970457)) with real code. Specifically, for each file, the tests: 1. Identify as many nodes as possible from all the bytecode instructions in the file, and assert that they are all distinct 2. Find all the nodes that should be identifiable, and assert that they were indeed identified somewhere In other words, it shows that there is a one-to-one mapping between the nodes and the instructions that can be handled. This leaves very little room for a bug to creep in. Furthermore, `executing` checks that the instructions compiled from the modified AST exactly match the original code save for a few small known exceptions. This accounts for all the quirks and optimisations in the interpreter. Currently it works in almost all cases for the following `ast` nodes: - `Call`, e.g. `self.foo(bar)` - `Attribute`, e.g. `point.x` - `Subscript`, e.g. `lst[1]` - `BinOp`, e.g. `x + y` (doesn't include `and` and `or`) - `UnaryOp`, e.g. `-n` (includes `not` but only works sometimes) - `Compare` e.g. `a < b` (not for chains such as `0 < p < 1`) The plan is to extend to more operations in the future. - **[`stack_data`](https://github.com/alexmojaki/stack_data)**: Extracts data from stack frames and tracebacks, particularly to display more useful tracebacks than the default. Also uses another related library of mine: **[`pure_eval`](https://github.com/alexmojaki/pure_eval)**. - **[`futurecoder`](https://futurecoder.io/)**: Highlights the executing node in tracebacks using `executing` via `stack_data`, and provides debugging with `snoop`. - **[`snoop`](https://github.com/alexmojaki/snoop)**: A feature-rich and convenient debugging library. Uses `executing` to show the operation which caused an exception and to allow the `pp` function to display the source of its arguments. - **[`heartrate`](https://github.com/alexmojaki/heartrate)**: A simple real time visualisation of the execution of a Python program. Uses `executing` to highlight currently executing operations, particularly in each frame of the stack trace. - **[`sorcery`](https://github.com/alexmojaki/sorcery)**: Dark magic delights in Python. Uses `executing` to let special callables called spells know where they're being called from. - **[`IPython`](https://github.com/ipython/ipython/pull/12150)**: Highlights the executing node in tracebacks using `executing` via [`stack_data`](https://github.com/alexmojaki/stack_data). - **[`icecream`](https://github.com/gruns/icecream)**: 🍦 Sweet and creamy print debugging. Uses `executing` to identify where `ic` is called and print its arguments. - **[`friendly_traceback`](https://github.com/friendly-traceback/friendly-traceback)**: Uses `stack_data` and `executing` to pinpoint the cause of errors and provide helpful explanations. - **[`python-devtools`](https://github.com/samuelcolvin/python-devtools)**: Uses `executing` for print debugging similar to `icecream`. - **[`sentry_sdk`](https://github.com/getsentry/sentry-python)**: Add the integration `sentry_sdk.integrations.executingExecutingIntegration()` to show the function `__qualname__` in each frame in sentry events. - **[`varname`](https://github.com/pwwang/python-varname)**: Dark magics about variable names in python. Uses `executing` to find where its various magical functions like `varname` and `nameof` are called from. https://github.com/alexmojaki/executing python3-executing noarch 3465d8d6d87a3267ef00c3eba63cbd60b44967cfbc7993befc40f2162bc08da3 Get the currently executing AST node of a frame, and other information [![Build Status](https://github.com/alexmojaki/executing/workflows/Tests/badge.svg?branch=master)](https://github.com/alexmojaki/executing/actions) [![Coverage Status](https://coveralls.io/repos/github/alexmojaki/executing/badge.svg?branch=master)](https://coveralls.io/github/alexmojaki/executing?branch=master) [![Supports Python versions 2.7 and 3.5+, including PyPy](https://img.shields.io/pypi/pyversions/executing.svg)](https://pypi.python.org/pypi/executing) This mini-package lets you get information about what a frame is currently doing, particularly the AST node being executed. * [Usage](#usage) * [Getting the AST node](#getting-the-ast-node) * [Getting the source code of the node](#getting-the-source-code-of-the-node) * [Getting the `__qualname__` of the current function](#getting-the-__qualname__-of-the-current-function) * [The Source class](#the-source-class) * [Installation](#installation) * [How does it work?](#how-does-it-work) * [Is it reliable?](#is-it-reliable) * [Which nodes can it identify?](#which-nodes-can-it-identify) * [Libraries that use this](#libraries-that-use-this) ```python import executing node = executing.Source.executing(frame).node ``` Then `node` will be an AST node (from the `ast` standard library module) or None if the node couldn't be identified (which may happen often and should always be checked). `node` will always be the same instance for multiple calls with frames at the same point of execution. If you have a traceback object, pass it directly to `Source.executing()` rather than the `tb_frame` attribute to get the correct node. For this you will need to separately install the [`asttokens`](https://github.com/gristlabs/asttokens) library, then obtain an `ASTTokens` object: ```python executing.Source.executing(frame).source.asttokens() ``` or: ```python executing.Source.for_frame(frame).asttokens() ``` or use one of the convenience methods: ```python executing.Source.executing(frame).text() executing.Source.executing(frame).text_range() ``` ```python executing.Source.executing(frame).code_qualname() ``` or: ```python executing.Source.for_frame(frame).code_qualname(frame.f_code) ``` Everything goes through the `Source` class. Only one instance of the class is created for each filename. Subclassing it to add more attributes on creation or methods is recommended. The classmethods such as `executing` will respect this. See the source code and docstrings for more detail. pip install executing If you don't like that you can just copy the file `executing.py`, there are no dependencies (but of course you won't get updates). Suppose the frame is executing this line: ```python self.foo(bar.x) ``` and in particular it's currently obtaining the attribute `self.foo`. Looking at the bytecode, specifically `frame.f_code.co_code[frame.f_lasti]`, we can tell that it's loading an attribute, but it's not obvious which one. We can narrow down the statement being executed using `frame.f_lineno` and find the two `ast.Attribute` nodes representing `self.foo` and `bar.x`. How do we find out which one it is, without recreating the entire compiler in Python? The trick is to modify the AST slightly for each candidate expression and observe the changes in the bytecode instructions. We change the AST to this: ```python (self.foo ** 'longuniqueconstant')(bar.x) ``` and compile it, and the bytecode will be almost the same but there will be two new instructions: LOAD_CONST 'longuniqueconstant' BINARY_POWER and just before that will be a `LOAD_ATTR` instruction corresponding to `self.foo`. Seeing that it's in the same position as the original instruction lets us know we've found our match. Yes - if it identifies a node, you can trust that it's identified the correct one. The tests are very thorough - in addition to unit tests which check various situations directly, there are property tests against a large number of files (see the filenames printed in [this build](https://travis-ci.org/alexmojaki/executing/jobs/557970457)) with real code. Specifically, for each file, the tests: 1. Identify as many nodes as possible from all the bytecode instructions in the file, and assert that they are all distinct 2. Find all the nodes that should be identifiable, and assert that they were indeed identified somewhere In other words, it shows that there is a one-to-one mapping between the nodes and the instructions that can be handled. This leaves very little room for a bug to creep in. Furthermore, `executing` checks that the instructions compiled from the modified AST exactly match the original code save for a few small known exceptions. This accounts for all the quirks and optimisations in the interpreter. Currently it works in almost all cases for the following `ast` nodes: - `Call`, e.g. `self.foo(bar)` - `Attribute`, e.g. `point.x` - `Subscript`, e.g. `lst[1]` - `BinOp`, e.g. `x + y` (doesn't include `and` and `or`) - `UnaryOp`, e.g. `-n` (includes `not` but only works sometimes) - `Compare` e.g. `a < b` (not for chains such as `0 < p < 1`) The plan is to extend to more operations in the future. - **[`stack_data`](https://github.com/alexmojaki/stack_data)**: Extracts data from stack frames and tracebacks, particularly to display more useful tracebacks than the default. Also uses another related library of mine: **[`pure_eval`](https://github.com/alexmojaki/pure_eval)**. - **[`futurecoder`](https://futurecoder.io/)**: Highlights the executing node in tracebacks using `executing` via `stack_data`, and provides debugging with `snoop`. - **[`snoop`](https://github.com/alexmojaki/snoop)**: A feature-rich and convenient debugging library. Uses `executing` to show the operation which caused an exception and to allow the `pp` function to display the source of its arguments. - **[`heartrate`](https://github.com/alexmojaki/heartrate)**: A simple real time visualisation of the execution of a Python program. Uses `executing` to highlight currently executing operations, particularly in each frame of the stack trace. - **[`sorcery`](https://github.com/alexmojaki/sorcery)**: Dark magic delights in Python. Uses `executing` to let special callables called spells know where they're being called from. - **[`IPython`](https://github.com/ipython/ipython/pull/12150)**: Highlights the executing node in tracebacks using `executing` via [`stack_data`](https://github.com/alexmojaki/stack_data). - **[`icecream`](https://github.com/gruns/icecream)**: 🍦 Sweet and creamy print debugging. Uses `executing` to identify where `ic` is called and print its arguments. - **[`friendly_traceback`](https://github.com/friendly-traceback/friendly-traceback)**: Uses `stack_data` and `executing` to pinpoint the cause of errors and provide helpful explanations. - **[`python-devtools`](https://github.com/samuelcolvin/python-devtools)**: Uses `executing` for print debugging similar to `icecream`. - **[`sentry_sdk`](https://github.com/getsentry/sentry-python)**: Add the integration `sentry_sdk.integrations.executingExecutingIntegration()` to show the function `__qualname__` in each frame in sentry events. - **[`varname`](https://github.com/pwwang/python-varname)**: Dark magics about variable names in python. Uses `executing` to find where its various magical functions like `varname` and `nameof` are called from. https://github.com/alexmojaki/executing python3-flask-whooshee noarch 021b4c5bdfbbfeb7707ed65f33034d3678d2df8d767829cbe5ed491afb7bd6c5 Flask-SQLAlchemy - Whoosh Integration Customizable Flask - SQLAlchemy - Whoosh integration https://github.com/bkabrda/flask-whooshee python3-html2text noarch fa188c27a7ef97e6d9db5104452fe0d60d51853be91d5ed6773dd7fb31161b04 Turn HTML into equivalent Markdown-structured text. Convert HTML to Markdown-formatted text. https://github.com/Alir3z4/html2text/ python3-html5-parser aarch64 aba4f17f750fe164e9c864d6244cafbfa83342d59cffbd849bad92471d681599 A fast, standards compliant, C based, HTML 5 parser for python A fast, standards compliant, C based, HTML 5 parser for python https://pypi.python.org/pypi/html5-parser python3-html5-parser aarch64 786f0cf86486b610ad768c657d987f5db3a322608e60da6b53fe17fefdbd6892 A fast, standards compliant, C based, HTML 5 parser for python A fast, standards compliant, C based, HTML 5 parser for python https://pypi.python.org/pypi/html5-parser python3-ipdb noarch 2644db020e24c622231a448b40042cab35c238acb60b450a6bc2d735b84b836a IPython-enabled pdb https://github.com/gotcha/ipdb python3-ipython noarch 6c096c2d8639410c1b3cebe2575d78487bc5449287aae4dcd03e855169379c89 IPython: Productive Interactive Computing IPython provides a rich toolkit to help you make the most out of using Python interactively. Its main components are: * A powerful interactive Python shell * A `Jupyter <https://jupyter.org/>`_ kernel to work with Python code in Jupyter notebooks and other interactive frontends. The enhanced interactive Python shells have the following main features: * Comprehensive object introspection. * Input history, persistent across sessions. * Caching of output results during a session with automatically generated references. * Extensible tab completion, with support by default for completion of python variables and keywords, filenames and function keywords. * Extensible system of 'magic' commands for controlling the environment and performing many tasks related either to IPython or the operating system. * A rich configuration system with easy switching between different setups (simpler than changing $PYTHONSTARTUP environment variables every time). * Session logging and reloading. * Extensible syntax processing for special purpose situations. * Access to the system shell with user-extensible alias system. * Easily embeddable in other Python programs and GUIs. * Integrated access to the pdb debugger and the Python profiler. The latest development version is always available from IPython's `GitHub site <http://github.com/ipython>`_. https://ipython.org python3-ipython noarch c1b8e6ecab4ec3404127bd59b0cdbf33075e05cd3374470a77d6811180da146a IPython: Productive Interactive Computing IPython provides a rich toolkit to help you make the most out of using Python interactively. Its main components are: * A powerful interactive Python shell * A `Jupyter <https://jupyter.org/>`_ kernel to work with Python code in Jupyter notebooks and other interactive frontends. The enhanced interactive Python shells have the following main features: * Comprehensive object introspection. * Input history, persistent across sessions. * Caching of output results during a session with automatically generated references. * Extensible tab completion, with support by default for completion of python variables and keywords, filenames and function keywords. * Extensible system of 'magic' commands for controlling the environment and performing many tasks related either to IPython or the operating system. * A rich configuration system with easy switching between different setups (simpler than changing $PYTHONSTARTUP environment variables every time). * Session logging and reloading. * Extensible syntax processing for special purpose situations. * Access to the system shell with user-extensible alias system. * Easily embeddable in other Python programs and GUIs. * Integrated access to the pdb debugger and the Python profiler. The latest development version is always available from IPython's `GitHub site <http://github.com/ipython>`_. https://ipython.org python3-jedi noarch 71c074ecce8fed034722a532af47777af9bc80fd85a4d9cc0fa9e3ddb900b8a8 A static analysis tool for Python that is typically used in IDEs/editors plugins Jedi is a static analysis tool for Python that is typically used in IDEs/editors plugins. It has a focus on autocompletion and goto functionality. Other features include refactoring, code search and finding references. https://github.com/davidhalter/jedi python3-jedi noarch 498d33664b4597db56756c40eaff3346e509d8f85f13050ed0cec460522cc548 A static analysis tool for Python that is typically used in IDEs/editors plugins Jedi is a static analysis tool for Python that is typically used in IDEs/editors plugins. It has a focus on autocompletion and goto functionality. Other features include refactoring, code search and finding references. https://github.com/davidhalter/jedi python3-koji noarch 692afea3b1a7113955853183ac63ad5aa3ff212268efdf0b84ec9f4f31a3a6c4 Build system tools python library Koji is a system for building and tracking RPMS. This subpackage provides python functions and libraries. https://pagure.io/koji/ python3-koji noarch 548917f43982b3d82a276e2bf8f0298bb82047918375761c92bca0124f2518c0 Build system tools python library Koji is a system for building and tracking RPMS. This subpackage provides python functions and libraries. https://pagure.io/koji/ python3-littleutils noarch fffb82ef874b2bced96ebdf5986ae86974c3f15c8d9116bbba74eaac690220a1 Small collection of Python utilities Small collection of Python utilities. https://pypi.org/pypi/littleutils python3-matplotlib-inline noarch 46298bec11a6b71e16f8a61fc1f2c11b6721cadc59f6934011eb88adcc951161 Inline Matplotlib backend for Jupyter This package provides support for matplotlib to display figures directly inline in the Jupyter notebook and related clients, as shown below. With conda: ```bash conda install -c conda-forge matplotlib-inline ``` With pip: ```bash pip install matplotlib-inline ``` Note that in current versions of JupyterLab and Jupyter Notebook, the explicit use of the `%matplotlib inline` directive is not needed anymore, though other third-party clients may still require it. This will produce a figure immediately below: ```python %matplotlib inline import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 3*np.pi, 500) plt.plot(x, np.sin(x**2)) plt.title('A simple chirp'); ``` Licensed under the terms of the BSD 3-Clause License, by the IPython Development Team (see `LICENSE` file). BSD 3-Clause License Copyright (c) 2019-2022, IPython Development Team. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. https://github.com/ipython/matplotlib-inline python3-openid noarch dc258f8b7f39e6d6e4ae3aa40056efae574b69b7f71f4eb95a50edbcf502de87 OpenID support for modern servers and consumers. This is a set of Python packages to support use of the OpenID decentralized identity system in your application, update to Python 3. Want to enable single sign-on for your web site? Use the openid.consumer package. Want to run your own OpenID server? Check out openid.server. Includes example code and support for a variety of storage back-ends. http://github.com/necaris/python3-openid python3-openid noarch fc939feddb826f8ab59c0b4daccd1335c7d463e76184bf627a2cf3318143bbe0 OpenID support for modern servers and consumers. This is a set of Python packages to support use of the OpenID decentralized identity system in your application, update to Python 3. Want to enable single sign-on for your web site? Use the openid.consumer package. Want to run your own OpenID server? Check out openid.server. Includes example code and support for a variety of storage back-ends. http://github.com/necaris/python3-openid python3-openid src 344d344b563a3b1dfc73ffce57742105e24735c94b288a6452bf935ad1ecfdaf OpenID support for modern servers and consumers. This is a set of Python packages to support use of the OpenID decentralized identity system in your application, update to Python 3. Want to enable single sign-on for your web site? Use the openid.consumer package. Want to run your own OpenID server? Check out openid.server. Includes example code and support for a variety of storage back-ends. http://github.com/necaris/python3-openid python3-openid src 4436f7a1bcb229d192b5e76d6e471c2be385bc98e0b3e882e039930fc83c4b88 OpenID support for modern servers and consumers. This is a set of Python packages to support use of the OpenID decentralized identity system in your application, update to Python 3. Want to enable single sign-on for your web site? Use the openid.consumer package. Want to run your own OpenID server? Check out openid.server. Includes example code and support for a variety of storage back-ends. http://github.com/necaris/python3-openid python3-openid-help noarch 32387bf5774d485adeb531e7de9b489dc413168e249e55f7ae362270b6e6b00f Development documents and examples for python3-openid This is a set of Python packages to support use of the OpenID decentralized identity system in your application, update to Python 3. Want to enable single sign-on for your web site? Use the openid.consumer package. Want to run your own OpenID server? Check out openid.server. Includes example code and support for a variety of storage back-ends. http://github.com/necaris/python3-openid python3-openid-help noarch 36a4f74006674fce63829b4ff4d6e2bbf8df5808dd7ee556098c538eeef1bf0b Development documents and examples for python3-openid This is a set of Python packages to support use of the OpenID decentralized identity system in your application, update to Python 3. Want to enable single sign-on for your web site? Use the openid.consumer package. Want to run your own OpenID server? Check out openid.server. Includes example code and support for a variety of storage back-ends. http://github.com/necaris/python3-openid python3-openid-teams noarch d6490ddec285658967bfd18cbb8360c0e600a23913ec8399529aef9f81394f10 This is an implementation of the OpenID teams extension for python-openid UNKNOWN http://www.github.com/puiterwijk/python-openid-teams/ python3-openid-teams noarch 5845631bef14c3923c8c729f4691e48d6e3cdd005591fc4b70c3173668610c9a This is an implementation of the OpenID teams extension for python-openid UNKNOWN http://www.github.com/puiterwijk/python-openid-teams/ python3-openidc-client noarch 28070827d9f2b14a44c15ed1796baaca8720fe6fddd9ed040e286da4a876c643 Python OpenID Connect client with token caching and management Python OpenID Connect client with token caching and management. python3-os-service-types noarch ed8dcbc02d083e5f334db954b2ec54bdccd203fee32197e56530fc373aa8f48c Python library for consuming OpenStack sevice-types-authority data https://pypi.org/project/os-service-types/ python3-oslo-concurrency noarch 70ea0ad7d6399b430001efa324b9fde670416f600599ba34d5fdb45c5c2d3004 Oslo Concurrency library OpenStack library for all concurrency-related code https://docs.openstack.org/oslo.concurrency/latest/ python3-oslo-config noarch 2e9da86eb322122c9faa78639b9c5a048ea252aed44d7123e3652dc756d3ad3b Oslo Configuration API The Oslo configuration API supports parsing command line arguments and .ini style configuration files. https://docs.openstack.org/oslo.config/latest/ python3-oslo-i18n noarch 223695b7dae295624dc8099e102ae4e98e6c64e56ee99eac27b44817760fae0e Oslo i18n library Internationalization and translation library https://docs.openstack.org/oslo.i18n/latest python3-oslo-serialization noarch 1ca47a4b8ba70ce89f6486d002c630213119adec09d1e12973760296b3c05198 Oslo Serialization library The oslo.serialization library provides support for representing objects in transmittable and storable formats, such as Base64, JSON and MessagePack. https://docs.openstack.org/oslo.serialization/latest/ python3-oslo-utils noarch c78efc26f0c56b4d2d724d38a0d3f9f484dedc6af9fcc76ab9df4023736154ff Oslo Utility library The oslo.utils library provides support for common utility type functions, such as encoding, exception handling, string manipulation, and time handling. https://docs.openstack.org/oslo.utils/latest/ python3-parso noarch 89a6cf190736c2209bde4ca07de1bdd060c25b25e502230085f339af87e71a8f A Python Parser Parso is a Python parser that supports error recovery and round-trip parsing for different Python versions. Parso consists of a small API to parse Python and analyse the syntax tree. https://github.com/davidhalter/parso python3-parso noarch ac97975e1997f9962b59829d2deba189f3bc8be65ccd2e1da37e4de41a8f5834 A Python Parser Parso is a Python parser that supports error recovery and round-trip parsing for different Python versions. Parso consists of a small API to parse Python and analyse the syntax tree. https://github.com/davidhalter/parso python3-pickleshare noarch 0d8184d45457a23a9859dc201b6379692542bb748030fafb3c6a98574254a1c7 Tiny 'shelve'-like database with concurrency support PickleShare - a small 'shelve' like datastore with concurrency support Like shelve, a PickleShareDB object acts like a normal dictionary. Unlike shelve, many processes can access the database simultaneously. Changing a value in database is immediately visible to other processes accessing the same database. Concurrency is possible because the values are stored in separate files. Hence the "database" is a directory where *all* files are governed by PickleShare. Example usage:: from pickleshare import * db = PickleShareDB('~/testpickleshare') db.clear() print("Should be empty:",db.items()) db['hello'] = 15 db['aku ankka'] = [1,2,313] db['paths/are/ok/key'] = [1,(5,46)] print(db.keys()) This module is certainly not ZODB, but can be used for low-load (non-mission-critical) situations where tiny code size trumps the advanced features of a "real" object database. Installation guide: pip install pickleshare https://github.com/pickleshare/pickleshare python3-prompt-toolkit noarch 3599fc2e23b2092055ce6fa03b1234f6417150850b1b3b97d5a08a2920742403 Library for building powerful interactive command lines in Python prompt_toolkit is a library for building powerful interactive command lines and terminal applications in Python. https://github.com/prompt-toolkit/python-prompt-toolkit python3-prompt-toolkit noarch 9c0c9636d0980c0ea9806c11db5a74baf1b09321b7843a72f52a795bfce688f3 Library for building powerful interactive command lines in Python prompt_toolkit is a library for building powerful interactive command lines and terminal applications in Python. https://github.com/prompt-toolkit/python-prompt-toolkit python3-pure-eval noarch a4f4109c89e73bd222196905f811a1128f457425d1604018ce16b8756f2f3de0 Safely evaluate AST nodes without side effects [![Build Status](https://travis-ci.org/alexmojaki/pure_eval.svg?branch=master)](https://travis-ci.org/alexmojaki/pure_eval) [![Coverage Status](https://coveralls.io/repos/github/alexmojaki/pure_eval/badge.svg?branch=master)](https://coveralls.io/github/alexmojaki/pure_eval?branch=master) [![Supports Python versions 3.5+](https://img.shields.io/pypi/pyversions/pure_eval.svg)](https://pypi.python.org/pypi/pure_eval) This is a Python package that lets you safely evaluate certain AST nodes without triggering arbitrary code that may have unwanted side effects. It can be installed from PyPI: pip install pure_eval To demonstrate usage, suppose we have an object defined as follows: ```python class Rectangle: def __init__(self, width, height): self.width = width self.height = height @property def area(self): print("Calculating area...") return self.width * self.height rect = Rectangle(3, 5) ``` Given the `rect` object, we want to evaluate whatever expressions we can in this source code: ```python source = "(rect.width, rect.height, rect.area)" ``` This library works with the AST, so let's parse the source code and peek inside: ```python import ast tree = ast.parse(source) the_tuple = tree.body[0].value for node in the_tuple.elts: print(ast.dump(node)) ``` Output: ```python Attribute(value=Name(id='rect', ctx=Load()), attr='width', ctx=Load()) Attribute(value=Name(id='rect', ctx=Load()), attr='height', ctx=Load()) Attribute(value=Name(id='rect', ctx=Load()), attr='area', ctx=Load()) ``` Now to actually use the library. First construct an Evaluator: ```python from pure_eval import Evaluator evaluator = Evaluator({"rect": rect}) ``` The argument to `Evaluator` should be a mapping from variable names to their values. Or if you have access to the stack frame where `rect` is defined, you can instead use: ```python evaluator = Evaluator.from_frame(frame) ``` Now to evaluate some nodes, using `evaluator[node]`: ```python print("rect.width:", evaluator[the_tuple.elts[0]]) print("rect:", evaluator[the_tuple.elts[0].value]) ``` Output: ``` rect.width: 3 rect: <__main__.Rectangle object at 0x105b0dd30> ``` OK, but you could have done the same thing with `eval`. The useful part is that it will refuse to evaluate the property `rect.area` because that would trigger unknown code. If we try, it'll raise a `CannotEval` exception. ```python from pure_eval import CannotEval try: print("rect.area:", evaluator[the_tuple.elts[2]]) # fails except CannotEval as e: print(e) # prints CannotEval ``` To find all the expressions that can be evaluated in a tree: ```python for node, value in evaluator.find_expressions(tree): print(ast.dump(node), value) ``` Output: ```python Attribute(value=Name(id='rect', ctx=Load()), attr='width', ctx=Load()) 3 Attribute(value=Name(id='rect', ctx=Load()), attr='height', ctx=Load()) 5 Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> ``` Note that this includes `rect` three times, once for each appearance in the source code. Since all these nodes are equivalent, we can group them together: ```python from pure_eval import group_expressions for nodes, values in group_expressions(evaluator.find_expressions(tree)): print(len(nodes), "nodes with value:", values) ``` Output: ``` 1 nodes with value: 3 1 nodes with value: 5 3 nodes with value: <__main__.Rectangle object at 0x10d374d30> ``` If we want to list all the expressions in a tree, we may want to filter out certain expressions whose values are obvious. For example, suppose we have a function `foo`: ```python def foo(): pass ``` If we refer to `foo` by its name as usual, then that's not interesting: ```python from pure_eval import is_expression_interesting node = ast.parse('foo').body[0].value print(ast.dump(node)) print(is_expression_interesting(node, foo)) ``` Output: ```python Name(id='foo', ctx=Load()) False ``` But if we refer to it by a different name, then it's interesting: ```python node = ast.parse('bar').body[0].value print(ast.dump(node)) print(is_expression_interesting(node, foo)) ``` Output: ```python Name(id='bar', ctx=Load()) True ``` In general `is_expression_interesting` returns False for the following values: - Literals (e.g. `123`, `'abc'`, `[1, 2, 3]`, `{'a': (), 'b': ([1, 2], [3])}`) - Variables or attributes whose name is equal to the value's `__name__`, such as `foo` above or `self.foo` if it was a method. - Builtins (e.g. `len`) referred to by their usual name. To make things easier, you can combine finding expressions, grouping them, and filtering out the obvious ones with: ```python evaluator.interesting_expressions_grouped(root) ``` To get the source code of an AST node, I recommend [asttokens](https://github.com/gristlabs/asttokens). Here's a complete example that brings it all together: ```python from asttokens import ASTTokens from pure_eval import Evaluator source = """ x = 1 d = {x: 2} y = d[x] """ names = {} exec(source, names) atok = ASTTokens(source, parse=True) for nodes, value in Evaluator(names).interesting_expressions_grouped(atok.tree): print(atok.get_text(nodes[0]), "=", value) ``` Output: ```python x = 1 d = {1: 2} y = 2 d[x] = 2 ``` http://github.com/alexmojaki/pure_eval python3-pure-eval noarch 35d18a606fc72a57fa695fe1e72765e4e98f031b079a6eefdac06703c24d976e Safely evaluate AST nodes without side effects [![Build Status](https://travis-ci.org/alexmojaki/pure_eval.svg?branch=master)](https://travis-ci.org/alexmojaki/pure_eval) [![Coverage Status](https://coveralls.io/repos/github/alexmojaki/pure_eval/badge.svg?branch=master)](https://coveralls.io/github/alexmojaki/pure_eval?branch=master) [![Supports Python versions 3.5+](https://img.shields.io/pypi/pyversions/pure_eval.svg)](https://pypi.python.org/pypi/pure_eval) This is a Python package that lets you safely evaluate certain AST nodes without triggering arbitrary code that may have unwanted side effects. It can be installed from PyPI: pip install pure_eval To demonstrate usage, suppose we have an object defined as follows: ```python class Rectangle: def __init__(self, width, height): self.width = width self.height = height @property def area(self): print("Calculating area...") return self.width * self.height rect = Rectangle(3, 5) ``` Given the `rect` object, we want to evaluate whatever expressions we can in this source code: ```python source = "(rect.width, rect.height, rect.area)" ``` This library works with the AST, so let's parse the source code and peek inside: ```python import ast tree = ast.parse(source) the_tuple = tree.body[0].value for node in the_tuple.elts: print(ast.dump(node)) ``` Output: ```python Attribute(value=Name(id='rect', ctx=Load()), attr='width', ctx=Load()) Attribute(value=Name(id='rect', ctx=Load()), attr='height', ctx=Load()) Attribute(value=Name(id='rect', ctx=Load()), attr='area', ctx=Load()) ``` Now to actually use the library. First construct an Evaluator: ```python from pure_eval import Evaluator evaluator = Evaluator({"rect": rect}) ``` The argument to `Evaluator` should be a mapping from variable names to their values. Or if you have access to the stack frame where `rect` is defined, you can instead use: ```python evaluator = Evaluator.from_frame(frame) ``` Now to evaluate some nodes, using `evaluator[node]`: ```python print("rect.width:", evaluator[the_tuple.elts[0]]) print("rect:", evaluator[the_tuple.elts[0].value]) ``` Output: ``` rect.width: 3 rect: <__main__.Rectangle object at 0x105b0dd30> ``` OK, but you could have done the same thing with `eval`. The useful part is that it will refuse to evaluate the property `rect.area` because that would trigger unknown code. If we try, it'll raise a `CannotEval` exception. ```python from pure_eval import CannotEval try: print("rect.area:", evaluator[the_tuple.elts[2]]) # fails except CannotEval as e: print(e) # prints CannotEval ``` To find all the expressions that can be evaluated in a tree: ```python for node, value in evaluator.find_expressions(tree): print(ast.dump(node), value) ``` Output: ```python Attribute(value=Name(id='rect', ctx=Load()), attr='width', ctx=Load()) 3 Attribute(value=Name(id='rect', ctx=Load()), attr='height', ctx=Load()) 5 Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> Name(id='rect', ctx=Load()) <__main__.Rectangle object at 0x105568d30> ``` Note that this includes `rect` three times, once for each appearance in the source code. Since all these nodes are equivalent, we can group them together: ```python from pure_eval import group_expressions for nodes, values in group_expressions(evaluator.find_expressions(tree)): print(len(nodes), "nodes with value:", values) ``` Output: ``` 1 nodes with value: 3 1 nodes with value: 5 3 nodes with value: <__main__.Rectangle object at 0x10d374d30> ``` If we want to list all the expressions in a tree, we may want to filter out certain expressions whose values are obvious. For example, suppose we have a function `foo`: ```python def foo(): pass ``` If we refer to `foo` by its name as usual, then that's not interesting: ```python from pure_eval import is_expression_interesting node = ast.parse('foo').body[0].value print(ast.dump(node)) print(is_expression_interesting(node, foo)) ``` Output: ```python Name(id='foo', ctx=Load()) False ``` But if we refer to it by a different name, then it's interesting: ```python node = ast.parse('bar').body[0].value print(ast.dump(node)) print(is_expression_interesting(node, foo)) ``` Output: ```python Name(id='bar', ctx=Load()) True ``` In general `is_expression_interesting` returns False for the following values: - Literals (e.g. `123`, `'abc'`, `[1, 2, 3]`, `{'a': (), 'b': ([1, 2], [3])}`) - Variables or attributes whose name is equal to the value's `__name__`, such as `foo` above or `self.foo` if it was a method. - Builtins (e.g. `len`) referred to by their usual name. To make things easier, you can combine finding expressions, grouping them, and filtering out the obvious ones with: ```python evaluator.interesting_expressions_grouped(root) ``` To get the source code of an AST node, I recommend [asttokens](https://github.com/gristlabs/asttokens). Here's a complete example that brings it all together: ```python from asttokens import ASTTokens from pure_eval import Evaluator source = """ x = 1 d = {x: 2} y = d[x] """ names = {} exec(source, names) atok = ASTTokens(source, parse=True) for nodes, value in Evaluator(names).interesting_expressions_grouped(atok.tree): print(atok.get_text(nodes[0]), "=", value) ``` Output: ```python x = 1 d = {1: 2} y = 2 d[x] = 2 ``` http://github.com/alexmojaki/pure_eval python3-py3dns noarch 52c1034de0240b63d2af549973b1f8611c3657a9dbbd71b1001a747375388d0b Python 3 DNS library Python 3 DNS library: https://launchpad.net/py3dns python3-py3dns noarch 65e520a71e5b5679e2c175b71fc33a73186c776d66cf602c78992d1b55665343 Python 3 DNS library Python 3 DNS library: https://launchpad.net/py3dns python3-pyLibravatar noarch 85e4db7b51c6bdf10adc6e81bb25a0017160c509f112c9b3e9cbb0136211a28c Python module for Libravatar PyLibravatar is an easy way to make use of the federated Libravatar_ avatar hosting service from within your Python applications. https://launchpad.net/pylibravatar python3-pyLibravatar noarch 0cbafc1ca0eed692ae6446550e5246046c53417a9152aa368fc3d17fbc9809c3 Python module for Libravatar PyLibravatar is an easy way to make use of the federated Libravatar_ avatar hosting service from within your Python applications. https://launchpad.net/pylibravatar python3-pyLibravatar noarch 5513cf427e2bac7c23658cd0f3542adec0265d5bf57e7699620dd23f42a36c7c Python module for Libravatar PyLibravatar is an easy way to make use of the federated Libravatar_ avatar hosting service from within your Python applications. https://launchpad.net/pylibravatar python3-pygal noarch e16758e59d1aaf00b95323a2140bafe9ab9e24b5e03d3b1141db13b33dd00ba2 A Python svg graph plotting library https://www.pygal.org/ python3-pygal noarch 57209ead2cfbdc135bcaa32cb9726fad0ce37e1ecaf61b29f32352c5a9461482 A Python svg graph plotting library https://www.pygal.org/ python3-pygit2 aarch64 14df02235f65cb139afbc3c8f18af01ed433e722ebd08d3361d064d265fb8f9a Python bindings for libgit2. - Documentation - http://www.pygit2.org/ - Install - http://www.pygit2.org/install.html - Download - https://pypi.python.org/pypi/pygit2 - Source code and issue tracker - https://github.com/libgit2/pygit2 - Changelog - https://github.com/libgit2/pygit2/blob/master/CHANGELOG.rst - Authors - https://github.com/libgit2/pygit2/blob/master/AUTHORS.rst https://github.com/libgit2/pygit2 python3-pygit2 aarch64 f6f1f70cb7fc72b971a27dd3138394a1db2a9741abaaeddbcfc84169c7379fed Python bindings for libgit2. - Documentation - http://www.pygit2.org/ - Install - http://www.pygit2.org/install.html - Download - https://pypi.python.org/pypi/pygit2 - Source code and issue tracker - https://github.com/libgit2/pygit2 - Changelog - https://github.com/libgit2/pygit2/blob/master/CHANGELOG.rst - Authors - https://github.com/libgit2/pygit2/blob/master/AUTHORS.rst https://github.com/libgit2/pygit2 python3-resalloc noarch 02dcc39e34e779ce0c557fb2d17757f1eb72a5d73655a9db7a859e76ac46a4f2 Resource allocator for expensive resources - Python 3 client library The resalloc project aims to help with taking care of dynamically allocated resources, for example ephemeral virtual machines used for the purposes of CI/CD tasks. The python3-resalloc package provides Python 3 client library for talking to the resalloc server. https://github.com/praiskup/resalloc python3-responses noarch 0d66538ccb96df9920cbf7d97aad6becb518ac4c2602b2de0739e3a0c2717b68 A utility library for mocking out the `requests` Python library. A utility library for mocking out the requests Python library. https://github.com/getsentry/responses python3-retask noarch 2d9f22e588aea478a87e2c3553b64778b94a3ee086138702518f06cc1bfe711e Python module to create and manage distributed task queues Python module to create and manage distributed task queues using redis. http://retask.readthedocs.org/en/latest/index.html python3-rich noarch d45c1c915045eab56dcc3b28aa2e0a06f98da4e4b04100b74e04048fa9efd77d Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal Rich is a Python library for rich text and beautiful formatting in the terminal https://github.com/willmcgugan/rich python3-rpkg noarch cb0ccdd5ba67094a266f1c513a7369f3c0c1c4e11cf414006d928cf48460fc8a Python library for interacting with rpm+git A python library for managing RPM package sources in a git repository. https://pagure.io/rpkg python3-rpkg noarch 40f382ffb97073bfb6b0ac9aa135ea2c3515aa3d5da7e2818cb29092a4087dd6 Python library for interacting with rpm+git A python library for managing RPM package sources in a git repository. https://pagure.io/rpkg python3-rpmautospec noarch 963aaaa82218d41df8e508c0f8237947597f02b1133d5f0350c7bc5b9295ccf8 Package and CLI tool to generate release fields and changelogs A package and CLI tool to generate RPM release fields and changelogs. https://pagure.io/fedora-infra/rpmautospec python3-rpmautospec noarch 0cb99176f5145c208bd76bdccbc4a791b23953632afdfe6f66a79851e5fdd22b Package and CLI tool to generate release fields and changelogs A package and CLI tool to generate RPM release fields and changelogs. https://pagure.io/fedora-infra/rpmautospec python3-stack-data noarch aab365cf75c6ee71fe04da62dd7172f5c983ae4a825da8892edde5dba58de3da Extract data from python stack frames and tracebacks for informative displays 6 | for i in range(5): 7 | row = [] 8 | result.append(row) --> 9 | print_stack() 10 | for j in range(5): ``` The code for `print_stack()` is fairly self-explanatory. If you want to learn more details about a particular class or method I suggest looking through some docstrings. `FrameInfo` is a class that accepts either a frame or a traceback object and provides a bunch of nice attributes and properties (which are cached so you don't need to worry about performance). In particular `frame_info.lines` is a list of `Line` objects. `line.render()` returns the source code of that line suitable for display. Without any arguments it simply strips any common leading indentation. Later on we'll see a more powerful use for it. You can see that `frame_info.lines` includes some lines of surrounding context. By default it includes 3 pieces of context before the main line and 1 piece after. We can configure the amount of context by passing options: ```python options = stack_data.Options(before=1, after=0) frame_info = stack_data.FrameInfo(frame, options) ``` Then the output looks like: ``` http://github.com/alexmojaki/stack_data python3-stack-data noarch 3a8407c5fc36f857c86da802448e909ab914f10ddc4a4f123cce107ff1c31c56 Extract data from python stack frames and tracebacks for informative displays 6 | for i in range(5): 7 | row = [] 8 | result.append(row) --> 9 | print_stack() 10 | for j in range(5): ``` The code for `print_stack()` is fairly self-explanatory. If you want to learn more details about a particular class or method I suggest looking through some docstrings. `FrameInfo` is a class that accepts either a frame or a traceback object and provides a bunch of nice attributes and properties (which are cached so you don't need to worry about performance). In particular `frame_info.lines` is a list of `Line` objects. `line.render()` returns the source code of that line suitable for display. Without any arguments it simply strips any common leading indentation. Later on we'll see a more powerful use for it. You can see that `frame_info.lines` includes some lines of surrounding context. By default it includes 3 pieces of context before the main line and 1 piece after. We can configure the amount of context by passing options: ```python options = stack_data.Options(before=1, after=0) frame_info = stack_data.FrameInfo(frame, options) ``` Then the output looks like: ``` http://github.com/alexmojaki/stack_data python3-templated-dictionary noarch 9c45fabb7873a7088f485ff3a1177607c1b1eb90e342fdbe7046ed92e3658693 Dictionary with Jinja2 expansion Dictionary where __getitem__() is run through Jinja2 template. https://github.com/xsuchy/templated-dictionary resalloc noarch b146ec8874203600bece39164700dd83c4fb689b689c7a1cda3039a529f158a1 Resource allocator for expensive resources - client tooling The resalloc project aims to help with taking care of dynamically allocated resources, for example ephemeral virtual machines used for the purposes of CI/CD tasks. The resalloc package provides the client-side tooling. https://github.com/praiskup/resalloc resalloc src cb201871fa508bbba26285898107a802f5e5ed10ab73aea3c54c4890905a5eae Resource allocator for expensive resources - client tooling The resalloc project aims to help with taking care of dynamically allocated resources, for example ephemeral virtual machines used for the purposes of CI/CD tasks. The resalloc package provides the client-side tooling. https://github.com/praiskup/resalloc resalloc-selinux noarch 0f12b01aa4dcaf0c244c34fa14915be207e70875dc371f5b9ebe5574e704fc2a SELinux module for resalloc The resalloc project aims to help with taking care of dynamically allocated resources, for example ephemeral virtual machines used for the purposes of CI/CD tasks. https://github.com/praiskup/resalloc resalloc-server noarch 0108e9dcb7c0adcab813295a51d3a31dad09a3c6af0878ac74480b01b5a21113 Resource allocator for expensive resources - server part The resalloc project aims to help with taking care of dynamically allocated resources, for example ephemeral virtual machines used for the purposes of CI/CD tasks. The resalloc-server package provides the resalloc server, and some tooling for resalloc administrators. https://github.com/praiskup/resalloc resalloc-webui noarch 26c7e3285ba216b3f351efda6b47056eb5fa0a18dd5f65343b30680755fb067d Resource allocator for expensive resources - webui part The resalloc project aims to help with taking care of dynamically allocated resources, for example ephemeral virtual machines used for the purposes of CI/CD tasks. The resalloc-webui package provides the resalloc webui, it shows page with information about resalloc resources. https://github.com/praiskup/resalloc rpkg src 4ef7fdc6a6db89b6846bd9147088b1d7583af1cf39d564bc2dcceed6a511a7f3 Python library for interacting with rpm+git Python library for interacting with rpm+git https://pagure.io/rpkg rpkg src f63fbdca85230f15f7d47a74e5b9e0cc643c7d19ae671f5d776559ed958673e0 Python library for interacting with rpm+git Python library for interacting with rpm+git https://pagure.io/rpkg rpkg src c8fa2d56a5f313465a1b051349dbb82881b7dd710b3c9fa7052eb5c33c6ef6fd Python library for interacting with rpm+git Python library for interacting with rpm+git https://pagure.io/rpkg rpkg src 92c3c427e083dcb5acfe4ad5ae191262f48bf3a5db07b9535ce05ce7257285af Python library for interacting with rpm+git Python library for interacting with rpm+git https://pagure.io/rpkg rpkg src 2f0a2f549905d3bee1e03aea55cdf4ec29a52653ba075b2b318042025011fb26 Python library for interacting with rpm+git Python library for interacting with rpm+git https://pagure.io/rpkg rpkg src 8132652132843b64d1ee3d984d3cb7cd731328dd81dd73003d853e8179328e3b Python library for interacting with rpm+git Python library for interacting with rpm+git https://pagure.io/rpkg rpkg src 2df99aa4138c3729d42e1b064db8cd3406c01c8e4da6a90f98aef9fa1b43165d Python library for interacting with rpm+git Python library for interacting with rpm+git https://pagure.io/rpkg rpkg noarch 907126fc459b44e174d166c11d76beb65234e7d86b6a2cdb69ad5b2a98f98868 RPM packaging utility This is an RPM packaging utility that can work with both DistGit and standard Git repositories and handles packed directory content as well as unpacked one. https://pagure.io/rpkg-util.git rpkg noarch aed40984964a52f5d54095d0a4e40832ad73d69bacab690b83ce511fc775483b RPM packaging utility This is an RPM packaging utility that can work with both DistGit and standard Git repositories and handles packed directory content as well as unpacked one. https://pagure.io/rpkg-util.git rpkg-common noarch a5c10f3b98694f2f87db8cbbe55afd9d00ee988fd5a6d342cf19abda565cb0c9 Common files for rpkg Common files for python2-rpkg and python3-rpkg. https://pagure.io/rpkg rpkg-common noarch 5c574d55a7abf43e264006f1fa4eb1084f98bc7ca5ea9a722693642ab529510c Common files for rpkg Common files for python2-rpkg and python3-rpkg. https://pagure.io/rpkg rpkg-macros noarch 94593466470216d3ac32b67842c53e26553395a7e939f99f2107774a83eb71a3 Set of preproc macros for rpkg utility Set of preproc macros to be used by rpkg utility. They are designed to dynamically generate certain parts of rpm spec files. You can use those macros also without rpkg by: $ cat <file_with_the_macros> | preproc -s /usr/lib/rpkg.macros.d/all.bash -e INPUT_PATH=<file_with_the_macros> INPUT_PATH env variable is passed to preproc to inform macros about the input file location. The variable is used to derive INPUT_DIR_PATH variable which rpkg macros use. If neither INPUT_PATH nor INPUT_DIR_PATH are specified, INPUT_PATH will stay empty and INPUT_DIR_PATH will default to '.' (the current working directory). Another option to experiment with the macros is to source /usr/lib/rpkg.macros.d/all.bash into your bash environment Then you can directly invoke the macros on your command-line as bash functions. See content in /usr/lib/rpkg.macros.d to discover available macros. Please, see man rpkg-macros for more information. https://pagure.io/rpkg-util.git rpkg-macros noarch 2b22fcdadaa11e4f0c32e77d6a91d917f5baf59fcd4592893697615c14e656c4 Set of preproc macros for rpkg utility Set of preproc macros to be used by rpkg utility. They are designed to dynamically generate certain parts of rpm spec files. You can use those macros also without rpkg by: $ cat <file_with_the_macros> | preproc -s /usr/lib/rpkg.macros.d/all.bash -e INPUT_PATH=<file_with_the_macros> INPUT_PATH env variable is passed to preproc to inform macros about the input file location. The variable is used to derive INPUT_DIR_PATH variable which rpkg macros use. If neither INPUT_PATH nor INPUT_DIR_PATH are specified, INPUT_PATH will stay empty and INPUT_DIR_PATH will default to '.' (the current working directory). Another option to experiment with the macros is to source /usr/lib/rpkg.macros.d/all.bash into your bash environment Then you can directly invoke the macros on your command-line as bash functions. See content in /usr/lib/rpkg.macros.d to discover available macros. Please, see man rpkg-macros for more information. https://pagure.io/rpkg-util.git rpkg-macros src 811635727af6e34aea460293b1190359e78aaf24b80fba732e8ad8e8c67ee6c7 Set of preproc macros for rpkg utility Set of preproc macros to be used by rpkg utility. They are designed to dynamically generate certain parts of rpm spec files. You can use those macros also without rpkg by: $ cat <file_with_the_macros> | preproc -s /usr/lib/rpkg.macros.d/all.bash -e INPUT_PATH=<file_with_the_macros> INPUT_PATH env variable is passed to preproc to inform macros about the input file location. The variable is used to derive INPUT_DIR_PATH variable which rpkg macros use. If neither INPUT_PATH nor INPUT_DIR_PATH are specified, INPUT_PATH will stay empty and INPUT_DIR_PATH will default to '.' (the current working directory). Another option to experiment with the macros is to source /usr/lib/rpkg.macros.d/all.bash into your bash environment Then you can directly invoke the macros on your command-line as bash functions. See content in /usr/lib/rpkg.macros.d to discover available macros. Please, see man rpkg-macros for more information. https://pagure.io/rpkg-util.git rpkg-macros src 512dd6d65383e3574042f6a9e92d2770f01e176ea9327cae73226cdf675f2fb1 Set of preproc macros for rpkg utility Set of preproc macros to be used by rpkg utility. They are designed to dynamically generate certain parts of rpm spec files. You can use those macros also without rpkg by: $ cat <file_with_the_macros> | preproc -s /usr/lib/rpkg.macros.d/all.bash -e INPUT_PATH=<file_with_the_macros> INPUT_PATH env variable is passed to preproc to inform macros about the input file location. The variable is used to derive INPUT_DIR_PATH variable which rpkg macros use. If neither INPUT_PATH nor INPUT_DIR_PATH are specified, INPUT_PATH will stay empty and INPUT_DIR_PATH will default to '.' (the current working directory). Another option to experiment with the macros is to source /usr/lib/rpkg.macros.d/all.bash into your bash environment Then you can directly invoke the macros on your command-line as bash functions. See content in /usr/lib/rpkg.macros.d to discover available macros. Please, see man rpkg-macros for more information. https://pagure.io/rpkg-util.git rpkg-util src 4fbc0e0a2bf86cc00800641841c8c2559699ba4069f97a5e0cad29ff93f4805f RPM packaging utility This package contains the rpkg utility. We are putting the actual 'rpkg' package into a subpackage because there already exists package https://src.fedoraproject.org/rpms/rpkg. That package, however, does not actually produce rpkg rpm whereas rpkg-util does. https://pagure.io/rpkg-util.git rpkg-util src 6b5f3aa82bcdfebf5cecaba242e5d50c9a97c1a9423bdf89dbcaca15177050ac RPM packaging utility This package contains the rpkg utility. We are putting the actual 'rpkg' package into a subpackage because there already exists package https://src.fedoraproject.org/rpms/rpkg. That package, however, does not actually produce rpkg rpm whereas rpkg-util does. https://pagure.io/rpkg-util.git rpkg-util src ee442dc9842c99c883b931bccecc1e1b59e9d07d2ab59fec3c359f4c6b0f321f RPM packaging utility This package contains the rpkg utility. We are putting the actual 'rpkg' package into a subpackage because there already exists package https://src.fedoraproject.org/rpms/rpkg. That package, however, does not actually produce rpkg rpm whereas rpkg-util does. https://pagure.io/rpkg-util.git rpm-git-tag-sort aarch64 8a7349590f144ada7f458e4d724638e5853da52932a9f0b5f6065f4f848b2931 Sorts merged git annotated tags according to topology and rpm version sorting. Sorts git annotated tags of Name-Version-Release form according to topology (primary criterion) and rpm version sorting (secondary criterion). Outputs only merged tags (i.e. those that reachable from the current HEAD). https://pagure.io/rpm-git-tag-sort rpm-git-tag-sort aarch64 340922a0d0fd865eef22f6e492e1fce8ac2c5e25de21b05ca58836c76db862b5 Sorts merged git annotated tags according to topology and rpm version sorting. Sorts git annotated tags of Name-Version-Release form according to topology (primary criterion) and rpm version sorting (secondary criterion). Outputs only merged tags (i.e. those that reachable from the current HEAD). https://pagure.io/rpm-git-tag-sort rpm-git-tag-sort aarch64 a443628ab4f63631724b6232fa05ab87014f19c4651ee4d1437490d46221c691 Sorts merged git annotated tags according to topology and rpm version sorting. Sorts git annotated tags of Name-Version-Release form according to topology (primary criterion) and rpm version sorting (secondary criterion). Outputs only merged tags (i.e. those that reachable from the current HEAD). https://pagure.io/rpm-git-tag-sort rpm-git-tag-sort src 4db6f063a0a0d7f6a1e2e5c9df65485cdaca67ef38ca1a5edbdb4ab09f009f9d Sorts merged git annotated tags according to topology and rpm version sorting. Sorts git annotated tags of Name-Version-Release form according to topology (primary criterion) and rpm version sorting (secondary criterion). Outputs only merged tags (i.e. those that reachable from the current HEAD). https://pagure.io/rpm-git-tag-sort rpm-git-tag-sort src 218308aaee85c5923e82fd9db87934d73eaec18636f1f4aef5dad84259bdce09 Sorts merged git annotated tags according to topology and rpm version sorting. Sorts git annotated tags of Name-Version-Release form according to topology (primary criterion) and rpm version sorting (secondary criterion). Outputs only merged tags (i.e. those that reachable from the current HEAD). https://pagure.io/rpm-git-tag-sort rpm-git-tag-sort src 2c72bdef70a6006c833ee81e1e727135730e2b51a86d302140a697ca0532cd1c Sorts merged git annotated tags according to topology and rpm version sorting. Sorts git annotated tags of Name-Version-Release form according to topology (primary criterion) and rpm version sorting (secondary criterion). Outputs only merged tags (i.e. those that reachable from the current HEAD). https://pagure.io/rpm-git-tag-sort rpm-git-tag-sort-debuginfo aarch64 f586ae3769e77e9dae8df307561f6884dcaaefffb2eb347327e9aee1e4f92ab4 Debug information for package rpm-git-tag-sort This package provides debug information for package rpm-git-tag-sort. Debug information is useful when developing applications that use this package or when debugging this package. https://pagure.io/rpm-git-tag-sort rpm-git-tag-sort-debuginfo aarch64 fa2b9aaa425fdd9f3a8a8af498942e676e96e149bdb913fd8742cbfdea33c94c Debug information for package rpm-git-tag-sort This package provides debug information for package rpm-git-tag-sort. Debug information is useful when developing applications that use this package or when debugging this package. https://pagure.io/rpm-git-tag-sort rpm-git-tag-sort-debuginfo aarch64 779efd9a2ca405bbebd761c766324206ef49a473ba3c0fa8cc140a14669c175e Debug information for package rpm-git-tag-sort This package provides debug information for package rpm-git-tag-sort. Debug information is useful when developing applications that use this package or when debugging this package. https://pagure.io/rpm-git-tag-sort rpm-git-tag-sort-debugsource aarch64 1f20f7141a283afeb5636c6b23a1dcbaca0f85715d2aab647779828317292828 Debug sources for package rpm-git-tag-sort This package provides debug sources for package rpm-git-tag-sort. Debug sources are useful when developing applications that use this package or when debugging this package. https://pagure.io/rpm-git-tag-sort rpm-git-tag-sort-debugsource aarch64 8656efdcd6101b8038302377b98902fec7fac021e2ff69a54ef1ac6cad5cc642 Debug sources for package rpm-git-tag-sort This package provides debug sources for package rpm-git-tag-sort. Debug sources are useful when developing applications that use this package or when debugging this package. https://pagure.io/rpm-git-tag-sort rpm-git-tag-sort-debugsource aarch64 d356cea0dee44a01d87f242d6889e77843e274f2c85b9096ef88074b72f205c3 Debug sources for package rpm-git-tag-sort This package provides debug sources for package rpm-git-tag-sort. Debug sources are useful when developing applications that use this package or when debugging this package. https://pagure.io/rpm-git-tag-sort rpmautospec noarch 155e6b7025b4c765ec95ec4c200b82860c8c9fa64b74488e16b8d552e5298fd2 CLI tool for generating RPM releases and changelogs CLI tool for generating RPM releases and changelogs https://pagure.io/fedora-infra/rpmautospec rpmautospec noarch 8b90e4f1bb17f11d3ddfcd5120d6bc53e8f41547ab17a42fc4f267f9b2c34831 CLI tool for generating RPM releases and changelogs CLI tool for generating RPM releases and changelogs https://pagure.io/fedora-infra/rpmautospec rpmautospec-rpm-macros noarch bb67b48c6b8d00e0573d1bd331ec31334e08d19e6b4b7456d08bfd07ea76d869 Rpmautospec RPM macros for local rpmbuild RPM macros with placeholders for building rpmautospec enabled packages localy https://pagure.io/fedora-infra/rpmautospec rpmautospec-rpm-macros noarch 0a981a30020bfe2e3ff34c1bcf8cd434efca175e595eab1a9812222894abbde0 Rpmautospec RPM macros for local rpmbuild RPM macros with placeholders for building rpmautospec enabled packages localy https://pagure.io/fedora-infra/rpmautospec tini aarch64 c33cf11741eb0b795976b78c9bce1f5caa74c233183c993061eeac2fb9bdb391 A tiny but valid init for containers Tini is the simplest init you could think of. All Tini does is spawn a single child (Tini is meant to be run in a container), and wait for it to exit all the while reaping zombies and performing signal forwarding. https://github.com/krallin/tini tini src b2d815261a8cdc0749ad25d30e286121468e947fc3f966fb2c11a9df049fa4b8 A tiny but valid init for containers Tini is the simplest init you could think of. All Tini does is spawn a single child (Tini is meant to be run in a container), and wait for it to exit all the while reaping zombies and performing signal forwarding. https://github.com/krallin/tini tini-debuginfo aarch64 f5f391a3cde032ac5edb01c076aca88b01ac676ebc26766207483a95a1c4d3ae Debug information for package tini This package provides debug information for package tini. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/krallin/tini tini-debugsource aarch64 db3dfd0da7b84a0f879d11fe8e0a8d923d4b4af72f1c3a1bad0162a8506aeaa1 Debug sources for package tini This package provides debug sources for package tini. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/krallin/tini tini-static aarch64 578d40335ca2e6d17d51b33d81c2e4c12ea363bfcfad5a8e52c56faf3a1598ea Standalone static build of tini This package contains a standalone static build of tini, meant to be used inside a container. https://github.com/krallin/tini tito noarch ef12c618750eb00c2741751947fa791c20579d055ba77c40f831c9aed707313b A tool for managing rpm based git projects Tito is a tool for managing tarballs, rpms, and builds for projects using git. https://github.com/rpm-software-management/tito tito src 48cc053c324dd03a4b58ebfe362631f39bff37a85af9275aa6e85b6c96fda8fd A tool for managing rpm based git projects Tito is a tool for managing tarballs, rpms, and builds for projects using git. https://github.com/rpm-software-management/tito