%global _empty_manifest_terminate_build 0 Name: python-fingerprints Version: 1.1.0 Release: 1 Summary: A library to generate entity fingerprints. License: MIT URL: http://github.com/alephdata/fingerprints Source0: https://mirrors.nju.edu.cn/pypi/web/packages/1a/4a/953982950ddd39da097c6e527aba8ca080c02cd76577c946295dfe2d02ed/fingerprints-1.1.0.tar.gz BuildArch: noarch Requires: python3-normality Requires: python3-pytest Requires: python3-pytest-cov Requires: python3-mypy Requires: python3-black Requires: python3-pyyaml Requires: python3-types-pyyaml Requires: python3-bump2version %description # fingerprints ![package](https://github.com/alephdata/fingerprints/workflows/package/badge.svg) This library helps with the generation of fingerprints for entity data. A fingerprint in this context is understood as a simplified entity identifier, derived from it's name or address and used for cross-referencing of entity across different datasets. ## Usage ```python import fingerprints fp = fingerprints.generate('Mr. Sherlock Holmes') assert fp == 'holmes sherlock' fp = fingerprints.generate('Siemens Aktiengesellschaft') assert fp == 'ag siemens' fp = fingerprints.generate('New York, New York') assert fp == 'new york' ``` ## Company type names A significant part of what `fingerprints` does it to recognize company legal form names. For example, `fingerprints` will be able to simplify `Общество с ограниченной ответственностью` to `ООО`, or `Aktiengesellschaft` to `AG`. The required database is based on two different sources: * A [Google Spreadsheet](https://docs.google.com/spreadsheets/d/1Cw2xQ3hcZOAgnnzejlY5Sv3OeMxKePTqcRhXQU8rCAw/edit?ts=5e7754cf#gid=0) created by OCCRP. * The ISO 20275: [Entity Legal Forms Code List](https://www.gleif.org/en/about-lei/code-lists/iso-20275-entity-legal-forms-code-list) Wikipedia also maintains an index of [types of business entity](https://en.wikipedia.org/wiki/Types_of_business_entity). ## See also * [Clustering in Depth](https://github.com/OpenRefine/OpenRefine/wiki/Clustering-In-Depth), part of the OpenRefine documentation discussing how to create collisions in data clustering. * [probablepeople](https://github.com/datamade/probablepeople), parser for western names made by the brilliant folks at datamade.us. %package -n python3-fingerprints Summary: A library to generate entity fingerprints. Provides: python-fingerprints BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-fingerprints # fingerprints ![package](https://github.com/alephdata/fingerprints/workflows/package/badge.svg) This library helps with the generation of fingerprints for entity data. A fingerprint in this context is understood as a simplified entity identifier, derived from it's name or address and used for cross-referencing of entity across different datasets. ## Usage ```python import fingerprints fp = fingerprints.generate('Mr. Sherlock Holmes') assert fp == 'holmes sherlock' fp = fingerprints.generate('Siemens Aktiengesellschaft') assert fp == 'ag siemens' fp = fingerprints.generate('New York, New York') assert fp == 'new york' ``` ## Company type names A significant part of what `fingerprints` does it to recognize company legal form names. For example, `fingerprints` will be able to simplify `Общество с ограниченной ответственностью` to `ООО`, or `Aktiengesellschaft` to `AG`. The required database is based on two different sources: * A [Google Spreadsheet](https://docs.google.com/spreadsheets/d/1Cw2xQ3hcZOAgnnzejlY5Sv3OeMxKePTqcRhXQU8rCAw/edit?ts=5e7754cf#gid=0) created by OCCRP. * The ISO 20275: [Entity Legal Forms Code List](https://www.gleif.org/en/about-lei/code-lists/iso-20275-entity-legal-forms-code-list) Wikipedia also maintains an index of [types of business entity](https://en.wikipedia.org/wiki/Types_of_business_entity). ## See also * [Clustering in Depth](https://github.com/OpenRefine/OpenRefine/wiki/Clustering-In-Depth), part of the OpenRefine documentation discussing how to create collisions in data clustering. * [probablepeople](https://github.com/datamade/probablepeople), parser for western names made by the brilliant folks at datamade.us. %package help Summary: Development documents and examples for fingerprints Provides: python3-fingerprints-doc %description help # fingerprints ![package](https://github.com/alephdata/fingerprints/workflows/package/badge.svg) This library helps with the generation of fingerprints for entity data. A fingerprint in this context is understood as a simplified entity identifier, derived from it's name or address and used for cross-referencing of entity across different datasets. ## Usage ```python import fingerprints fp = fingerprints.generate('Mr. Sherlock Holmes') assert fp == 'holmes sherlock' fp = fingerprints.generate('Siemens Aktiengesellschaft') assert fp == 'ag siemens' fp = fingerprints.generate('New York, New York') assert fp == 'new york' ``` ## Company type names A significant part of what `fingerprints` does it to recognize company legal form names. For example, `fingerprints` will be able to simplify `Общество с ограниченной ответственностью` to `ООО`, or `Aktiengesellschaft` to `AG`. The required database is based on two different sources: * A [Google Spreadsheet](https://docs.google.com/spreadsheets/d/1Cw2xQ3hcZOAgnnzejlY5Sv3OeMxKePTqcRhXQU8rCAw/edit?ts=5e7754cf#gid=0) created by OCCRP. * The ISO 20275: [Entity Legal Forms Code List](https://www.gleif.org/en/about-lei/code-lists/iso-20275-entity-legal-forms-code-list) Wikipedia also maintains an index of [types of business entity](https://en.wikipedia.org/wiki/Types_of_business_entity). ## See also * [Clustering in Depth](https://github.com/OpenRefine/OpenRefine/wiki/Clustering-In-Depth), part of the OpenRefine documentation discussing how to create collisions in data clustering. * [probablepeople](https://github.com/datamade/probablepeople), parser for western names made by the brilliant folks at datamade.us. %prep %autosetup -n fingerprints-1.1.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-fingerprints -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 1.1.0-1 - Package Spec generated