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|
%global _empty_manifest_terminate_build 0
Name: python-fuzzycat
Version: 0.1.23
Release: 1
Summary: Fuzzy matching utilities for scholarly metadata
License: MIT License
URL: https://github.com/miku/fuzzycat
Source0: https://mirrors.aliyun.com/pypi/web/packages/ff/91/19a75e56b496384ca5635de7640cc50ef0de75853b1ce3c758ea6e85cdb0/fuzzycat-0.1.23.tar.gz
BuildArch: noarch
Requires: python3-dynaconf
Requires: python3-elasticsearch
Requires: python3-elasticsearch-dsl
Requires: python3-fatcat-openapi-client
Requires: python3-ftfy
Requires: python3-glom
Requires: python3-grobid-tei-xml
Requires: python3-jellyfish
Requires: python3-pyyaml
Requires: python3-regex
Requires: python3-requests
Requires: python3-thefuzz
Requires: python3-toml
Requires: python3-unidecode
Requires: python3-zstandard
Requires: python3-ipython
Requires: python3-isort
Requires: python3-mypy
Requires: python3-pylint
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-twine
Requires: python3-yapf
%description
This Python library contains routines for finding near-duplicate bibliographic
entities (primarily research papers), and estimating whether two metadata
records describe the same work (or variations of the same work). Some routines
are designed to work "offline" with batches of billions of sorted metadata
records, and others are designed to work "online" making queries against hosted
web services and catalogs.
`fuzzycat` was originally developed by Martin Czygan at the Internet Archive,
and is used in the construction of a [citation
graph](https://gitlab.com/internetarchive/refcat) and to identify duplicate
records in the [fatcat.wiki](https://fatcat.wiki) catalog and
[scholar.archive.org](https://scholar.archive.org) search index.
**DISCLAIMER:** this tool is still under development, as indicated by the "0"
major version. The interface, semantics, and behavior are likely to be tweaked.
## Quickstart
Inside a `virtualenv` (or similar), install with [pip](https://pypi.org/project/pip/):
```
pip install fuzzycat
```
The `fuzzycat.simple` module contains high-level helpers which query Internet
Archive hosted services:
import elasticsearch
from fuzzycat.simple import *
es_client = elasticsearch.Elasticsearch("https://search.fatcat.wiki:443")
# parses reference using GROBID (at https://grobid.qa.fatcat.wiki),
# then queries Elasticsearch (at https://search.fatcat.wiki),
# then scores candidates against latest catalog record fetched from
# https://api.fatcat.wiki
best_match = closest_fuzzy_unstructured_match(
"""Cunningham HB, Weis JJ, Taveras LR, Huerta S. Mesh migration following abdominal hernia repair: a comprehensive review. Hernia. 2019 Apr;23(2):235-243. doi: 10.1007/s10029-019-01898-9. Epub 2019 Jan 30. PMID: 30701369.""",
es_client=es_client)
print(best_match)
# FuzzyReleaseMatchResult(status=<Status.EXACT: 'exact'>, reason=<Reason.DOI: 'doi'>, release={...})
# same as above, but without the GROBID parsing, and returns multiple results
matches = close_fuzzy_biblio_matches(
dict(
title="Mesh migration following abdominal hernia repair: a comprehensive review",
first_author="Cunningham",
year=2019,
journal="Hernia",
),
es_client=es_client,
)
A CLI tool is included for processing records in UNIX stdin/stdout pipelines:
# print usage
python -m fuzzycat
## Features and Use-Cases
The [refcat project](https://gitlab.com/internetarchive/refcat) builds on top
of this library to build a citation graph by processing billions of structured
and unstructured reference records extracted from scholarly papers (note: jfor
performance critical parts, some code has been ported to Go, albeit the test
suite is shared between the Python and Go implementations).
Automated imports of metadata records into the fatcat catalog use fuzzycat to
filter new metadata which look like duplicates of existing records from other
sources.
In conjunction with standard command-line tools (like `sort`), fatcat bulk
metadata snapshots can be clustered and reduced into groups to flag duplicate
records for merging.
Extracted reference strings from any source (webpages, books, papers, wikis,
databases, etc) can be resolved against the fatcat catalog of scholarly papers.
## Support and Acknowledgements
Work on this software received support from the Andrew W. Mellon Foundation
through multiple phases of the ["Ensuring the Persistent Access of Open Access
Journal Literature"](https://mellon.org/grants/grants-database/advanced-search/?amount-low=&amount-high=&year-start=&year-end=&city=&state=&country=&q=%22Ensuring+the+Persistent+Access%22&per_page=25) project (see [original announcement](http://blog.archive.org/2018/03/05/andrew-w-mellon-foundation-awards-grant-to-the-internet-archive-for-long-tail-journal-preservation/)).
Additional acknowledgements [at fatcat.wiki](https://fatcat.wiki/about).
%package -n python3-fuzzycat
Summary: Fuzzy matching utilities for scholarly metadata
Provides: python-fuzzycat
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-fuzzycat
This Python library contains routines for finding near-duplicate bibliographic
entities (primarily research papers), and estimating whether two metadata
records describe the same work (or variations of the same work). Some routines
are designed to work "offline" with batches of billions of sorted metadata
records, and others are designed to work "online" making queries against hosted
web services and catalogs.
`fuzzycat` was originally developed by Martin Czygan at the Internet Archive,
and is used in the construction of a [citation
graph](https://gitlab.com/internetarchive/refcat) and to identify duplicate
records in the [fatcat.wiki](https://fatcat.wiki) catalog and
[scholar.archive.org](https://scholar.archive.org) search index.
**DISCLAIMER:** this tool is still under development, as indicated by the "0"
major version. The interface, semantics, and behavior are likely to be tweaked.
## Quickstart
Inside a `virtualenv` (or similar), install with [pip](https://pypi.org/project/pip/):
```
pip install fuzzycat
```
The `fuzzycat.simple` module contains high-level helpers which query Internet
Archive hosted services:
import elasticsearch
from fuzzycat.simple import *
es_client = elasticsearch.Elasticsearch("https://search.fatcat.wiki:443")
# parses reference using GROBID (at https://grobid.qa.fatcat.wiki),
# then queries Elasticsearch (at https://search.fatcat.wiki),
# then scores candidates against latest catalog record fetched from
# https://api.fatcat.wiki
best_match = closest_fuzzy_unstructured_match(
"""Cunningham HB, Weis JJ, Taveras LR, Huerta S. Mesh migration following abdominal hernia repair: a comprehensive review. Hernia. 2019 Apr;23(2):235-243. doi: 10.1007/s10029-019-01898-9. Epub 2019 Jan 30. PMID: 30701369.""",
es_client=es_client)
print(best_match)
# FuzzyReleaseMatchResult(status=<Status.EXACT: 'exact'>, reason=<Reason.DOI: 'doi'>, release={...})
# same as above, but without the GROBID parsing, and returns multiple results
matches = close_fuzzy_biblio_matches(
dict(
title="Mesh migration following abdominal hernia repair: a comprehensive review",
first_author="Cunningham",
year=2019,
journal="Hernia",
),
es_client=es_client,
)
A CLI tool is included for processing records in UNIX stdin/stdout pipelines:
# print usage
python -m fuzzycat
## Features and Use-Cases
The [refcat project](https://gitlab.com/internetarchive/refcat) builds on top
of this library to build a citation graph by processing billions of structured
and unstructured reference records extracted from scholarly papers (note: jfor
performance critical parts, some code has been ported to Go, albeit the test
suite is shared between the Python and Go implementations).
Automated imports of metadata records into the fatcat catalog use fuzzycat to
filter new metadata which look like duplicates of existing records from other
sources.
In conjunction with standard command-line tools (like `sort`), fatcat bulk
metadata snapshots can be clustered and reduced into groups to flag duplicate
records for merging.
Extracted reference strings from any source (webpages, books, papers, wikis,
databases, etc) can be resolved against the fatcat catalog of scholarly papers.
## Support and Acknowledgements
Work on this software received support from the Andrew W. Mellon Foundation
through multiple phases of the ["Ensuring the Persistent Access of Open Access
Journal Literature"](https://mellon.org/grants/grants-database/advanced-search/?amount-low=&amount-high=&year-start=&year-end=&city=&state=&country=&q=%22Ensuring+the+Persistent+Access%22&per_page=25) project (see [original announcement](http://blog.archive.org/2018/03/05/andrew-w-mellon-foundation-awards-grant-to-the-internet-archive-for-long-tail-journal-preservation/)).
Additional acknowledgements [at fatcat.wiki](https://fatcat.wiki/about).
%package help
Summary: Development documents and examples for fuzzycat
Provides: python3-fuzzycat-doc
%description help
This Python library contains routines for finding near-duplicate bibliographic
entities (primarily research papers), and estimating whether two metadata
records describe the same work (or variations of the same work). Some routines
are designed to work "offline" with batches of billions of sorted metadata
records, and others are designed to work "online" making queries against hosted
web services and catalogs.
`fuzzycat` was originally developed by Martin Czygan at the Internet Archive,
and is used in the construction of a [citation
graph](https://gitlab.com/internetarchive/refcat) and to identify duplicate
records in the [fatcat.wiki](https://fatcat.wiki) catalog and
[scholar.archive.org](https://scholar.archive.org) search index.
**DISCLAIMER:** this tool is still under development, as indicated by the "0"
major version. The interface, semantics, and behavior are likely to be tweaked.
## Quickstart
Inside a `virtualenv` (or similar), install with [pip](https://pypi.org/project/pip/):
```
pip install fuzzycat
```
The `fuzzycat.simple` module contains high-level helpers which query Internet
Archive hosted services:
import elasticsearch
from fuzzycat.simple import *
es_client = elasticsearch.Elasticsearch("https://search.fatcat.wiki:443")
# parses reference using GROBID (at https://grobid.qa.fatcat.wiki),
# then queries Elasticsearch (at https://search.fatcat.wiki),
# then scores candidates against latest catalog record fetched from
# https://api.fatcat.wiki
best_match = closest_fuzzy_unstructured_match(
"""Cunningham HB, Weis JJ, Taveras LR, Huerta S. Mesh migration following abdominal hernia repair: a comprehensive review. Hernia. 2019 Apr;23(2):235-243. doi: 10.1007/s10029-019-01898-9. Epub 2019 Jan 30. PMID: 30701369.""",
es_client=es_client)
print(best_match)
# FuzzyReleaseMatchResult(status=<Status.EXACT: 'exact'>, reason=<Reason.DOI: 'doi'>, release={...})
# same as above, but without the GROBID parsing, and returns multiple results
matches = close_fuzzy_biblio_matches(
dict(
title="Mesh migration following abdominal hernia repair: a comprehensive review",
first_author="Cunningham",
year=2019,
journal="Hernia",
),
es_client=es_client,
)
A CLI tool is included for processing records in UNIX stdin/stdout pipelines:
# print usage
python -m fuzzycat
## Features and Use-Cases
The [refcat project](https://gitlab.com/internetarchive/refcat) builds on top
of this library to build a citation graph by processing billions of structured
and unstructured reference records extracted from scholarly papers (note: jfor
performance critical parts, some code has been ported to Go, albeit the test
suite is shared between the Python and Go implementations).
Automated imports of metadata records into the fatcat catalog use fuzzycat to
filter new metadata which look like duplicates of existing records from other
sources.
In conjunction with standard command-line tools (like `sort`), fatcat bulk
metadata snapshots can be clustered and reduced into groups to flag duplicate
records for merging.
Extracted reference strings from any source (webpages, books, papers, wikis,
databases, etc) can be resolved against the fatcat catalog of scholarly papers.
## Support and Acknowledgements
Work on this software received support from the Andrew W. Mellon Foundation
through multiple phases of the ["Ensuring the Persistent Access of Open Access
Journal Literature"](https://mellon.org/grants/grants-database/advanced-search/?amount-low=&amount-high=&year-start=&year-end=&city=&state=&country=&q=%22Ensuring+the+Persistent+Access%22&per_page=25) project (see [original announcement](http://blog.archive.org/2018/03/05/andrew-w-mellon-foundation-awards-grant-to-the-internet-archive-for-long-tail-journal-preservation/)).
Additional acknowledgements [at fatcat.wiki](https://fatcat.wiki/about).
%prep
%autosetup -n fuzzycat-0.1.23
%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-fuzzycat -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.23-1
- Package Spec generated
|
