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diff --git a/python-geograpy3.spec b/python-geograpy3.spec new file mode 100644 index 0000000..95cb010 --- /dev/null +++ b/python-geograpy3.spec @@ -0,0 +1,344 @@ +%global _empty_manifest_terminate_build 0 +Name: python-geograpy3 +Version: 0.2.6 +Release: 1 +Summary: Extract countries, regions and cities from a URL or text +License: Apache +URL: https://github.com/somnathrakshit/geograpy3 +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/e3/91/79ee302e5ab4a47d3f844539b0a247d65755f2fb163471f418a02c790747/geograpy3-0.2.6.tar.gz +BuildArch: noarch + +Requires: python3-newspaper3k +Requires: python3-nltk +Requires: python3-jellyfish +Requires: python3-numpy +Requires: python3-pylodstorage +Requires: python3-sphinx-rtd-theme +Requires: python3-scikit-learn +Requires: python3-pandas + +%description +geograpy extracts place names from a URL or text, and adds context to those names -- for example distinguishing between a country, region or city. +The extraction is a two step process. The first process is a Natural Language Processing task which analyzes a text for potential mentions of geographic locations. In the next step the words which represent such locations are looked up using the Locator. +If you already know that your content has geographic information you might want to use the Locator interface directly. +## Examples/Tutorial +* [see Examples/Tutorial Wiki](http://wiki.bitplan.com/index.php/Geograpy#Examples) +## Install & Setup +Grab the package using `pip` (this will take a few minutes) +```bash +pip install geograpy3 +``` +geograpy3 uses [NLTK](http://www.nltk.org/) for entity recognition, so you'll also need +to download the models we're using. Fortunately there's a command that'll take +care of this for you. +```bash +geograpy-nltk +``` +## Getting the source code +```bash +git clone https://github.com/somnathrakshit/geograpy3 +cd geograpy3 +scripts/install +``` +## Basic Usage +Import the module, give some text or a URL, and presto. +```python +import geograpy +url = 'https://en.wikipedia.org/wiki/2012_Summer_Olympics_torch_relay' +places = geograpy.get_geoPlace_context(url=url) +``` +Now you have access to information about all the places mentioned in the linked +article. +* `places.countries` _contains a list of country names_ +* `places.regions` _contains a list of region names_ +* `places.cities` _contains a list of city names_ +* `places.other` _lists everything that wasn't clearly a country, region or city_ +Note that the `other` list might be useful for shorter texts, to pull out +information like street names, points of interest, etc, but at the moment is +a bit messy when scanning longer texts that contain possessive forms of proper +nouns (like "Russian" instead of "Russia"). +## But Wait, There's More +In addition to listing the names of discovered places, you'll also get some +information about the relationships between places. +* `places.country_regions` _regions broken down by country_ +* `places.country_cities` _cities broken down by country_ +* `places.address_strings` _city, region, country strings useful for geocoding_ +## Last But Not Least +While a text might mention many places, it's probably focused on one or two, so +geograpy3 also breaks down countries, regions and cities by number of mentions. +* `places.country_mentions` +* `places.region_mentions` +* `places.city_mentions` +Each of these returns a list of tuples. The first item in the tuple is the place +name and the second item is the number of mentions. For example: + [('Russian Federation', 14), (u'Ukraine', 11), (u'Lithuania', 1)] +## If You're Really Serious +You can of course use each of Geograpy's modules on their own. For example: +```python +from geograpy import extraction +e = extraction.Extractor(url='https://en.wikipedia.org/wiki/2012_Summer_Olympics_torch_relay') +e.find_geoEntities() +# You can now access all of the places found by the Extractor +print(e.places) +``` +Place context is handled in the `places` module. For example: +```python +from geograpy import places +pc = places.PlaceContext(['Cleveland', 'Ohio', 'United States']) +pc.set_countries() +print pc.countries #['United States'] +pc.set_regions() +print(pc.regions #['Ohio']) +pc.set_cities() +print(pc.cities #['Cleveland']) +print(pc.address_strings #['Cleveland, Ohio, United States']) +``` +And of course all of the other information shown above (`country_regions` etc) +is available after the corresponding `set_` method is called. +## Stackoverflow +* [Questions tagged with 'geograpy'](https://stackoverflow.com/questions/tagged/geograpy) +## Credits +geograpy3 uses the following excellent libraries: +* [NLTK](http://www.nltk.org/) for entity recognition +* [newspaper](https://github.com/codelucas/newspaper) for text extraction from HTML +* [jellyfish](https://github.com/sunlightlabs/jellyfish) for fuzzy text match +* [pylodstorage](https://pypi.org/project/pylodstorage/) for storage and retrieval of tabular data from SQL and SPARQL sources +geograpy3 uses the following data sources: +* [ISO3166ErrorDictionary](https://github.com/bodacea/countryname/blob/master/countryname/databases/ISO3166ErrorDictionary.csv) for common country mispellings _via [Sara-Jayne Terp](https://github.com/bodacea)_ +* [Wikidata](https://www.wikidata.org) for country/region/city information with disambiguation via population +Hat tip to [Chris Albon](https://github.com/chrisalbon) for the name. + +%package -n python3-geograpy3 +Summary: Extract countries, regions and cities from a URL or text +Provides: python-geograpy3 +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-geograpy3 +geograpy extracts place names from a URL or text, and adds context to those names -- for example distinguishing between a country, region or city. +The extraction is a two step process. The first process is a Natural Language Processing task which analyzes a text for potential mentions of geographic locations. In the next step the words which represent such locations are looked up using the Locator. +If you already know that your content has geographic information you might want to use the Locator interface directly. +## Examples/Tutorial +* [see Examples/Tutorial Wiki](http://wiki.bitplan.com/index.php/Geograpy#Examples) +## Install & Setup +Grab the package using `pip` (this will take a few minutes) +```bash +pip install geograpy3 +``` +geograpy3 uses [NLTK](http://www.nltk.org/) for entity recognition, so you'll also need +to download the models we're using. Fortunately there's a command that'll take +care of this for you. +```bash +geograpy-nltk +``` +## Getting the source code +```bash +git clone https://github.com/somnathrakshit/geograpy3 +cd geograpy3 +scripts/install +``` +## Basic Usage +Import the module, give some text or a URL, and presto. +```python +import geograpy +url = 'https://en.wikipedia.org/wiki/2012_Summer_Olympics_torch_relay' +places = geograpy.get_geoPlace_context(url=url) +``` +Now you have access to information about all the places mentioned in the linked +article. +* `places.countries` _contains a list of country names_ +* `places.regions` _contains a list of region names_ +* `places.cities` _contains a list of city names_ +* `places.other` _lists everything that wasn't clearly a country, region or city_ +Note that the `other` list might be useful for shorter texts, to pull out +information like street names, points of interest, etc, but at the moment is +a bit messy when scanning longer texts that contain possessive forms of proper +nouns (like "Russian" instead of "Russia"). +## But Wait, There's More +In addition to listing the names of discovered places, you'll also get some +information about the relationships between places. +* `places.country_regions` _regions broken down by country_ +* `places.country_cities` _cities broken down by country_ +* `places.address_strings` _city, region, country strings useful for geocoding_ +## Last But Not Least +While a text might mention many places, it's probably focused on one or two, so +geograpy3 also breaks down countries, regions and cities by number of mentions. +* `places.country_mentions` +* `places.region_mentions` +* `places.city_mentions` +Each of these returns a list of tuples. The first item in the tuple is the place +name and the second item is the number of mentions. For example: + [('Russian Federation', 14), (u'Ukraine', 11), (u'Lithuania', 1)] +## If You're Really Serious +You can of course use each of Geograpy's modules on their own. For example: +```python +from geograpy import extraction +e = extraction.Extractor(url='https://en.wikipedia.org/wiki/2012_Summer_Olympics_torch_relay') +e.find_geoEntities() +# You can now access all of the places found by the Extractor +print(e.places) +``` +Place context is handled in the `places` module. For example: +```python +from geograpy import places +pc = places.PlaceContext(['Cleveland', 'Ohio', 'United States']) +pc.set_countries() +print pc.countries #['United States'] +pc.set_regions() +print(pc.regions #['Ohio']) +pc.set_cities() +print(pc.cities #['Cleveland']) +print(pc.address_strings #['Cleveland, Ohio, United States']) +``` +And of course all of the other information shown above (`country_regions` etc) +is available after the corresponding `set_` method is called. +## Stackoverflow +* [Questions tagged with 'geograpy'](https://stackoverflow.com/questions/tagged/geograpy) +## Credits +geograpy3 uses the following excellent libraries: +* [NLTK](http://www.nltk.org/) for entity recognition +* [newspaper](https://github.com/codelucas/newspaper) for text extraction from HTML +* [jellyfish](https://github.com/sunlightlabs/jellyfish) for fuzzy text match +* [pylodstorage](https://pypi.org/project/pylodstorage/) for storage and retrieval of tabular data from SQL and SPARQL sources +geograpy3 uses the following data sources: +* [ISO3166ErrorDictionary](https://github.com/bodacea/countryname/blob/master/countryname/databases/ISO3166ErrorDictionary.csv) for common country mispellings _via [Sara-Jayne Terp](https://github.com/bodacea)_ +* [Wikidata](https://www.wikidata.org) for country/region/city information with disambiguation via population +Hat tip to [Chris Albon](https://github.com/chrisalbon) for the name. + +%package help +Summary: Development documents and examples for geograpy3 +Provides: python3-geograpy3-doc +%description help +geograpy extracts place names from a URL or text, and adds context to those names -- for example distinguishing between a country, region or city. +The extraction is a two step process. The first process is a Natural Language Processing task which analyzes a text for potential mentions of geographic locations. In the next step the words which represent such locations are looked up using the Locator. +If you already know that your content has geographic information you might want to use the Locator interface directly. +## Examples/Tutorial +* [see Examples/Tutorial Wiki](http://wiki.bitplan.com/index.php/Geograpy#Examples) +## Install & Setup +Grab the package using `pip` (this will take a few minutes) +```bash +pip install geograpy3 +``` +geograpy3 uses [NLTK](http://www.nltk.org/) for entity recognition, so you'll also need +to download the models we're using. Fortunately there's a command that'll take +care of this for you. +```bash +geograpy-nltk +``` +## Getting the source code +```bash +git clone https://github.com/somnathrakshit/geograpy3 +cd geograpy3 +scripts/install +``` +## Basic Usage +Import the module, give some text or a URL, and presto. +```python +import geograpy +url = 'https://en.wikipedia.org/wiki/2012_Summer_Olympics_torch_relay' +places = geograpy.get_geoPlace_context(url=url) +``` +Now you have access to information about all the places mentioned in the linked +article. +* `places.countries` _contains a list of country names_ +* `places.regions` _contains a list of region names_ +* `places.cities` _contains a list of city names_ +* `places.other` _lists everything that wasn't clearly a country, region or city_ +Note that the `other` list might be useful for shorter texts, to pull out +information like street names, points of interest, etc, but at the moment is +a bit messy when scanning longer texts that contain possessive forms of proper +nouns (like "Russian" instead of "Russia"). +## But Wait, There's More +In addition to listing the names of discovered places, you'll also get some +information about the relationships between places. +* `places.country_regions` _regions broken down by country_ +* `places.country_cities` _cities broken down by country_ +* `places.address_strings` _city, region, country strings useful for geocoding_ +## Last But Not Least +While a text might mention many places, it's probably focused on one or two, so +geograpy3 also breaks down countries, regions and cities by number of mentions. +* `places.country_mentions` +* `places.region_mentions` +* `places.city_mentions` +Each of these returns a list of tuples. The first item in the tuple is the place +name and the second item is the number of mentions. For example: + [('Russian Federation', 14), (u'Ukraine', 11), (u'Lithuania', 1)] +## If You're Really Serious +You can of course use each of Geograpy's modules on their own. For example: +```python +from geograpy import extraction +e = extraction.Extractor(url='https://en.wikipedia.org/wiki/2012_Summer_Olympics_torch_relay') +e.find_geoEntities() +# You can now access all of the places found by the Extractor +print(e.places) +``` +Place context is handled in the `places` module. For example: +```python +from geograpy import places +pc = places.PlaceContext(['Cleveland', 'Ohio', 'United States']) +pc.set_countries() +print pc.countries #['United States'] +pc.set_regions() +print(pc.regions #['Ohio']) +pc.set_cities() +print(pc.cities #['Cleveland']) +print(pc.address_strings #['Cleveland, Ohio, United States']) +``` +And of course all of the other information shown above (`country_regions` etc) +is available after the corresponding `set_` method is called. +## Stackoverflow +* [Questions tagged with 'geograpy'](https://stackoverflow.com/questions/tagged/geograpy) +## Credits +geograpy3 uses the following excellent libraries: +* [NLTK](http://www.nltk.org/) for entity recognition +* [newspaper](https://github.com/codelucas/newspaper) for text extraction from HTML +* [jellyfish](https://github.com/sunlightlabs/jellyfish) for fuzzy text match +* [pylodstorage](https://pypi.org/project/pylodstorage/) for storage and retrieval of tabular data from SQL and SPARQL sources +geograpy3 uses the following data sources: +* [ISO3166ErrorDictionary](https://github.com/bodacea/countryname/blob/master/countryname/databases/ISO3166ErrorDictionary.csv) for common country mispellings _via [Sara-Jayne Terp](https://github.com/bodacea)_ +* [Wikidata](https://www.wikidata.org) for country/region/city information with disambiguation via population +Hat tip to [Chris Albon](https://github.com/chrisalbon) for the name. + +%prep +%autosetup -n geograpy3-0.2.6 + +%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-geograpy3 -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.6-1 +- Package Spec generated |