automatic import of python-geograpy3

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+/geograpy3-0.2.6.tar.gz
diff --git a/python-geograpy3.spec b/python-geograpy3.spec
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+%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
diff --git a/sources b/sources
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--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+5a71a84d282ea189c5af1e2852a935fe  geograpy3-0.2.6.tar.gz