path: root/python-geograpy3.spec

%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
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.6-1
- Package Spec generated