Saravanan666/pypi-org huggingface dataset:
Package: geoplots
Matplotlib rcParams for GeoSpatial Plots=======================Installationpipinstallgeoplots-UUsageimportmatplotlib.pyplotaspltfromgeoplotsimportparamsplt.rcParams.update(params)LicenseThis is free and unencumbered software released into the public domain.Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any means.
Package: geopolygonize
GeopolygonizeConvert geographic rasters into simplified polygons. Given an input raster file, this tool produces a gpkg file representation of the raster that simplifies out pixelation. It also maintains shapes' relations to one another such that the output is guaranteed to have no gaps.AlgorithmMost existing methods for polygon simplification such as theDouglas–Peucker simplification algorithmorconcave hullsoperate on a single polygon at a time. This presents a problem when run on a collection of polygons that fit together perfectly–simplifying each polygon separately will introduce unpredictable gaps. Our algorithm takes an approach similar toTopoJSON. It identifies the boundaries shared between polygons, simplifies those boundaries, then assigns the simplified boundaries back to the polygons. This results in simplified polygons that fit perfectly together without any gaps.Installpip install geopolygonizeQuickstartTo convert a raster to simplified polygons, run:geopolygonize --input-file="data/input.tif" --output-file="data/output.shp"Options + APIRefer to thedocs.DevelopmentInstall the dependencies:pip install -r requirements.txtThen, run the CLI:python -m src.geopolygonize --input-file="data/input.tif" --output-file="data/output.shp"Test against some existing input:./tests/run_e2e_test.sh <input>Find test raster inputshere.
Package: geoprisma_config
Geoprisma Config================Tool to configure geoprisma maps using a prostgresql database. It's made to be installed in a django app.Geoprisma is a library used for webmapping made by MapGears. You can find the code at http://www.geoprisma.orgInstall-------Get it from pypi:pip install geoprisma_configGet it from github:git clone git://github.com/solution-globale-informatique/geoprisma_config.gitcd geoprisma_configpython setup.pyQuick start-----------Contrib Admin is required.1. Add "geoprisma_config" to your INSTALLED_APPS setting like this::INSTALLED_APPS = (...'geoprisma_config',)2. Include the geoprisma_config URLconf in your project urls.py like this::url(r'^geoprisma_config/', include('geoprisma_config.urls')),3. Run `python manage.py syncdb` to create the geoprisma_config models.4. Start the development server and visit http://127.0.0.1:8000/admin/to manage your geoprisma configs (you'll need the Admin app enabled).Production Setup----------------Staticfiles is needed to work, so check https://docs.djangoproject.com/en/dev/howto/static-files/ for support.Tested------This app have been tested with Django 1.3, 1.4 and 1.5 .Django 1.2 might be ok if you add django-staticfiles app. Check https://docs.djangoproject.com/en/dev/howto/static-files/BSD License=======Copyright (c) 2013, Solution Globale Informatique & NippourAll rights reserved.Redistribution and use in source and binary forms, with or withoutmodification, are permitted provided that the following conditions are met:1. Redistributions of source code must retain the above copyrightnotice, this list of conditions and the following disclaimer.2. Redistributions in binary form must reproduce the above copyrightnotice, this list of conditions and the following disclaimer in thedocumentation and/or other materials provided with the distribution.3. All advertising materials mentioning features or use of this softwaremust display the following acknowledgement:This product includes software developed by the Solution Globale Informatique and Nippour.4. Neither the name of the Solution Globale Informatique and Nippour nor thenames of its contributors may be used to endorse or promote productsderived from this software without specific prior written permission.THIS SOFTWARE IS PROVIDED BY Solution Globale Informatique AND Nippour ''AS IS'' AND ANYEXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIEDWARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE AREDISCLAIMED. IN NO EVENT SHALL Solution Globale Informatique or Nippour BE LIABLE FOR ANYDIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED ANDON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THISSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Package: geoproximity
GeoProximity Package Usage GuideIntroductionGeoProximity is a Python package designed to facilitate geospatial distance calculations, point projections, and related operations. It includes functions for both Haversine distance (great-circle distance) and Euclidean distance calculations. GeoProximity is suitable for applications that require proximity analysis, spatial operations, and basic geospatial functionality.InstallationEnsure Python is installed on your system, then install thegeoproximitypackage usingpip:pipinstallgeoproximityCalculate harversine distanceimportgeoproximitygeoproximity.haversine_distance(coord1,coord2)It returns the haversine distance between two points in km.Find the nearest neighbor from the list of coordinates to the referece pointgeoproximity.nearest_neighbor(reference_point,coordinates_list)It returns a tuple containing the nearest point and distance of the nearest point from the reference point in km.Example:reference_point=(37.7749,-122.4194)other_points=[(34.0522,-118.2437),(40.7128,-74.0060),(41.8781,-87.6298)]nearest_point,min_distance=geoproximity.nearest_neighbor(reference_point,other_points)Calculate the area of a polygongeoproximity.calculate_polygon_area(polygon_geojson)We need to pass the polygon geojson as a parameter to thecalculate_polygon_areafunction. It returns the area of the polygon in square km.
Package: geopy
geopy is a Python client for several popular geocoding web
services.geopy makes it easy for Python developers to locate the coordinates of
addresses, cities, countries, and landmarks across the globe using
third-party geocoders and other data sources.geopy includes geocoder classes for theOpenStreetMap Nominatim,Google Geocoding API (V3), and many other geocoding services.
The full list is available on theGeocoders doc section.
Geocoder classes are located ingeopy.geocoders.geopy is tested against CPython (versions 3.7, 3.8, 3.9, 3.10, 3.11, 3.12)
and PyPy3. geopy 1.x line also supported CPython 2.7, 3.4 and PyPy2.© geopy contributors 2006-2018 (see AUTHORS) under theMIT
License.InstallationInstall usingpipwith:pip install geopyOr,download a wheel or source archive from
PyPI.GeocodingTo geolocate a query to an address and coordinates:>>>fromgeopy.geocodersimportNominatim>>>geolocator=Nominatim(user_agent="specify_your_app_name_here")>>>location=geolocator.geocode("175 5th Avenue NYC")>>>print(location.address)Flatiron Building, 175, 5th Avenue, Flatiron, New York, NYC, New York, ...>>>print((location.latitude,location.longitude))(40.7410861, -73.9896297241625)>>>print(location.raw){'place_id': '9167009604', 'type': 'attraction', ...}To find the address corresponding to a set of coordinates:>>>fromgeopy.geocodersimportNominatim>>>geolocator=Nominatim(user_agent="specify_your_app_name_here")>>>location=geolocator.reverse("52.509669, 13.376294")>>>print(location.address)Potsdamer Platz, Mitte, Berlin, 10117, Deutschland, European Union>>>print((location.latitude,location.longitude))(52.5094982, 13.3765983)>>>print(location.raw){'place_id': '654513', 'osm_type': 'node', ...}Measuring DistanceGeopy can calculate geodesic distance between two points using thegeodesic distanceor thegreat-circle distance,
with a default of the geodesic distance available as the functiongeopy.distance.distance.Here’s an example usage of the geodesic distance, taking pair
of(lat, lon)tuples:>>>fromgeopy.distanceimportgeodesic>>>newport_ri=(41.49008,-71.312796)>>>cleveland_oh=(41.499498,-81.695391)>>>print(geodesic(newport_ri,cleveland_oh).miles)538.390445368Using great-circle distance, also taking pair of(lat, lon)tuples:>>>fromgeopy.distanceimportgreat_circle>>>newport_ri=(41.49008,-71.312796)>>>cleveland_oh=(41.499498,-81.695391)>>>print(great_circle(newport_ri,cleveland_oh).miles)536.997990696DocumentationMore documentation and examples can be found atRead the Docs.
Package: geo-py
Set of algorithms and structures related to geodesy.APIgeo.sphereFunctions onto spheregeo.sphere.approximate_distancedefapproximate_distance(point1,point2):Approximate calculation distance
(expanding the trigonometric functions around the midpoint)geo.sphere.haversine_distancedef_haversine_distance(point1,point2):Calculating haversine distance between two points (seehttps://en.wikipedia.org/wiki/Haversine_formula,https://www.math.ksu.edu/~dbski/writings/haversine.pdf)Is numerically better-conditioned for small distancesgeo.sphere.distancedefdistance(point1,point2):Calculating great-circle distance (seehttps://en.wikipedia.org/wiki/Great-circle_distance)geo.sphere.bearingdefbearing(point1,point2):Calculating initial bearing between two points
(seehttp://www.movable-type.co.uk/scripts/latlong.html)geo.sphere.final_bearingdeffinal_bearing(point1,point2):Calculating finatl bearing (initial bering + 180) between two pointsgeo.sphere.destinationdefdestination(point,distance,bearing):Given a start point, initial bearing, and distance, this will
calculate the destina­tion point and final bearing travelling
along a (shortest distance) great circle arc. (seehttp://www.movable-type.co.uk/scripts/latlong.htm)geo.sphere.approximate_destinationdefapproximate_destination(point,distance,theta):geo.sphere.from4326_to3857deffrom4326_to3857(point):Reproject point from EPSG:4326 (https://epsg.io/4326) to EPSG:3857 (https://epsg.io/3857) (see http://wiki.openstreetmap.org/wiki/Mercator)Spherical Mercator:E = R*(λ - λo)
N = R*ln(tan(π/4+φ/2))geo.sphere.from3857_to4326deffrom4326_to3857(point):Reproject point from EPSG:3857 (https://epsg.io/3857) to EPSG:4326 (https://epsg.io/4326) (see http://wiki.openstreetmap.org/wiki/Mercator)Reverse Spherical Mercator:λ = E/R + λo
φ = π/2 - 2*arctan(exp(-N/R))geo.ellipsoidFunctions onto ellipsoidgeo.ellipsoid.distancedefdistance(point1,point2,ellipsoid=WGS84):Calculating distance with using vincenty’s formula
(seehttps://en.wikipedia.org/wiki/Vincenty’s_formulae)geo.ellipsoid.from4326_to3395deffrom4326_to3395(point,ellipsoid=WGS84):Reproject point from EPSG:4326 (https://epsg.io/4326) to EPSG:3395 (https://epsg.io/3395) (see https://en.wikipedia.org/wiki/Mercator_projection#Generalization_to_the_ellipsoid)Ellipsoidal Mercator:E = a*(λ - λo)
N = a*ln(tan(π/4+φ/2)*((1-e*sin(φ))/(1+e*sin(φ)))**e/2)geo.ellipsoid.from3395_to4326deffrom3395_to4326(point,ellipsoid=WGS84):Reproject point from EPSG:3395 (https://epsg.io/3395) to EPSG:4326 (https://epsg.io/4326) (see https://en.wikipedia.org/wiki/Mercator_projection#Generalization_to_the_ellipsoid)Reverse Ellipsoidal Mercator:λ = E/a + λo
φ = π/2 + 2*arctan(exp(-N/a)*((1-e*sin(φ))/(1+e*sin(φ))**e/2))
Package: geopycoor
geopycoorEsse é um pacote que permite realizar diversas funcionalidades relacionadas a mapas geográficos.Instalação:pip install geopycoorUso:...
import geopycoor as gcO programa permite ao usuário:visualizar um mapa com uma latitude, longitude e nível de zoom específicos;
adicionar marcadores ao mapa;
calcular a distância entre dois pontos no mapa;
recuperar dados meteorológicos de um determinado local;
converter um endereço ou coordenadas para seu formato oposto.O programa usa as seguintes classes:Mapa: da biblioteca Folium, representa um mapa;
Coordenada: representa um par de latitude e longitude;
Localização: representa uma localização com um endereço e coordenadas;
OSMGeocoder: da biblioteca geopy, é usado para recuperar um objeto Location de um endereço.
Package: geopylib
geopylibEsse é um pacote que permite realizar diversas funcionalidades relacionadas a mapas geográficos.Instalação:pip install geopylibUso:...
import geopylib as glO programa permite ao usuário:visualizar um mapa com uma latitude, longitude e nível de zoom específicos;
adicionar marcadores ao mapa;
calcular a distância entre dois pontos no mapa;
recuperar dados meteorológicos de um determinado local;
converter um endereço ou coordenadas para seu formato oposto.O programa usa as seguintes classes:Mapa: da biblioteca Folium, representa um mapa;
Coordenada: representa um par de latitude e longitude;
Localização: representa uma localização com um endereço e coordenadas;
OSMGeocoder: da biblioteca geopy, é usado para recuperar um objeto Location de um endereço.
Package: geo_python
GEO PythonSimple GEO library based on Redis GEO commandsFree software: MIT licenseDocumentation:https://geo-python.readthedocs.io.From version 3.2, Redis contains a set of very wonderful commands: the GEO commands (https://redis.io/commands#geo)With these commands, we can easily develop LBS or GEO application.Unfortunately, these features are not in redis-py(https://github.com/andymccurdy/redis-py) released packages, so we can only use its development version.Get StartedIn [1]: from geo_python import Point
In [2]: class MyPoint(Point):
   ...:     __key__ = 'my_point'
   ...:


In [3]: point = MyPoint.create(120, 40, 'my point 1')
In [4]: MyPoint.query_by_pos(point.longitude, point.latitude)
Out[4]: [<MyPoint __key__:my_point longitude:120.000000894 latitude:39.9999999108 member:my point 1>]


In [5]: MyPoint.query_by_member(point.member)
Out[5]: [<MyPoint __key__:my_point longitude:120.000000894 latitude:39.9999999108 member:my point 1>]


In [6]: point.update(member='my point 2')
In [7]: print point.member
my point 2

In [8]: another_point = MyPoint.get_by_member(point.member)

In [9]: print another_point
<MyPoint __key__:my_point longitude:120.000000894 latitude:39.9999999108 member:my point 2>

In [10]: print MyPoint.dist(point, another_point)
0.0

In [11]: point.geo_hash()
Out[11]: 'wxj7d9v2fs0'CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2016-12-30)First release on PyPI.0.1.1 (2017-01-12)Change the config0.1.2 (2017-01-12)Fix config error
Package: geopyv
geopyvPython/C++ based PIV/DIC package.Development instructionsSetupUse pipenv in root folder of project:pip install pipenvpipenv shellcd externalgit submodule update --initcd ..pipenv install .pipenv uninstall geopyvDocs using sphinxUsing sphinx and 'Read the Docs' theme:pip install sphinx spinx-rtd-themeIn the /docs folder:make clean htmlOpen index.html in build to view the contents.Use numpy docstrings formatting (via napoleon extension to sphinx, which is installed in
conf.py).Tests using pytestUse pytests to execute tests in the /tests folder: pipenv install pytestAll files in tests folderAll files named 'tests_*.py' where * is the module, function or class nameSeehttps://www.youtube.com/watch?v=etosV2IWBF0for an introductionVersion controlBasicsgit status - checks status of local repogit add . - adds all files to staging areagit commit -m "Message..." - commits staged files with the message outlinedgit push - Pushes commit to GitHubTo fork and write a featureFollow this guide:http://blog.davidecoppola.com/2016/11/howto-contribute-to-open-source-project-on-github/Code stylePlease check code style before commits...FormattingUse black:https://black.readthedocs.io/en/stable/the_black_code_style.htmlIf using pipenv install via: pipenv install black --preDon't forget the --pre or it will cause problems!PEP8 complianceCheck PEP8 compliance using flake8: pipenv install flake8
Package: geoql
Library for performing queries and transformations on GeoJSON data (with emphasis on support for abstract graph representations).Package Installation and UsageThe package is available on PyPI:python -m pip install geoqlThe library can be imported in the usual ways:import geoql
from geoql import geoqlExamplesAn example of usage is provided  below:import geojson
from geoql import geoql
import geoleaflet
import requests

url = 'https://raw.githubusercontent.com/Data-Mechanics/geoql/master/examples/'

# Boston ZIP Codes regions.
z = geoql.loads(requests.get(url + 'example_zips.geojson').text, encoding="latin-1")

# Extract of street data.
g = geoql.loads(requests.get(url + 'example_extract.geojson').text, encoding="latin-1")

g = g.properties_null_remove()\
     .tags_parse_str_to_dict()\
     .keep_by_property({"highway": {"$in": ["residential", "secondary", "tertiary"]}})
g = g.keep_within_radius((42.3551, -71.0656), 0.75, 'miles') # 0.75 miles from Boston Common.
g = g.keep_that_intersect(z) # Only those entries found in a Boston ZIP Code regions.
g = g.node_edge_graph() # Converted into a graph with nodes and edges.
g.dump(open('example_extract.geojson', 'w'))
open('leaflet.html', 'w').write(geoleaflet.html(g)) # Create visualization.An alternative example of usage is provided  below (the below usage is deprecated but will remain supported):import geojson
import geoql
import geoleaflet
import requests

url = 'https://raw.githubusercontent.com/Data-Mechanics/geoql/master/examples/'

# Boston ZIP Codes regions.
z = geojson.loads(requests.get(url + 'example_zips.geojson').text, encoding="latin-1")

# Extract of street data.
g = geojson.loads(requests.get(url + 'example_extract.geojson').text, encoding="latin-1")

g = geoql.features_properties_null_remove(g)
g = geoql.features_tags_parse_str_to_dict(g)
g = geoql.features_keep_by_property(g, {"highway": {"$in": ["residential", "secondary", "tertiary"]}})
g = geoql.features_keep_within_radius(g, (42.3551, -71.0656), 0.75, 'miles') # Within 0.75 of Boston Common.
g = geoql.features_keep_intersecting_features(g, z) # Only those entries found in a Boston ZIP Code regions.
g = geoql.features_node_edge_graph(g) # Converted into a graph with nodes and edges.
open('example_extract.geojson', 'w').write(geojson.dumps(g))
open('leaflet.html', 'w').write(geoleaflet.html(g)) # Create visualization.
Package: geoquiz
Country Shape QuizName the country from its shape!!This is a Python quiz application that allows users to take a quiz with questions displayed alongside images.
Users can input their answers, and the application will provide feedback on whether the answers are correct or not.InstallationRequirementsPython (>= 3.10)Install using pipxpipxinstallgeoquizAccessing the AppTo access the app, open your terminal and type the following command:geoquizUsageStart QuizClick on the "Start Quiz" button to begin.Handling MistakesIf you make a mistake, you have two options:Click on the "Show Answer" button to reveal the correct answer.Choose not to show the correct answer.Choose Display OrderSelect the order in which countries should appear:Increasing DifficultyRandom OrderSet the Number of CountriesChoose the number of countries to appear in the quiz:All countries10 countriesEnter a custom number in the provided boxStart PlayingType the correct country names in the answer box.Minor spelling mistakes are forgiven.AcknowledgmentsAll the images are imported from geojson files obtained fromhttps://geojson-maps.ash.ms/,geoBoundariesandhttps://maps.princeton.edu/catalog/stanford-sn252ts8433Audio files used :https://freesound.org/people/Mudkip2016/sounds/423930/,https://freesound.org/people/Gronkjaer/sounds/554053/
Package: georapid
Geospatial Knowledge supporting Intelligence workflowsQuery broadcasted news worldwide and visualize them using spatial aggregations. This modern Python module represents an idiomatic client accessing theGeospatial Knowledge APIsbeing hosted onRapid API Hub.Why is it important?Geospatial Knowledge refers to semantic information about specific locations on the Earth's surface.
It includes location-enabled things - not strings - like physical features of the landscape, the location of cities or in general human activities, and their spatial distribution.
Various sources like satellite imagery, location-enabled datasets, and most important any location-enabled information in the context of Open Source Intelligence (OSINT) are used to create Geospatial Knowledge.
By analyzing location-enabled things an analyst is empowered to gain insights into various aspects of human activities.Next stepsPlease, check out theRapidAPI Account Creation and Management Guide.Start with theUsagesection for further information, including
how to install the Python module.Featuresgeoprotests APIQuery protests worldwide and visualize them using spatial aggregations.geoconflicts APIQuery armed conflict events worldwide and visualize them using spatial aggregations.geofires APIQuery wildfires worldwide and visualize them using spatial aggregations.geojoins APIJoins two spatially enabled feature collections based on their relative spatial locations.geodetic APIEnables various geodetic functions like buffers, points from distance and direction, points along path and wedge construction.Ready to useThe geoprotests and geofires services offer ready-to-use geospatial features representing broadcasted news related to various themes. The underlying serverless cloud-backend analyses raw geospatial locations of news articles provided by theGlobal Database of Events, Language and Tone (GDELT) Project.The geoconflicts service offer ready-to-use geospatial features representing armed conflicts since 2020-01-01. The underlying serverless cloud-backend analyses raw armed conflicts of theUpsalla Conflict Data Program (UCDP).Please cite:Hegre, Håvard, Mihai Croicu, Kristine Eck, and Stina Högbladh (July 2020)
Introducing the UCDP Candidate Events Dataset. Research & PoliticsEvery geospatial result support the GeoJSON and Esri FeatureSet format out of the box. All endpoints support a date parameter for filtering the geospatial features. For best sustainability, the serverless cloud-backend queries the articles from the knowledge graph and calculates the geospatial features on-the-fly. You can use these geospatial features to build various mapping and geospatial applications.
Package: georaptor
GeoRaptor: Python Geohash Compression ToolGeohash is a geocoding system invented by Gustavo Niemeyer and placed into the public domain. It is a hierarchical spatial data structure which subdivides space into buckets of grid shape, which is one of the many applications of what is known as a Z-order curve, and generally space-filling curves.Geohash creation for a polygon at a given precision level could result in a huge set of geohashes.GeoRaptorcreates the best combination of geohashes across various levels to represent a polygon, by starting from the highest level and iterating till the optimal blend is brewed. Result accuracy remains the same as that of the starting geohash level, but data size reduces considerably for large polygons, thereby improving speed and performance.Following is a sample of what georaptor doesInput:1096 geohashes at precision 6 for Singapore.Output:414 geohashes with a mix of precision 5 and 6.PerformanceInput sample size:18,992,425Output:220,375Total execution time:35.8013219833 secondsUsage$ georaptor <input> –output <output> –minlevel <minlevel> –maxlevel <maxlevel>Example$ georaptor sample.csvOR$ georaptor sample.csv –output sample_out.csv –minlevel 3 –maxlevel 4InstallationTo install georaptor, simply:$ pip install georaptorLicense:Licensed under the Apache License, Version 2.0.Copyright 2017 Ashwin Nair <https://www.linkedin.com/in/nairashwin7>

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.