Saravanan666/pypi-org huggingface dataset:
Package: geocode-sparcs
geocode_sparcsis a program for geocoding health data from New York State’sStatewide Planning and Research Cooperative System (SPARCS)with a local installation ofPelias. It might also be useful as a Pelias wrapper for other regions and datasets, but for now at least, the focus is on SPARCS. The various kinds of fallback logic and string munging the program implements are specifically to improve performance on SPARCS.Installgeocode_sparcsviapipwith the commandpip install geocode_sparcs. Python dependencies are automatically installed, but you’ll need to set up Pelias with the instructions forPelias on Docker. You can usethe provided Pelias project directory; just be sure to setDATA_DIRin.envto where you want to store all the data. Setting up Pelias with this configuration can take a few hours of downloading and processing.Once Pelias is up, you can geocode with the commandpython3-mgeocode_sparcs, passing in addresses to geocode through standard input. Each address should be aJSONobject on its own line (perJSON Lines) with the keysline1,city, andzip. The values should all be strings (evenzip) and are presumed to come from the columnsPAT_ADDR_LINE1,PAT_ADDR_CITY, andPAT_ADDR_ZIP5in aSPARCS_LOCATIONfile; it is also assumed that you already checked thatPAT_ADDR_STis equal toNYfor each case. Here’s an example (with addresses that aren’t actually from SPARCS, since that’s protected health information):$ echo '{"line1": "405 East 42nd St", "city": "New York", "zip": "10017"}' >>input.txt
$ echo '{"line1": "351 Northern Blvd", "city": "Albany", "zip": "12204"}' >>input.txt
$ python3 -m geocode_sparcs <input.txtThe output is also in JSON Lines. By default, the firstfeaturesresult from Pelias for each input is returned without further processing. Seepython3-mgeocode_sparcs--helpfor command-line options.
Package: geocodeus
geocodeusTheZipCachemodule from thegeocodeuspackage is a Python library that enables users to retrieve data for US zip codes, such as the latitude and longitude coordinates, city, state, county, and timezone information. This module leverages a caching mechanism to speed up data retrieval and optimize memory usage.To use theZipCachemodule, users must first import it using thefrom geocodeus import ZipCache statement. Once the module is imported, they can create an instance of theZipCacheclass with no arguments, which initializes the cache and makes it ready for use. To obtain data for a particular US zip code, users can then call thegetData()method with a zip code string as the argument. The method returns a dictionary that contains information about the specified zip code.The ZipCache module employs a caching mechanism that enhances performance and memory usage efficiency. When a user calls thegetData()method with a zip code, the module first checks whether the data for that code is already in the cache.Overall, theZipCachemodule from thegeocodeuspackage is a powerful and easy-to-use tool for obtaining data for US zip codes. With its efficient caching mechanism and reliable data sources, this module is a must-have for any Python developer working with US zip codes.InstallationYou can install geocodeus using pip:pipinstallgeocodeusUsageHere's an example of how to use Zip Cache:fromgeocodeusimportZipCachecache=ZipCache()# Get data for a zip codedata=cache.getData("96105")print(data)ContributingPull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: geocoding-api-extract
geocoding-api-extractThis package makes it easy to connect to theUS GeoCoding apiand extract data from a large set of addresses.  It batches up the request for querying the api and returns the results in a Pandas DataFrame as a result.  The US geocoding api does accept batches but it does not work unless you have more address details like zipcode.  This script allows you to look up address details using only street address, city, and state.Installpip install geocoding_api_extractUsageThe following code will retrieve a DataFrame with the following columns: ['address', 'state', 'county', 'tract', 'cent_lat', 'cent_lon','us_zip'].import geocoding_api_extract as geox

tmp_folder = "tmp"

addr_data = {'address': ['6021 CERVINUS RUN', "4008 VIVAS LN", "7612 CAYENNE LN"],
             'city': 'Austin',
             'state': 'TX'}

result = geox.extract_address_details(addr_data['address'], addr_data['city'], addr_data['state'],
                                      tmp_folder)

geox.remove_tmp_files('Austin', 'TX', 'tmp')I have also included a larger sample to try this out in the 'sample_data' folder.import pandas as pd

import geocoding_api_extract as geox

tmp_folder = "tmp"

df = pd.read_parquet('sample_data/addresses_sample.parquet.gzip')

addresses = df['address']
city = 'Austin'
state = 'TX'

result = geox.extract_address_details(addresses, city, state,
                                      tmp_folder)

geox.remove_tmp_files('Austin', 'TX', 'tmp')
Package: geo-com-cal
基于python3.5+pyside2-5.15.1实现的大地测量综合计算，使用LGPL 3.0协议，开放了geo和task类，Task类包含一个ui和Geo计算类，可以用方法show()出一个UI界面.对于高斯投影部分，对CRS75,CRS80以及克拉索夫斯基三个椭球精度达到0.001，对其他椭球，误差可至个位数，可以查带值公式重求X,Bf计算。
Package: geocommit
Geocommit allows you to attach geolocation information to your commits for
later analysis.Everything happens on your machine to protect your privacy, you you need to push the geo annotations explicitly if you want to share them with others.InstallationUse pip (or easy_install):pip install --upgrade geocommit(If you don’t have pip yet, useeasy_install pipto get it.)UsageGeo-enable a repository (installs a few hooks that enable keep track of your location for this repository):git geo setupAttach geo-information to HEAD:git geo noteFetch geolocation information from remote:git geo fetchFetch and merge geolocation information:git geo syncDevelopinggit clone https://github.com/peritus/geocommit.git
python bootstrap.py
./bin/buildoutCrazy ideasfoursquare integration3rd party service with badges:
* jetsetter badge (commits at five different airports)
* mountain badge (commits at over 4000ft altitude)geocommit + git-remote-couch + geocouch === awesome!git geolog foo..bar > foobar.kmlChrome/Safari extension to display google maps all over githubGeocommit data format (v1.0)We store a number of keys with values in git notes or hg commits.
There is a long and a short format. Both define a set of key/value
pairs in no particular order. The format version defines the allowed
keys.<version> is a version number of the format x.y
<key> is an alphanumeric lowercase identifier without spaces or other special characters except _ and -
<value> must not contain a linebreak, “,” or “;”The short format is:
geocommit(<version>): <key> <value>, …, <key> <value>;The long format is, terminated by an empty line:geocommit (<version)
<key>: <value>
…
<key>: <value>Version 1.0 of the format defines the keys:long (required) contains longitude value of a coordinate in WGS84lat (required) contains latitude value of a coordinate in WGS84src (required) contains the name of the data provider used to generate the geodataalt (optional) contains altitude in metresspeed (optional) speed in metres / seconddir (optional) direction of travelhacc (optional) horizontal accuracy of long/lat values in metresvacc (optional) vertical accuracy of altitude value in metres
Package: geo-coor
Longitude and latitude coordinate conversionINTRODUCTIONTWD97 <–> WGS84 經緯度座標轉換 (TWD97互轉WGS84)Installpip installgeo-coorUSAGEcmd:geo_coor "PATH OF YOUR SETTING.PY"NoteAbout the setting file you can reference inhere.get help:geo_coor-hDEMOREFERENCEhttps://wiki.openstreetmap.org/wiki/Zh-hant:key_formulas_and_constantshttp://140.121.160.124/GEO/%E5%BA%A7%E6%A8%99%E7%B3%BB%E7%B5%B1.pdfhttps://kknews.cc/zh-tw/news/gaqn49.htmlhttp://blog.minstrel.idv.tw/2004/05/twd67-twd97.htmlhttp://www.public.tw/prog/dannyer/exam_papers130130/Uploads/107%E9%90%B5%E8%B7%AF%E7%89%B9%E8%80%83-%E5%93%A1%E7%B4%9A-%E6%B8%AC%E9%87%8F%E5%AD%B8%E6%A6%82%E8%A6%81.pdfhttps://ctcourse.pccu.edu.tw/ezfiles/232/1232/attach/26/pta_45324_4949370_37141.pdf★http://lovegeo.blogspot.com/2015/08/l2_8.html
Package: geocoordinates
Geographic coordinates coding problemContained is my solution for the geographic coordinates coding problem. The Julia version of this code can be found athttps://github.com/kathesch/GeoCoordinates.InstallationRun the following in terminal to install geocoordinates with pippip install geocoordinatesRunningOpen a python REPLimport geocoordinates as gcgc.time_series_analysis()~%pythonPython3.10.7(main,Sep142022,22:38:23)[Clang14.0.0(clang-1400.0.29.102)]ondarwinType"help","copyright","credits"or"license"formoreinformation.>>>importgeocoordinatesasgc>>>gc.time_series_analysis()VelocityatUnixtime1532334000:[-995.91526875-2514.4388939855.92122005]m/sVelocityatUnixtime1532335268:[-3471.021283081760.25788787-4867.4760627]m/s
Package: geocortex-reporting-client
Geocortex Reporting Client for PythonThis Python library makes it easy to runGeocortex ReportingorGeocortex Printingjobs.RequirementsPython 3.6 or laterInstalling the packageThis package is published toPyPi, and can be installed usingpip:pipinstallgeocortex-reporting-clientGenerating a reportThe client exports arunasync function that will return a url to the report upon completion.fromgeocortex.reporting.clientimportrunurl=awaitrun("itemid",[...otherarguments])Argumentsitem_idis required. All other arguments are optional.ArgumentTypeDescriptionitem_idstrThe portal item ID of the Reporting or Printing item.portal_urlstrThe URL of the ArcGIS Portal instance to use. Defaults to ArcGIS Online:"https://www.arcgis.com"tokenstrThe Portal access token to be used to access secured resources. If not provided requests to secured resources will fail.culturestrThe culture to use for localization. For example"en-US".result_file_namestrThe name assigned to the output file. It is used as the suggested name when downloading the result.dpiintThe DPI to use when rendering a map print. Defaults to96.use_pollingboolWhenTrue, the job service will be polled periodically for results. WhenFalse, connect to the job service using WebSockets to listen for results. It's recommended to use WebSockets where possible. Defaults toFalse.**kwargs**anyOther parameters to pass to the job. These are commonly used to parameterize your template. For examplerun("itemid", FeatureIds=[1, 2, 3])DocumentationFindfurther documentation on the SDKon theGeocortex Developer Center
Package: geocube
Tool to convert geopandas vector data into rasterized xarray data.DocumentationStable:https://corteva.github.io/geocube/stable/Latest:https://corteva.github.io/geocube/latest/Bugs/QuestionsReport bugs/feature requests:https://github.com/corteva/geocube/issuesAsk questions:https://github.com/corteva/geocube/discussionsAsk developer questions:https://gitter.im/corteva–geocube/communityAsk questions from the GIS community:https://gis.stackexchange.com/questions/tagged/geocubeCreditsThis package was originally templated with withCookiecutter.
Package: geocurrency
GeocurrencyAPI service that providesList of countries according to ISO-3166List of currencies according to ISO-4217List of conversion rates fetched from BCE APIsCustom conversion rates support by keyList of units and Unit SystemsConversions from from and to currenciesConversion from and to unitsInstallPreparationset the following environment variablesVENV_DIR: virtualenv directoryGEOCURRENCY_SRC: Source directory for geocurrencyGEOCURRENCY_DB_NAME: database nameGEOCURRENCY_DB_USERNAME: database usernameGEOCURRENCY_DB_PASSWORD: database passwordDockerCreate a "packages" directory at the root of the project
Generate packages from "build.sh"Folder "deployment/docker" provides an easy to setup docker environment runningDjango appRedis cachePostgres backendnginx load balancerjust run "docker-compose up"Using packagesENV_NAME=<env_name>
PROJECT_NAME=<project_name>
PYTHON_VERSION=/usr/bin/python3 # use pypy :)
virtualenv -p $PYTHON_VERSION $ENV_NAME
cd $ENV_NAME
source bin/activate
pip install geocurrency
django-admin startproject $PROJECT_NAME
cp site_packages/geocurrency/core/settings.example.py $PROJECT_NAME/$PROJECT_NAME/settings.py
cp site_packages/geocurrency/core/urls.example.py $PROJECT_NAME/$PROJECT_NAME/urls.py
cd $PROJECT_NAMEAdapt settings and urls for your environment.UsageRoutesThis package provides a OpenAPI documentation. See urls.example.py for a setup example.AuthenticationThe app provides most services without authentication.
Authentication is required to store custom conversion rates and custom conversion units.
By default, the app is configured to allow authentication through an API token that can be generated for a user on the
Django admin site.API authentication can be achieved with an Authorization header header with value Token 
curl -H "Authorization: Token ".Language supportThe app supports translations for countries and units in 15 languages. More languages should be available soon.Fetch ratesThe app uses python-forex as a mecanism to fetch currency rates.
A django command is available to fetch rates from command line :
$ ./manage.py fetch_ratesAboutProject goalsWeb based services to convert units and currencies.
GeoCurrency is a portmanteau of the words "Geocoding" and "Currency" which where the main goals of the initial projectProject websiteA live version of this service is available athttps://api.geocurrency.me.LeadershipThis project is maintained by Frédéric Meuroufm@peabytes.me.
Package: geodantic
geodanticLightweight, type-safe and spec-conformingGeoJSONparsing and validation using Pydantic.Installationpip install geodanticExamplesParse and validate GeoJSON features with custom properties:importpydanticfromgeodanticimportFeature,Point# Use your own properties Pydantic models@pydantic.dataclasses.dataclassclassMyProperties:foo:strdata={"type":"Feature","geometry":{"type":"Point","coordinates":[1,2]},"properties":{"foo":"abc"},}# Parse and validate data into spec-conforming objectsparsed=Feature[Point,MyProperties](**data)"""Feature[Point, MyProperties](type=<GeoJSONObjectType.FEATURE: 'Feature'>,bbox=None,geometry=Point(type=<GeoJSONObjectType.POINT: 'Point'>,bbox=None,coordinates=(1.0, 2.0)),properties=MyProperties(foo='abc'),id=None)"""# Dump back into JSON using standard Pydantic methodsparsed.model_dump_json(exclude_unset=True,indent=2)"""{"type": "Feature","geometry": {"type": "Point","coordinates": [1.0,2.0]},"properties": {"foo": "abc"}}"""Parse objects of arbitrary types:fromgeodanticimportGeometryCollectiondata={"type":"GeometryCollection","geometries":[{"type":"Polygon","coordinates":[[[1,2],[3,4],[5,6],[1,2]]],"bbox":[1,2,3,4],},{"type":"GeometryCollection","geometries":[{"type":"Point","coordinates":[1,2]}],},],}# Parse any geometry typeparsed=GeometryCollection(**data)"""GeometryCollection(type=<GeoJSONObjectType.GEOMETRY_COLLECTION: 'GeometryCollection'>,bbox=None,geometries=[Polygon(type=<GeoJSONObjectType.POLYGON: 'Polygon'>,bbox=(1.0, 2.0, 3.0, 4.0),coordinates=[[(1.0, 2.0), (3.0, 4.0), (5.0, 6.0), (1.0, 2.0)]]),GeometryCollection(type=<GeoJSONObjectType.GEOMETRY_COLLECTION: 'GeometryCollection'>,bbox=None,geometries=[Point(type=<GeoJSONObjectType.POINT: 'Point'>,bbox=None,coordinates=(1.0, 2.0))])])"""ContributingSet up the project usingPoetry:poetry installFormat the code:make lintRun tests:make testCheck for typing and format issues:make check
Package: geodata
# Geodata
Geodata provides a placename lookup gazeteer utilizing a local database using the 20M entry geonames.org data.<a name=”features”></a>
## Features
* Builds a local sqlite3 database of geoname.org data.
* Provides powerful search routines
* Word search
* Wildcard search
* Phonetic search using Double Metaphone
* Search by feature type.  E.g. all mountains starting with Be in Germany[See User Guide Wiki for details](https://github.com/corb555/Geodata/wiki/User-Guide)
Package: geodata2pg
💿 批量导入 geojson  (shpfile todo) 到 postgresqlParams:db_config: {
"DB_USER": ""
"DB_PASSWORD": ""
"DB_HOST": ""
"DB_PORT": ""
"DB_NAME": ""
}static_path: 相对路径if_exists: 'fail' | 'replace' | 'append', geopandas 中的参数:rocket: 简单 Demofromgeodata2pgimportmainif__name__=='__main__':db_config={"DB_USER":"postgres","DB_PASSWORD":"1973","DB_HOST":"localhost","DB_PORT":"5433","DB_NAME":"mapbox-api"}path='./static'main(db_config,path,'replace')
Package: geo-data-br
# Geo-localized data in BrazilPython lib for easily dealing with Geo-localized Data in BrazilWe have implemented more than 200 dimensions of geolocalized data from www.atlasbrasil.org.brIt contains a detailed set of demographic information such as gender, age, income, education, inequality and many other interesting things.# Installation## Installing thegeo_data_brlibFirst install [geopandas](http://geopandas.org/install.html) using conda:conda install geopandasThen,pip3 install geo_data_br# Features## Getting data from list of pointsGiven a list of latitude and longitude points we can use the lib to retrieve the corresponding data.import geo_data_br.points
points_of_interest = [(-59.23352, -3.35030), (-60.17875, -3.27442)]
demographics_on_poi = geo_data_br.points.data_on_points(points_of_interest)
demographics_on_poi.gini
>>> 0    0.59
>>> 1    0.51
>>> Name: gini, dtype: float64demographics_on_poi.espvida
>>> 0    72.91
>>> 1    68.70
>>> Name: espvida, dtype: float64# Contributing
Package: geodataExtent
Use as CLI-ToolInstallUsageExamplesUse as moduleUse as CLI-ToolInstallpip install geodataExtentUsageextract-extent --helpgives:Options:
  --path TEXT  Path to Folder containing Geofiles
  -c, --clear  Clear screen before showing results
  -t, --time   execute time extraction for one file
  -s, --space  execute boundingbox extraction for one file
  -h, --hull   execute convex-hull extraction for one file
  --help       Show this message and exit.Those areonlyoptions, you do not have to use them. However, if you do not choos any of the execution flags(-t / -s / -h), the folderextraction will be triggered and gives you the spatial and temporal extent of each of your Geofiles within the chosen folder in addition to the full spatial and temporal extent of the folder.You are not limeted to choose only one option but all of them at once except for--help.If you do not use--path, the path will be prompted. That means it is a shortcut only.Examples$ extract-extent -t -s -h
Pleas enter path to Folder: <path>
Pleas enter filename: <filename>

Timeextent:
['1935/01/01 00:00:00 GMT+0', '2014/01/01 00:00:00 GMT+0', 365.253164556962]


Spatialextent:
[-179.5, -89.5, 179.5, 89.5]


Spatialextent as Convex Hull:
[(-179.5, -89.5), (-179.5, 89.5), (179.5, 89.5), (179.5, -89.5)]The Timeextent starts with the beginning and ends with the end date as ISO8601 standard. the last number is the average intervall in which measurements have been taken.The spatial extent is shown as a boundingbox.[minX/minLong, minY/minLat, maxX/maxLong, maxY/maxLat]For more percission the-h / --hullflag gives you the spatial exnent as a convex hull. That means from all the points of a dataset the outer most points are beeing calculated and returned in correct order.FolderextractionIf you want to extract your hole folder, the-c / --clearflag is recommended because a long list of processing outputs is generated before the final output appears.$ extract-extent -c --path "<folder path>"Use as module
Package: geodataflow.api
geodataflow.apiGeodataFlow WebAPI component usingFastAPIwhich provides access to GeodataFlow backend via API REST calls.GeodataFlowis a Geoprocessing framework for fetching, translating and manipulating Geospatial data (Raster,Vector,EO/STAC collections) by using aPipelineor sequence of operations on input data. It is very much like theGDALlibrary which handles raster and vector data.The project is split up into several namespace packages.geodataflow.apiimplements a WEB API endpoint conforming toOpenAPIspecification with methods to get metadata, query states and run workflows. This package includes thegeodataflow.coresubpackage.Installationgeodataflow.apijust provides theFastAPIapp, you need to install one of available backends in order to have a complete list of useful modules.Using pypiTo install the latest stable version frompypi, write this in the command-line:>pipinstallgeodataflow.api[spatial,dataframes,eodag,gee]Optional backends:spatialInstalls thegeodataflow.spatialbackend implementation for GeodataFlow usingGDAL/OGR.dataframesInstalls thegeodataflow.dataframesbackend implementation for GeodataFlow usingGeopandas.Optional extras:eodagEODAG -Earth Observation Data Access Gatewayis a Python package for searching and downloading remotely sensed images while offering an unified API for data access regardless of the data provider.Installing this extraEODAGadds access to more EO Products from different providers toEOProductCatalogandEOProductDatasetmodules.geeGEE -Google Earth Engine APIis a geospatial processing service. WithEarth Engine, you can perform geospatial processing at scale, powered by Google Cloud Platform.GEErequires authentication, please, read available documentationhere.Installing this extraGEEmakes possible the access to Google Cloud Platform toGEEProductCatalogandGEEProductDatasetmodules.Using docker-composedocker-compose.ymlbuilds images and starts  GeodataFlow API and Workbench components to easily run Workflows withGeodataFlow.PACKAGE_WITH_GEODATAFLOW_PIPELINE_CONTEXTin the yml file indicates the backend implementation to load. The default value isgeodataflow.spatial. If you prefer to use another backend, please, change it before starting.Write in the command-line from the root folder of the project:>docker-composeupThen, type in your favorite Web Browser:http://localhost:9630/docsto check the interface of the REST WebAPI service.http://localhost:9640/workbench.htmlto access to the Workbench UI designer and where you can design and run Workflows!To remove all resources:>docker-composedown--rmiall-v--remove-orphansContributeHave you spotted a typo in our documentation? Have you observed a bug while runningGeodataFlow? Do you have a suggestion for a new feature?Don't hesitate and open an issue or submit a pull request, contributions are most welcome!LicenseGeodataFlowis licensed under Apache License v2.0.
SeeLICENSEfile for details.CreditsGeodataFlowis built on top of amazingly useful open source projects. SeeNOTICEfile for details about those projects
and their licenses.Thank you to all the authors of these projects!
Package: geodataset
geodatasetExtension of netCDF4.Dataset for geospatial data.InstallationInstall requirements with conda:conda env create -f environment.ymlActivate the environment:conda activate geodatasetInstall geodataset using pip:pip install geodatasetUsageOpen netCDF file for input and access geo- metadatafromgeodataset.toolsimportopen_netcdfn=open_netcdf('netcdf_file.nc')print(n.projection)print(n.get_bbox())print(n.get_lonlat_arrays())
Package: geodatatool
geodatatoolPython package for geographic data toolsFree software: MIT licenseDocumentation:https://clancygeodata.github.io/geodatatoolFeaturesCreate basic interactive maps in Jupyter NotebooksAdd files to maps in shapefile or geoJSON formatLoad and display visual media in Jupyter NotebooksCreditsThis package was created withCookiecutterand thegiswqs/pypackageproject template.
Package: geo-data-utils
Geology Data Utils v1.1.1Getting Startedinstall from pypipip install geo-data-utilsInstall direct from gitpip install git+https://gitlab.com/csiro-geoanalytics/python-shared/geo-data-utils.gitOr clone the repo so you can make changes or view the code, note the -e in the pip install is optional, it makes it so any code changes are automatically picked up.git clone https://gitlab.com/csiro-geoanalytics/python-shared/geo-data-utils.git
<Navigate to the code directory>
pip install -e .If you would like to work on the code in a virtual env a pipfile is availablepip install pipenv
pipenv install
pipenv shellUsageOnce it's installed use the command 'geo-data-utils'TODO: more info