Saravanan666/pypi-org huggingface dataset:
Package: geolambert
geolambert-pythongeolambert-python is a Python library and provides functions to covert
geographical corrdinates from LambertIIe/Lambert93 systems to WGS84 using
IGN Algorithms. C codes (lambert.c,lambert.h) come fromyageek/lambert.Website:https://github.com/chengs/geolambert-python/Usage>>> from geolambert import lambert93_to_wgs84, lambertIIe_to_wgs84
>>> lambert93_to_wgs84(668832.5384, 6950138.7285)
(2.5686536326051743, 49.649610985851474)
>>> lambertIIe_to_wgs84(73150, 2396665)
(-4.780198557157784, 48.354052821116824)InstallationDependenciesgeolambert-python requires:Python (>= 2.7 or >= 3.3)For development, cython>=0.26.1 is required.User installationTo install geolambert-python usingpippip install geolambertTo install geolambert-python from sourcegit clone https://github.com/chengs/geolambert-python/
cd geolambert-python
python setup.py installor install with compling codes usingCythonpython setup.py install --cythonAlternativesC++:https://github.com/yageek/lambertJave:https://github.com/yageek/lambert-javaPython:https://github.com/yageek/lambert-pythonPython:https://pypi.python.org/pypi/pyproj+ PROJ.4
Package: geolang
geolangis Simple Georgian Language ToolKit for Python 3Installation RequirementsPython >= 3.6To install the source, download it fromhttps://github.com/Lh4cKg/simple-geolang-toolkitand dopython setup.py install.A simple way to installgeolangpackage:$ pip install geolangTo install the sourcegeolangpackage:$ git clone https://github.com/Lh4cKg/simple-geolang-toolkit "geolang"
$ cd geolang
$ python3 setup.py build
$ python3 setup.py installUsageTo usegeolang, just import it in your project like so:>>> from geolang import encode_slugify, encode_textYou can convert the Latin words in Georgian>>> txt = "i love you python and django"
>>> encode_text(txt)
'ი ლოვე yოუ ფyტჰონ ანდ დჯანგო'You can convert the Georgian words in Latin>>> txt = "მე მიყვარს ანი"
>>> encode_text(txt)
'me miqvars ani'>>> txt = "ი ლოვე ყოუ პყტჰონ ანდ დჯანგო"
>>> encode_slugify(txt)
'ი-ლოვე-ყოუ-პყტჰონ-ანდ-დჯანგო'
>>> encode_slugify(txt)
'i-love-qou-pqthon-and-django'
>>> txt = "é\jcàé\jcàétéétéé\jéé\jcàété\jcàétécàété"
>>> encode_slugify(_try_encode_slugify_1)
'éjcàéjcàétéétééjééjcàétéjcàétécàété'See moretestsin this project.Source CodeThe source code can be found ongithub.ContributingThere are plenty of ways to contribute to this project. If you think you’ve found a bug please submit anissue.Licensegeolangis distributed under MITlicense.
Package: geolia
GeoliaGeometry and mesh toolsGeometry and mesh definition helpers--- Using the Gmsh Python APIRead meshes--- Keeping track of physical domains withmeshioInterpolation--- From unstructured to structured grids.DocumentationSee the website with API reference and some examples atbenvial.gitlab.io/geolia.InstallationFrom PypiSimply runpipinstallgeoliaFrom sourceClone the repositorygitclonehttps://gitlab.com/benvial/geolia.gitInstall the package locallycdgeolia
pipinstall-e.From gitlabpipinstall-egit+https://gitlab.com/benvial/geolia.git#egg=geolia
Package: geolib
GeolibA python library for geohash encoding, decoding and finding neighbour cells. This is a python port ofChris Veness' javascript implementation.Wikipedia referenceInstallationpipenv install geoliborpip install geolibUsagefrom geolib import geohashEncode a latitude, longtiude to geohashgeohash.encode(latitude, longitude, precision)
>>> geohash.encode('70.2995', '-27.9993', 7)
>>> gkkpfveDecode a geohash to latitude, longitudegeohash.decode(geohash), returns latitude, longitude as tuple of decimals
>>> geohash.decode('gkkpfve')
>>> (70.2995, -27.9993)Find neighbouring cells of a geohashgeohash.neighbours(geohash) 
retuns a namedtuple (n, ne, e, se, s, sw, w, nw)    

>>> neighbours = geohash.neighbours('gcpuyph')
>>> neighbours
>>> ('gcpuypk', 'gcpuypm', 'gcpuypj', 'gcpuynv', 'gcpuynu', 'gcpuyng', 'gcpuyp5', 'gcpuyp7')
>>> neighbours.ne
>>> gcpuypmFind adjacent cell in a given directiongeohash.adjacent(geohash, direction)
>>> geohash.adjacent('gcpuyph', 'n')
>>> gcpuypkFind SW/NE latitude/longitude bounds of a geohashgeohash.bounds(geohash)
returns a namedtuple ((sw_lat, sw_lon), ((ne_lat, ne_lon))
>>> bounds = geohash.bounds('ezs42')
>>> bounds
>>> ((42.583, -5.625), (42.627, -5.58)))
>>> bounds.sw.lat 
>>> 42.583DocumentationGeolib readthedocs
Package: geolibs
A wrapper library that integrates GeoEngine and MMLab libraries for GeoSpatial ML development.Installationpipinstalltorchtorchvisionpipinstall-Uopenmimmiminstall"mmengine>=0.6.0"miminstall"mmcv>=2.0.0rc4,<2.1.0"miminstall"mmdet>=3.0.0rc6,<3.1.0"miminstall"mmyolo"miminstall"mmsegmentation>=1.0.0"miminstall"mmpretrain>=1.0.0rc7"pipinstallgeolibsLicenseGPLv3DocumentationView documentationhere<https://engine.granular.ai/>_
Package: geolibs-cartoasync
GeoLibs-CARTOasyncAsynchronous Python client for CARTO.FeaturesSQL APIBatch APICOPY queriesImport APIRead and write Panda's DataFramesMaps API?TestsInstallationpipinstallcartoasyncUsageSQL API examplefromcartoasyncimportAuth,SQLClientauth=Auth(username='username',api_key='api_key')sql_client=SQLClient(auth)result=awaitsql_client.send('SELECT 1 AS one;')print(result)>>>{>>>"rows":[>>>{>>>"one":1>>>}>>>],>>>"time":0.002,>>>"fields":{>>>"one":{>>>"type":"number">>>}>>>},>>>"total_rows":1>>>}SQL API example, step by step1. Instantiate anAuthobject:1.1. CARTO cloud:auth=Auth(username='username',api_key='api_key')1.2. CARTO OnPremises or cloud organization with an implict user:auth=Auth(base_url='https://myapp.com/user/username/',api_key='api_key')1.3. CARTO OnPremises or cloud organization without an implicit user:auth=Auth(base_url='https://myapp.com/',username='username',api_key='api_key')1.4. SSL:TheAuthconstructor has andsslattribute. You can use it for handle to the library aPython's SSL context, or set it toFalsefor relaxing certification checks. More info onAIOHTTP doc.2. Instantiate the SQLClient and send queries:2.1. SQLClient's own AIOHTTP session:sql_client=SQLClient(auth)result=awaitsql_client.send('SELECT 1 AS one;')2.2. External AIOHTTP session you need to care until the end of its days:importaiohttpsql_client=SQLClient(auth)result=awaitsql_client.send('SELECT 1 AS one;',aiohttp.ClientSession())
Package: geolibs-dator
GeoLibs-DatorDator, a data extractor (ETL as a library), that uses Pandas' DataFrames as in memory temporal storage.FeaturesSourceExtractTransformLoadBigQueryYYCARTOYYY*CSVYYPandasYPostgreSQLYYY* Note:We are waiting for the append feature onCARTOframes, because the one we are using is añapa.ConfigurationCreate aconfig.ymlfile using theconfig.example.ymlone as guide. You can find in that one all the possible ETL cases.If you are using BigQuery in your ETL process, you need to add aGOOGLE_APPLICATION_CREDENTIALSenvironment variable with the path to your Google Cloud'scredentials.jsonfile.You can test them with theexample.pyfile.Exampledator_config.ymldatastorages:
  bigquery_input:
    type: bigquery
    data:
      query: SELECT * FROM `dataset.table` WHERE updated_at >= '2019-05-04T00:00:00Z' AND updated_at < '2019-06-01T00:00:00Z';

  carto_input:
    type: carto
    credentials:
      url: https://domain.com/user/user/
      api_key: api_key
    data:
      table: table

  postgresql_input:
    credentials:
      ...
    data:
      query: SELECT * FROM somewhere;
      types:
        - name: timeinstant
          type: datetime
        - name: fillinglevel
          type: float
        - name: temperature
          type: int
        - name: category
          type: str

  carto_output:
    type: carto
    credentials:
      url: https://domain.com/user/user/
      api_key: api_key
    data:
      table: table
      append: false

transformations:
  bigquery_agg:
    type: bigquery
    time:
      field: updated_at
      start: "2019-05-02T00:00:00Z"  # As string or YAML will parse them as DateTimes
      finish: "2019-05-03T00:00:00Z"
      step: 5 MINUTE
    aggregate:
      by:
        - container_id
        - updated_at
      fields:
        field_0: avg
        field_1: max

extract: bigquery_input
transform: bigquery_agg
load: carto_outputHow to useThis package is designed to accomplish ETL operations in three steps:ExtractThe extract method is a default method, this means although this method can be overwritten, by default, it must work via config.(This section under construction)Transform(This section under construction)LoadThe load method is a default method, this means although this method can be overwritten, by default, it must work via config. It can receive 2 parameters, the Pandas dataframe and a dictionary with extra info.Exampleapp.pyfromdatorimportDatordator=Dator('/usr/src/app/dator_config.yml')df=dator.extract()df=dator.transform(df)dator.load(df)app.pywith extra infofromdatorimportDatordefupsert_method:passdator=Dator('/usr/src/app/dator_config.yml')df=dator.extract()df=dator.transform(df)dator.load(df,{'method':upsert_method})TODOsBetter doc.Tests.
Package: geolidar
# PythonPackageStructure### General InstructionYour datasets should be stored in the data folderYour scripts should be stored in the script folderYour notebooks should be stored in the notebook folderYour models should be stored in the model folderYour test files/scripts should be stored in the tests folderAll travis yml files should be stored in the root folderAny other files should be placed in the appropriate folder or directory
Package: geolink2oereb
OEREBlex geoLink Formatter|||||||||This is a small library, meant to be used in combination with OEREBlex. It usesgeolink_formatterandpyramid_oerebas proven libs to handle
configuration and connection to ÖREBlex. On top, it provides a decent way to transform received ÖREBlex
documents into the OeREBKRMtrsfr structure objects for further handling.For detailed information, please refer to the onlinedocumentation.Changelog1.0.0Supported GEO-Link API version: 20170214 (v1.0.0)Initial version
Package: geolinks
geolinksUtilities to deal with geospatial links.  Working implementation
of the Cat-Interop work athttps://github.com/OSGeo/Cat-InteropInstallationgeolinks is best installed and used within a Python virtualenv.RequirementsPython 3 and abovePythonvirtualenvpackageDependenciesDependencies are listed inrequirements.txt. Dependencies
are automatically installed during geolinks's installation.Installing the Packagefrom source:python3-mvenvmy-envcdmy-env
.bin/activate
gitclonehttps://github.com/geopython/geolinks.gitcdgeolinks
python3setup.pybuild
python3setup.pyinstallvia pip:pip3installgeolinksRunninggeolinkslinksniff'http://host/wms?service=WMS'Using the API from Python>>>fromgeolinksimportsniff_link>>>sniff_link('http://host/wms?service=WMS')'OGC:WMS'>>>sniff_link('http://host/wms?service=WPS')'OGC:WPS'>>>sniff_link('http://host/wms?service=CSW')'OGC:CSW'>>>sniff_link('http://host/data/roads.kmz')'OGC:KML'>>>sniff_link('http://host/data/roads.kml')'OGC:KML'Running Tests# via setuptoolspython3setup.pytest# manuallycdtests
python3run_tests.pyDevelopmentSetting up a Development EnvironmentSame as installing a package.  Use a virtualenv.  Also install developer
requirements:pip3install-rrequirements-dev.txtReleasingvigeolinks/__init__.py
gitcommit-m'update release version'geolinks/__init__.py
videbian/changelog# add changelog entry and summary of updates# push changesgitpushoriginmaster
gittag-ax.y.z-m'tagging release x.y.z'# push taggitpush--tags
rm-frbuilddist*.egg-info
python3setup.pysdistbdist_wheel--universal
twineuploaddist/*Code ConventionsPEP8Bugs and IssuesAll bugs, enhancements and issues are managed onGitHub.ContactTom Kralidis
Package: geolocate
GeolocateGeoreferencing large amounts of data for free.Special thanks to @brunodepauloalmeida and the whole team for the contributions.How?It's using the very same API that Waze uses to georeference addresses before
it finds the best route to that destination. It requires no API keys, works
really well and has fairly high throughput.In order to make this package extensible, there's an abstract classGeolocateEnginethat defines the interface for the engines. This allows
for the addition of new engines without having to modify the code.How do I use it?First you have to install thegeolocatepackage for Python 3.7+:pip3 install geolocateThen, for a single address:>>>fromgeolocateimportgeolocate>>>geolocate("1 Infinite Loop, Cupertino, CA 95014"){'latitude':37.3311841,'longitude':-122.0287127}Or, if you want to run things in parallel:>>>fromgeolocateimportgeolocate_batch>>>geolocate_batch(["1 Infinite Loop, Cupertino, CA 95014","Eiffel Tower"])100%|███████|2/2[00:01<00:00,1.66it/s][{'latitude':37.3311841,'longitude':-122.0287127},{'latitude':48.8560934,'longitude':2.2930458}]Advanced usageBothgeolocateandgeolocate_batchaccept the following keyword arguments:engine (geolocate.engines.GeolocateEngine): Engine to use for
geolocating the address. Defaults togeolocate.engines.WazeEnginetimeout (int): The timeout in seconds.tries (int): The number of attempts to geolocate the address.backoff_factor (float): The backoff factor. Delay will grow by{backoff factor} * (2 ** ({number of total retries} - 1)).on_not_found (str or callable): A callback function for when the
address is not found. The signature of the callback function
should be:defcallback(address:str):...whereaddressis the address that was not found. The return
value of the callback function is returned by the geolocate
function.on_error (str or callable): A callback function for when an error
occurs. The signature of the callback function should be:defcallback(address:str,error:Exception):...whereaddressis the address that caused the error anderroris the exception that occurred. The return value of
the callback function is returned by the geolocate function.In addition, thegeolocate_batchfunction accepts the following
keyword arguments:num_cpus (int): The number of CPUs to use. If None, the number of
CPUs will be determined automatically.
Package: geolocator
The geolocator library provides a common interface to location-related data
from various sources and provides means to calculate distances between places.
Currently the MaxMind country & city geoip data sources are supported. There is
also some preliminary support for using GNS (GEOnet Names Server )data. For
distance calculation, the Haversine function is used.The library can be useful for example to implement location-based features in
different www frameworks; for an example of that, see the GeoLocation Zope /
CMF / Plone package from which this library was spun off. Please note, however,
that there is nothing www-specific in the library, nor does it have any
external dependencies.If you use this library, please send me email; I would like to add links to
the package home page to point to other software using this library.
Package: geolog
geologMy senior year GEOG 510 project. This is a package which uses functionality from ipyleaflet and will add more unique functions over the course of the semester.Free software: MIT licenseDocumentation:https://whitson-buck.github.io/geologFeaturesadd_draw_controls() to create shapes on your mapmore to come.CreditsThis package was created withCookiecutterand thegiswqs/pypackageproject template.
Package: geoluminate
GeoluminateDocumentationThe full documentation is athttps://geoluminate.github.io/geoluminate/QuickstartInstall Geoluminate::pip install geoluminateAdd it to yourINSTALLED_APPS:INSTALLED_APPS = (
    ...
    'geoluminate',
    ...
)Add Geoluminate's URL patterns:urlpatterns = [
    ...
    path('', include("geoluminate.urls")),
    ...
]FeaturesTODORunning TestsDoes the code actually work?source <YOURVIRTUALENV>/bin/activate
(myenv) $ pip install tox
(myenv) $ toxDevelopment commandspip install -r requirements_dev.txt
invoke -lCredits
Package: geolysis
Project LinksHomepagePyPiBug ReportsDiscussions[!IMPORTANT]
Project documentation is underwayTable of ContentsWhat is geolysis?InstallationSoil Classification ExampleAASHTO ClassificationUSCS ClassificationRelease HistoryCode of ConductContributingLicenseContact InformationWhat is geolysis?geolysisis an open-source software for geotechnical analysis and modeling.
It provides features such assoil classifications(based on the USCS and AASHTO classification standards),estimating soil bearing capacityusing SPT N-value, andestimating of soil engineering parameterssuch asSoil Unit Weight(moist, saturated, and submerged),Compression index,soil internal angle of friction, andundrained shear strength of soil.Features to include in upcoming versions:Settlement analysisDiscrete element method (DEM)The motivation behindgeolysisis to provide free software to assist
geotechnical engineers in their day-to-day work and to expose civil
engineering students (especially geotechnical students) to tools that
can make them industry-ready geotechnical engineers right from college.InstallationpipinstallgeolysisSoil Classification ExampleAASHTO Classification>>>fromgeolysis.soil_classifierimportAASHTO>>>aashto_cls=AASHTO(liquid_limit=30.2,plasticity_index=6.3,fines=11.18)>>>aashto_cls.soil_class'A-2-4(0)'>>>aashto_cls.soil_desc'Silty or clayey gravel and sand'USCS Classification>>>fromgeolysis.soil_classifierimportUSCS>>>uscs_cls=USCS(liquid_limit=34.1,plastic_limit=21.1,fines=47.88,...sand=37.84,gravel=14.8)>>>uscs_cls.soil_class'SC'>>>uscs_cls.soil_desc'Clayey sands'>>>uscs_cls=USCS(liquid_limit=30.8,plastic_limit=20.7,fines=10.29,...sand=81.89,gravel=7.83,d_10=0.07,d_30=0.3,d_60=0.8)>>>uscs_cls.soil_class'SW-SC'>>>uscs_cls.soil_desc'Well graded sand with clay'Release HistoryCheck thechangelogfor release history.Code of ConductThis project has acode of conductthat we
expect all contributors to adhere to. Please read and follow it
when participating in this project.ContributingIf you would like to contribute to this project, please read thecontributing guidelinesLicenseDistributed under theMITlicense. By using,
distributing, or contributing to this project, you agree to the
terms and conditions of this license.Contact InformationLinkedIn[!IMPORTANT]
For questions or comments aboutgeolysis, please ask them in thediscussions forum
Package: geomac-wildfires
Download wildfires data from GeoMACInstallation$ pipenv install geomac-wildfiresCommand-line usageUsage: geomacwildfires [OPTIONS] COMMAND [ARGS]...

  A command-line interface for downloading wildfire perimeter data from
  GeoMAC. Returns GeoJSON.

Options:
  --help  Show this message and exit.

Commands:
  active-perimeters   Perimeters of active fires in a recent 24-hour period
  all-perimeters      Perimeters of all fires
  nifc-incidents      Fire incident points from NIFC situation reportsDownload data from GeoMAC.$ geomacwildfires active-perimeters
$ geomacwildfires all-perimeters
$ geomacwildfires get-nifc-incidentsPython usageImport the library.>>> import geomac_wildfires
>>> data = geomac_wildfires.get_active_perimeters()
>>> data = geomac_wildfires.get_all_perimeters()
>>> data = geomac_wildfires.get_nifc_incidents()ContributingInstall dependencies for development.$ pipenv install --devRun tests.:$ make testShipping new version to PyPI.$ make shipDeveloping the CLIThe command-line interface is implemented using Click and setuptools. To install it locally for development inside your virtual environment, run the following installation command, asprescribed by the Click documentation.$ pip install --editable .
Package: geomagdata
Geomagnetic Data IndicesGeomagnetic indices downloader and parser, returns Ap, F10.7 (unsmoothed and smoothed) and Kp.This is derived fromgeomagindices, and has been modified to:Support the newpost-SWPC data sources for all data dating back to 1932.Fix a bug where averaging would not cross year boundaries.It is a drop-in replacement forgeomagindices.Output datatype ispandas.DataFrame(for multiple times)internally, usespandas.Index.get_locto find nearest time to request.Missing data is returned asNaN(Not a Number floating point value).Installation$gitclonehttps://github.com/sunipkm/geomagdata
$cdgeomagdata
$pipinstall.Examplesuse from other programs likeimportgeomagdataasgiinds=gi.get_indices(date)where date is Pythondatetime.date, datetime.datetime, etc.pythonExamples/PlotIndices.py2015-01-012016-01-01
Package: geomagindices
Geomagnetic IndicesGeomagnetic indices downloader and parser, returns Ap, F10.7 (unsmoothed and smoothed) and Kp.
Let us know via GitHub Issue if something is missing.Output datatype ispandas.DataFrame(for multiple times)Missing data is returned asNaN(Not a Number floating point value).Examplesuse from other programs likeimportgeomagindicesasgiinds=gi.get_indices(datetime)where date is Pythondatetime.datetime, etc.pythonExamples/PlotIndices.py2015-01-012016-01-01NotesWe should add readers for the new post-SWPC data sources, from 2018 onward as noted at:https://www.celestrak.com/SpaceData/SpaceWx-format.phpKp, Apf10.7Let us know via GitHub Issue if you want this new data.
Package: geomagnetism
Product NameToolbox of utilities to compute the geomagmetic field.InstallationRun the following to install:pipinstallgeomagnetismUsage examplerefer togeomagnetism-exemples.Release History0.0.2 correct pole ambiguity by using limitig value of associated Legendre polynomial
0.0.3 create geomagnetic field cartography using PlateCarree representation. 'Miller' would be better)
0.0.9 plot the iso values of the field. Support Millet, stereographic an Lambert projectionMeta- François Bertin– francois.bertin7@wanadoo.frDistributed under theMIT licenseAbout the authors- François Bertin now retired was an expert senior in nanocharacterization at CEA-LETI
Package: geomap
Generate a map of geolocationsExampleTo use as a library.>>> import geomap
>>> m = geomap.Map(args.projection)
>>> lon =[37.7833, ...]
>>> lat = [-122.4167, ...]
>>> m.add_coordinates(lon, lat)
# to show the map
>>> m.generate_map()
# to save to a file
>>> m.generate_map(output='map.png')To use as a standalone program.# to show the map
$ geo-map coordinates.txt
# to save to a file
$ geo-map coordinates.txt -o map.pdfThe input is a comma separated values (CSV) file, the first value is the
longitude and the second values is the latitude.37.7833, -122.4167
42.3581, 71.0636
.
.
.InstallationTo install geomap, simply:$ pip install geomap
Package: geomapdemo
geomapdemoA Python package for interactive mapping.LogoFree software: MIT licenseDocumentation:https://zyang91.github.io/geomapdemoFeaturesCreate random numbers and random textBasic Mapping and drawingVector and raster layer functions availableIpyleaflet-based interactive map functionFolium-based interactive map functionChart module for interactive chart-makingSupported Python VersionPython 3Python 3.7Python 3.8Python 3.9Python 3.10Python 3.11
Package: geo-map-harmonizer
Geo Map HarmonizerThis package includes an algorithm specialized in harmonizing geotiff maps.
Its main function is to generate a harmonized legend based on the spatial
distribution of classes in two maps, aiming for maximum accuracy in comparison.Additionally, the package provides data analysis functions for detailed
reports. Specific strategies for handling Big Data are implemented, such as
dividing maps into smaller blocks and consolidating results.Installation1 - InstallGDALon your system.
2 - InstallPython GDAL Library3 - Install Geo Map Harmonizer withpip install geo-map-harmonizer.UtilizationThe application uses a class to store the paths to the maps in .tif format,
and uses the map_legend_harmonizer method
to generate the legend harmonization for the two maps.fromgeomapharmonizerimportGeoMapHamonizergeomap=GeoMapHamonizer(path_map1="path/to/map1.tif",path_map2="path/to/map2.tif")df_legends=geomap.map_legend_harmonizer()
Package: geomapi
GeomAPIA joint API to standardize geomatic data storage and processing.
Developed by the Geomatics research group at the KU LeuvenInstallationUse the package managerpipto install geomapi.pipinstallgeomapiDocumentationYou can read the full API reference here:DocumentationQuickstartimportgeomapifromgeomapi.nodesimportNodenewNode=Node()ContributingThe master branch is protected and you can only make changes by submitting a merge request.
Please create a new branch if you would like to make changes and submit them for approval.LicensingThe code in this project is licensed under MIT license.