Saravanan666/pypi-org huggingface dataset:
Package: geotext
Geotext extracts country and city mentions from textFree software: MIT licenseDocumentation:https://geotext.readthedocs.org.UsagefromgeotextimportGeoTextplaces=GeoText("London is a great city")places.cities# "London"# filter by country coderesult=geotext.GeoText('I loved Rio de Janeiro and Havana','BR').cities# 'Rio de Janeiro'GeoText('New York, Texas, and also China').country_mentions# OrderedDict([(u'US', 2), (u'CN', 1)])Installationpipinstallhttps://github.com/elyase/geotext/archive/master.zipFeaturesNo external dependenciesFastData fromhttp://www.geonames.orglicensed under the Creative Commons Attribution 3.0 License.Similar projectsgeography: geography is more advanced and bigger in scope compared to geotext and can do everything geotext does. On the other hand geotext is leaner: has no external dependencies, is faster (re vs nltk) and also depends on libraries and data covered with more permissive licenses.History0.4.0 (2018-07-30)Fix unicode errors0.3.0 (2017-12-03)Support for Brazilian cities (credit to @joseluizcoe)0.2.0 (2017-07-01)Python 3 support (credit to @freezer9)0.1.0 (2014-01-11)First release on PyPI.
Package: geotexxx
gefxml_readerPackage om geotechnische sonderingen of boringen in GEF, BRO XML of SIKB0101 XML te lezen en visualiserenDependeciesZie environment.ymlInstructionInstalleer de package:pip install geotexxxofconda install -c conda-forge geotexxxImporteer de package:from geotexxx.gefxml_reader import Cpt, BoreMaak een leeg object:test = Cpt()oftest = Bore()Lees een bestand:test.load_gef(filename)oftest.load_xml(filename)Maak een plot in map ./output:test.plot()gefxml_viewerbiedt een grafische interface om sonderingen en boringen incl. eenvoudige labproeven te plotten.Complexe proevenBeschikbaar:korrelgrootteverdeling:figs = test.plot_korrelgrootte_verdelingen()samendrukkingsproeven:figs = test.plot_samendrukkingsproeven()Vragen of opmerkingen?Stuur een bericht aan Thomas van der Linden, bijvoorbeeld viaLinkedInResultaten?Heb je mooie resultaten gemaakt met deze applicatie? We vinden het heel leuk als je ze deelt (en Thomas tagt)
Package: geothmetic-meandian
Geothmetic MeandianPython package to provide the calculation of the "geothmetric median" as defined inXKCD # 2435. For when you aren't sure whether to use the mean, median, or geometric mean.Instalationpip3installgeothmetic_meandianExample Usage>>>fromgeothmetic_meandianimportgeothmetic_meandian>>>geothmetic_meandian([1,1,2,3,5])2.0889513309015815
Package: geotile
GeoTileGeoTile is an open-source python library for creating and manipulating the tiles of the raster dataset. The package will be very useful for managing the raster tiles which can be used for deep learning traing dataset.Full documentationThe complete documentation of this package is available here:https://geotile.readthedocs.io/en/latest/InstallationThe easy installation ofgeotileis by usingcondaenvironment,condainstall-cconda-forgegeotileIf you want to install it forpip, check the documentation here:https://geotile.readthedocs.io/en/latest/pages/install.htmlSome basic examplesPlease check the complete documentation here:https://geotile.readthedocs.io/en/latest/fromgeotileimportGeoTilegt=GeoTile(r"path/to/raster/data.tif")# to generate the tiles of rastergt.generate_tiles(r'/path/to/output/folder',tile_x=256,tile_y=256,stride_x=256,stride_y=256)# to generate the tiles of selected bands onlygt.generate_tiles(r'/path/to/output/folder',bands=[4,3,2],tile_x=256,tile_y=256,stride_x=256,stride_y=256)# to merge the tilesfromgeotileimportmosaic
mosaic('/path/to/input/folder/tiles',output_file='path/to/output/file.tif')# to generate the raster mask from shapefilegt.mask('/path/to/shapefile.shp','/path/to/output/file.tif')# to rasterize the shapefile based on column value,gt.rasterization(input_vector='path/to/shp.shp',out_path='path/to/output.tif'value_col="value_col")# to close the filegt.close()
Package: geotiler
GeoTiler is a library to create maps using tiles from a map provider.The main goal of the library is to enable a programmer to create maps using
tiles downloaded from OpenStreetMap, Stamen or other map provider.The maps can be used by interactive applications or to create data analysis
graphs.The code is based on Python port of Modest Maps project [1][2], which is
licensed under BSD license. All subsequent changes are licensed under GPL
v3 license.[1]http://modestmaps.com/[2]https://github.com/stamen/modestmaps-pyNoteVersion 0.20.0 of GeoTiler will change API. Please use:geotiler>=0.14.2,<0.20.0as your dependency requirement if you need old API.
Package: geo-tool
经纬度解析转换库pip安装pipinstallgeo_tool1.百度经纬度解析fromgeo_toolimportBaiduGeobaidu_map_key='xxxxxxxxxxxx'print(BaiduGeo(baidu_map_key).geo2address(22.52955,113.93078))print(BaiduGeo(baidu_map_key).get_city_name_by_geo(22.52955,113.93078))print(BaiduGeo(baidu_map_key).address2geo('北京市海淀区上地十街10号'))2.高德经纬度解析fromgeo_toolimportGaodeGeogaode_map_key='xxxxxxxx'print(GaodeGeo(gaode_map_key).geo2address(22.52955,113.93078))print(GaodeGeo(gaode_map_key).address2geo("广东省深圳市南山区"))print(GaodeGeo(gaode_map_key).get_city_name_by_geo(22.52955,113.93078))3.谷歌经纬度解析fromgeo_toolimportGoogelGeogoogle_key='xxxxxx'print(GoogelGeo(google_key).address2geo('深圳市南山区'))4.经纬度互相转换fromgeo_toolimportGeoTransformbaidu_lng='114.126496'baidu_lat='22.538926'print(GeoTransform(lng=baidu_lng,lat=baidu_lat).baidu2google())google_lng=114.12008298963123google_lat=22.53258746867191print(GeoTransform(lng=google_lng,lat=google_lat).google2baidu())
Package: geotoolkit
geotoolkitA lightweight python library for working with geographic coordinates. This library can be used in things such as drone navigation, speed measurement, and more.ExamplesMeasuring distance between two points:p1 = (33.93369079118477, -118.41900963926824)
p2 = (33.937366248549935, -118.38276824755901)
distance = distance(p1, p2)
print(distance) # 2.0952233910697995, in milesChecking if a point lies within (inside of) a polygon:raw_json:str = "[[34.04709656460684, -118.51420813970792], [34.0173210880725, -118.00731306657742], [33.92068010802825, -117.92185949106074], [33.695565897860064, -118.00828413031054], [33.693142098170746, -118.38020139809352]]" # load a polygon from a JSON array of arrays
polygon:list[tuple[float, float]] = load_polygon(raw_json)
point1:tuple[float, float] = (33.924709007544834, -118.22191807002923)
point2:tuple[float, float] = (33.88682983206357, -118.72492888526082)
print(inside_polygon(point1, polygon)) # True (it is within the polygon)
print(inside_polygon(point2, polygon)) # False (it is outside of the polygon)Measuring speed:mph = speed_mph((33.92533554514059, -118.32099118005947), (33.92532143656184, -118.31715080400728), 9.8)
print(mph) # 80.96760998067498 MPH
Package: geo-tools-py
地理位置计算安装 geo-tools-py
pip install geo-tools-py==1.0.0

链接:https://pypi.org/project/geo-tools-py/1.0.0/坐标点最近圆形半径搜索poi搜索1. 自建地图数据库(datahub.wtf)
        自建数据库，主要来自地图平台，另有美团，大众点评等其他服务类数据，最新最全poi数据
    2. 百度地图
    3. 高德地图from GeoData.POI import POI
    poi = POI() #请求自建数据
    print(poi.get_data_hub_pois(lat='12',lon='13',page=1,pageSize=10))
    #请求高德地图接口,请先补全key
    print(poi.get_gao_de_poi(key=None, locations='123.12,45.12',page=1))
    #请求百度地图接口,请先补全Key
    print(poi.get_baidu_poi(key=None, location='123.12,45.12',page=1))电子围栏（点与多边形的位置关系）1. 点射线法判断from GeoCal.Geometry import Geometry
    print(Geometry([]).point_intersect_line({"x":1,"y":1},{"x":3,"y":10},{"x":10,"y":0.2}))2. uber h3 判断多个经纬点与多个多边形匹配计算坐标点距离计算from GeoCal.LocDisDir import LocDisDir
    l = LocDisDir()
    l.geodistance(116.498079, 39.752304,117.498079, 39.752304)距离坐标点loc1,相距500m,与正东方向成30度角的坐标点计算from GeoCal.LocDisDir import LocDisDir
    l = LocDisDir()
    lat1,lon1 =39.752304,116.498079
    l.get_new_lng_angle(lng1, lat1, dist=500, angle=90)微信名片
Package: geo-track-analyzer
Track analyzerThe focus of this package lies on analyzing and visualizing tracks of cycling or similar activities. Depending on the usecase settings likestopped_speed_thresholdormax_speed_percentilemay not be appropriate.Installing the package withcliextra, I.e. usingpip install geo-track-analyzer[cli], add utility tools. See thedocumentationfor details.From filesTracks my be initialized from.gpxand.fitfiles using theGPXFileTrackandFITTrackobject, respectively.ProgrammaticallyYou can instanciate tracks programmatically inside your code using thePyTrackclass.PyTrack(points:list[tuple[float,float]]=...,elevations:None|list[float]=...,times:None|list[datetime]=...,heartrate:None|list[int]=None,cadence:None|list[int]=None,power:None|list[int]=None,)Extracting track dataThe data of the track can be extracted into a pandas DataFrame object with the columns:latitude: Track point latitude valuelongitude: Track point longitude valueelevation: Track point elevation valuespeed: Speed in m/s calculated relative to previous point. Requires time to be present in track.distance: Distance in m relative to previous pointheartrate: Heartrate in bpm (if present in input)cadence: Cadence in rmp(if present in input)power: Power in W (if present in input)time: Time in seconds relative to previous point. Time must be present in track.cum_time: Cummulated time of the track/segment in seconds.  Requires time to be present in track.cum_time_moving: Cummulated moving time of the track/segment in seconds.  Requires time to be present in track.cum_distance: Cummulated distance in track/segement in meters.cum_distance_moving:  Cummulated moving distance in track/segement in meters.cum_distance_stopped:  Cummulated stopped distance in track/segement in meters.moving: Bool flag specifing if thestopped_speed_thresholdwas exceeded for the point.Because some values are relative to previous points, the first point in the segment is not represented in this dataframe.Furthermore an summary of the segments and tracks can be generated in the form of aSegmentOverviewcontaining:Time in seconds (moving and totoal)Distance in meters and km (moving and totoal)Maximum and average velocity in m/s and km/hMaximum and minimum elevation in metersUphill and downhill elevation in metersVisualizing the trackVisualizations of a track can be generated via theplotmethod and thekindparameter. Seedocumentationfor further details and examples how to use the visualizations.
Package: geotran
The Python Geospatial Library, Geotran, is a comprehensive toolkit for geospatial data preprocessing, analysis, and modeling. It provides a variety of functions for transforming and manipulating geospatial data, including data normalization, resampling, filtering, and feature selection. It also offers a range of analytical techniques, such as spatial clustering, classification, and regression, as well as machine learning algorithms for predictive modeling.Geotran is designed to work with a variety of geospatial data formats, including shapefiles, GeoJSON, and raster data. It also supports integration with popular geospatial tools and platforms, such as QGIS, GDAL, and ArcGIS.Geotran is widely used in the geospatial industry, research, and open-source communities for developing geospatial applications, performing spatial analysis, and modeling complex geospatial phenomena.Example:from geotran import raster
from matplotlib import pyplot


fig, (axr, axg, axb) = pyplot.subplots(1,3, figsize=(21,7))

raster.plot('T60GVV.tif',bands=[3],ax=axr, title="Red", cmap='Reds')
raster.plot('T60GVV.tif',bands=[2],ax=axg, title="Green", cmap="Greens")
raster.plot('T60GVV.tif',bands=[1],ax=axb, title="Blue", cmap="Blues")
![Alt text](image.png)

input_raster_files = ['blue.tif', 'green.tif',
                      'red.tif', 'red-edge.tif', 'nir.tif']
output_raster_file = datetime.now().strftime("%d%m%Y%H%M%S") + "_stacked.tif"

stacked_file = stack_rasters(input_raster_files, output_raster_file, band_names=[
                             'blue', 'green', 'red', 'rededge', 'nir'])

clip_raster_by_shp(raster_file, shapefile, output_file, epsg_code=2193)

(more)
Package: geotrans
DocumentationPublished on ReadTheDocs:DocumentationFeaturesCommand line utility for easy transformations between geodata/spatial
filetypes.Python functions with documentation for direct usage.This is my own main use case: A package with all basic geopandas
file loads and saves bundled.Uses Python pathlib for cross-platform path handling.SupportCurrently supports:GeoPackagesEsri
ShapefilesFile
GeodatabasesRead onlyGeoJSONAll file formats supported by geopandas can be implemented.Dependenciesgeopandasfor
transforming between geodata filetypes which in turn usesfiona(that
usesGDAL).clickfor command line
integration.AlternativesTheGDALtoologr2ogris a
much more sophisticated command-line tool for converting between spatial
file formats.Geopandas by itself supports many more spatial file formats. For more
advanced use cases when interacting with Python I recommend just using
geopandas.Fiona provides a command-line interfacefio.fio.InstallationPyPipipinstallgeotranspoetry for developmentgitclonehttps://github.com/nialov/geotransform.gitcdgeotransformpoetryinstallUsing geotransformCommand lineRungeotrans--helpto print the command line help for the utility.To transform from a geopackage file with a single layer to an ESRI
shapefile:geotransinput_file.gpkg--to_typeshp--outputoutput_file.shpTo transform from a geopackage file with multiple layers to multiple
ESRI shapefiles into a given directory:geotransinput_file.gpkg--to_typeshp--outputoutput_dirPythonAll main functions in charge of loading and saving geodata files are
exposed in the transform.py file in the geotrans package.fromgeotrans.transformimportload_file,save_files,SHAPEFILE_DRIVERfrompathlibimportPath# Your geodata filefilepath=Path("input_file.gpkg")# load_file returns a single or multiple geodataframes depending# on how many layers are in the file.geodataframes,layer_names=load_file(filepath)# Assuming geopackage contained only one layer -># Save acquired geodataframe and layersave_files(geodataframes,layer_names,[Path("output_file.shp")],SHAPEFILE_DRIVER)LicenseThis project is licensed under the terms of theMIT
license.Copyright © 2020, Nikolas Ovaskainen.
Package: geotriangulate
geotriangulategeotriangulate is a Python library for finding object location by triangulation method.InstallationJust save geotriangulate.py to any convenient location and import itUsageimportgeotriangulate# Creates a new measurement with longitude lon, latitude lat, azimuth alphameas=Measurement(lon,lat,alpha)# Creates a new target objecttargobj=TargObj()''' Adds measurement meas of the target object. returns the id assigned to the measurement.If the new measurement does not provide any additional information, None is returned'''meas_id=targobj.AddMeasurement(meas)# Deletes the measurement by id. If there is no measurement with such an id, an error is throwntargobj.DeleteMeasurement(meas_id)# Access to the current average location of the objecttargobj.CurMedPosLicenseMIT
Package: geotribu
Geotribu CLIOutil en ligne de commande pour les tâches récurrentes du projet Geotribu.InstallerViapip:pipinstallgeotribuVia Docker :dockerpullghcr.io/geotribu/cliVia un exécutable pré-compilé :télécharger pour son système d'exploitation.UtiliserInstallation locale :geotribu--helpOu avec l'image Docker :dockerrun--rmghcr.io/geotribu/cli:latestgeotribu--helpPour plus d'informations,consulter la documentation.
Package: geourban
Geospatial Urban API ServiceDirect access to simulated spatially enabled traffic grids of urban regions. This modern Python module represents an idiomatic client accessing theGeospatial Urban APIbeing hosted onRapid API.Traffic grid accumulating the number of simulated agents moving using bikes through the city of Bonn between 07:00 and 08:00 AMSimulated agent positions moving by car within a distance of 250 meters of a highway cross road intersection being located at (50.746708, 7.074405) in the city of Bonn starting at 08:00 AM and lasting 30 secondsWhy is it important?The geospatial urban API solves a common challenge for urban digital twins – obtaining insights into mobility behavior of citizens using simulated traffic scenarios. Traffic planners use powerful tools in shaping urban mobility sustainable. But without having access to citizen movement profiles there is always a need for simulating, analyzing, testing and mitigating traffic scenarios.Next stepsPlease, check out theRapidAPI Account Creation and Management Guide.Start with theIntroductionsection for further information.Follow theGuidesor take a look at theUrban Traffic Bonnnotebook.Ready to useThe geospatial urban API offer ready-to-use geospatial features representing simulated spatially enabled traffic grids of urban regions.Every geospatial result support the GeoJSON and Esri FeatureSet format out of the box. For best sustainability, the serverless cloud-backend queries the geospatial features from a big data store. You can use these geospatial features to build various mapping and geospatial applications.
Package: geoutils
GeoUtils: consistent geospatial analysis in Python.GeoUtilsis an open source project to develop a core Python package for geospatial analysis and foster inter-operability between other Python GIS packages.It aims atfacilitating end-user geospatial analysis by revolving around consistentRasterandVectorobjectsthat effortlessly interface between
themselves. GeoUtils is founded onimplicit loading behaviour,robust numerical interfacingandconvenient object-based methodsto easily perform
the most common higher-level tasks needed by geospatial users.If you are looking for an accessible Python package to write the Python equivalent of yourGDALcommand lines, or of yourQGISanalysis pipelinewithout a steep learning curveon Python GIS syntax, GeoUtils is perfect for you! For more advanced
users, GeoUtils also aims at being efficient and scalable by supporting lazy loading and parallel computing (ongoing).GeoUtils relies onRasterio,GeoPandasandPyprojfor georeferenced
calculations, and onNumPyandXarrayfor numerical analysis. It allows easy access to
the functionalities of these packages through interfacing or composition, and quick inter-operability through object conversion.DocumentationFor a quick start, full feature description or search through the API, see GeoUtils' documentation at:https://geoutils.readthedocs.io.Installationmambainstall-cconda-forgegeoutilsSeemamba's documentationto installmamba, which will solve your environment much faster thanconda.Start contributingFork the repository, make a feature branch and push changes.When ready, submit a pull request from the feature branch of your fork toGlacioHack/geoutils:main.The PR will be reviewed by at least one maintainer, discussed, then merged.More info onour contributing page.
Package: geov
GeoVOverviewThe GeoV model was designed by Georges Harik and usesRotary Positional Embeddings with Relative distances (RoPER)byGeorges HarkandVaruna Jayasiri.RoPER, in addition to using relative positions in the attention score
calculation by RoPE embeddings, adds relative positional information explicitly to value embeddings.
Specifically, it incorporates the relative positions of the tokens paid attention to.
RoPER has given better performance in some algorithmic tasks, and seems comparable to RoPE in language modeling.The GeoV tokenizer usesSentencePieceunigram language modeland tokenizes symbols,
digits and new line characters separately, in order to achieve better performance on mathematical content and code.This model was contributed bygharikandvpj.We have shared 9B parameter pre-trained model atGeoV/GeoV-9b,
We plan to release checkpoints around every 20b tokens trained from here until around 300b tokens.
We will also train smaller and larger versions.
Our aim is to have broadly available smaller and larger models.This implementation is built on top oftransformerslibrary.InstallationspipinstallgeovGenerationfromgeovimportGeoVForCausalLM,GeoVTokenizermodel=GeoVForCausalLM.from_pretrained("GeoV/GeoV-9b")tokenizer=GeoVTokenizer.from_pretrained("GeoV/GeoV-9b")prompt="In mathematics, topology is the study of"input_ids=tokenizer(prompt,return_tensors="pt").input_idsgen_tokens=model.generate(input_ids,do_sample=True,temperature=0.9,max_length=100,)gen_text=tokenizer.batch_decode(gen_tokens)[0]
Package: geovectorslib
OverviewThis library provides vectorized direct and inverse geodesic methods.The motivation was to have the accurate and fast vectorized geodesic routines for sailboat routing project. There are few python libraries, withgeographiclibbeing the most accurate and reliable. Haversine method, which is widely used as an example of fast inverse method can be vectorized rather easily, however the errors are expected to be atleast 0.5%. There are no vectorized AND accurate options.This library is based onnumpyand usesVincenty's formulae. It is heavily based on theMovable Type Scripts blogand JavascriptGeodesycode.Vincenty's inverse algorithm is accurate, but sensitive tonearly antipodal points. One approach would be to returnNaNfor such points, with the assumption that they are not frequently observed in practical applications, however asthis discussionnicely pointed out the package cannot be complete if it cannot handle these situations. I found that the issue can be solved by relaxing one of convergence criteria, but it results in errors up to 0.25% vs geographiclib for these points.So, instead, this library uses the vectorized Vincenty's formulae withgeographiclib as a fallbackfor unconverged points.Seenotebookfor execution time comparisons vs geographiclib.Direct method for 100,000 points

94.9 ms ± 25 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
vs
9.79 s ± 1.4 s per loop (mean ± std. dev. of 7 runs, 1 loop each)Inverse method for 100,000 points

1.5 s ± 504 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
vs
24.2 s ± 3.91 s per loop (mean ± std. dev. of 7 runs, 1 loop each)SetupRequirementsPython 3.7+NumpyGeographiclibInstallationInstall it directly into an activated virtual environment:pip install geovectorslibUsageAfter installation, the package can imported:$ python
>>> from geovectorslib import direct, inverse
>>> direct(lats1, lon1, bearings, distances, ellipsoid='WGS84')
>>> inverse(lats1, lons1, lats2, lons2, ellipsoid='WGS84')Latitudes in decimal degrees [-90; +90].
Longitudes in decimal degrees [-180; +180].
Bearings in decimal degrees [0; 360].
Distances in meters.You can specify a different ellipsoid or leave blank to use WGS84.
Available ellipsoid keywords are:ETRS89WGS84GRS80GRS67Intl-1924Clarke-1880Airy-1830ReferencesMovable Type ScriptsGeodesyGeopyGeographiclibStackoverflow discussion