Saravanan666/pypi-org huggingface dataset:
Package: geospock-cli
GeoSpock CLIInstallationInstalling the CLI from pip$ python -m pip install geospock-cliLogging in with your enterprise Identity Provider (GeoSpock v3.1 and above)Thelogincommand creates a configuration file with an encoded copy of your username and password along with the
request address for your deployment.Alternatively, if you do not want to save your details in a config file:all geospock commands can be run with the arguments--user <value> --password <value> --request-address  <value>.the CLI will also check for the environment variablesGEOSPOCK_USERandGEOSPOCK_PASSWORDand use these to
authenticate. The--request-address <value>argument will still need to be added to the command.An optional--profile {profileID}argument can be used to set up configurations for multiple GeoSpock deployments.
All subsequentgeospockcommands can then use this profile flag to specify that deployment.Getting credentials for the inbuilt Identity Provider (GeoSpock v3.0)Theinitcommand creates a configuration file with the argument values for your your deployment:geospock init --clientid abcdefgh1234 --audience https://testaudience.geospock.com --auth0url login.test.com  --request-address https://testrequest.geospock.com/graphqlAn optional--profile {profileID}argument can be used to set up configurations for multiple GeoSpock deployments.
All subsequentgeospockcommands can then use this profile flag to specify that deployment.To authenticate the CLI to use your GeoSpock account, the following command can be used (alternatively this will be run
automatically when a user first tries to use a command).geospock get-credentials [--profile {profileID} --no-browser]This will open a web-page in the user's default web browser to authenticate. If the--no-browserflag is added, this
will instead provide a web-address for a user to visit in order to authenticate.
The user should enter their GeoSpock username and password when requested to authorise the CLI.
This process should only be required once per user per profile.Running the CLIThe CLI can be activated at the command line usinggeospock COMMAND ... [--profile {profileID}]A list of commands can be shown by usinggeospock help [--profile {profileID}]. Further information on the
input types of each command can be obtained by runninggeospock help COMMAND [--profile {profileID}].
Package: geospots
GeospotsGeospots contains functions for working with spatial data. Developed for SpotsApp support and standalone usage.Geospots relies on the Open Route Service API (https://openrouteservice.org/), a crowd sourced service which provides spatial data directly from Open Street Map.
To make full use of geospots, you will need a free API key from ORS:https://openrouteservice.org/dev/#/signupDocumentation:https://geospots.readthedocs.io.FeaturesIsochrones: Using the return_iso function, input a coordinate and driving mode to determine which areas you can reach from a spot in given timesCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-07-28)First release on PyPI.
Package: geosrw
geosrwA python package that provides a set of tools for working with spatial data using the GEOS library. The package is designed to simplify the process of reading, writing, and transforming spatial data, such as vector and raster data.Free software: MIT licenseDocumentation:https://jeff-250.github.io/geosrwFeaturesTODOCreditsThis package was created withCookiecutterand thegiswqs/pypackageproject template.
Package: geosss
GeoSSS: Geodesic Slice Sampling on the SphereThis python package implements two novel tuning-free MCMC algorithms, anideal geodesic slice samplerbased on accept/reject strategy and ashrinkage-based geodesic slice samplerto sample from spherical distributions on arbitrary dimensions. The package also includes the implementation of random-walk Metropolis-Hastings (RWMH) and Hamiltonian Monte Carlo (HMC) whose step-size parameter is automatically tuned.
As shown in ourpaper, our algorithms have outperformed RWMH and HMC for spherical distributions.This demo quickly illustrates that. We consider a target that is a mixture of von Mises-Fisher distribution on a 2-sphere with concentration parameter $\kappa=80$. By using $10^3$ samples, our samplers GeoSSS (reject) and GeoSSS (shrink) (top row) explore all modes, whereas RWMH and HMC (bottom row) get stuck in a single mode.InstallationGeoSSS is available for installation fromPyPI. Therefore, simply type:pipinstallgeosssTo install dependencies required to run scripts underscripts/,pipinstallgeosss[extras]Getting StartedA minimal example to get started as well as reproduce the above demo:importgeosssasgsimportnumpyasnp# parameters for mixture of von Mises-Fisher (vMF)# distributionsd=3# required dimensionK=3# number of mixture componentskappa=80.0# concentration parameter# mus (mean directions) of the vMF mixture componentsmus=np.array([[0.86981638,-0.37077248,0.32549536],[-0.19772391,-0.89279985,-0.40473902],[0.19047726,0.22240888,-0.95616562]])# target pdfvmfs=[gs.VonMisesFisher(kappa*mu)formuinmus]pdf=gs.MixtureModel(vmfs)# sampler parametersn_samples=int(1e3)# no. of samplesburnin=int(0.1*n_samples)# burnin samplesseed=3521# sampler seed# initial state of the samplersinit_state=np.array([-0.86333052,0.18685286,-0.46877117])# sampling with the four samplerssamples={}# GeoSSS (reject): ideal geodesic slice samplerrsss=gs.RejectionSphericalSliceSampler(pdf,init_state,seed)samples['sss-reject']=rsss.sample(n_samples,burnin)# GeoSSS (shrink): shrinkage-based geodesic slice samplerssss=gs.ShrinkageSphericalSliceSampler(pdf,init_state,seed)samples['sss-shrink']=ssss.sample(n_samples,burnin)# RWMH: random-walk Metropolis Hastingsrwmh=gs.MetropolisHastings(pdf,init_state,seed)samples['rwmh']=rwmh.sample(n_samples,burnin)# HMC: Hamiltonian Monte Carlohmc=gs.SphericalHMC(pdf,init_state,seed)samples['hmc']=hmc.sample(n_samples,burnin)# visualize samples in 3dgs.compare_samplers_3d(pdf,samples)The plots in thepaperunder numerical illustrations section were generated usingbingham.py,mixture_vMF.py,ess_vMF.pyandcurve.py.DevelopmentTo install this package and its development dependencies in editable mode, please do the followinggitclonehttps://github.com/microscopic-image-analysis/geosss.gitcdgeosss
pipinstall-e.[dev]CitationIf you use this package or ideas from the paper, please consider citing us.@misc{habeck2023,title={Geodesicslicesamplingonthesphere},author={MichaelHabeckandMareikeHasenpflugandShantanuKodgirwarandDanielRudolf},year={2023},eprint={2301.08056},archivePrefix={arXiv},primaryClass={stat.ME}}
Package: geostat
geostatDisclaimerThis software is preliminary or provisional and is subject to revision. It is being provided to meet the need for timely best science. The software has not received final approval by the U.S. Geological Survey (USGS). No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. The software is provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the software.Development and testingpip install -e .[test]
Package: geostates
geostatesGeostates is a Python package for quickly plotting choropleth maps of the United States.InstallationPyPITo install geostates run:pip install geostates.Dev roadmapThe current version of geostates only allows for plotting choropleth maps of the United States using a very specific
set of shapefiles. In the future, the goal is to add:Ability to plot United States maps with county-level data.Add support for Congressional Districts, Metropolitan Statistical Areas, and Zip Code Areas to individual state
plots.Add support for plotting categorical data for United States maps.Add features for plotting political science maps (ie. Electoral College maps, etc.).How to contributeYou can contribute to the geostates package by installing it and giving it a try. You can also file issues on the
issue tracker or submit a pull request if you would like to make any improvements.AcknowledgementsThis package's core functionality relies substantially on thegeopandaspackage.The idea to use inset axes for this package came from Cimbali. A helpful explanation on utilizing segmented
colormaps came from JohanC. As mentioned in the comments, the function for discretizing matplotlib colormaps was
inspired by an example provided in Jake VanderPlas'Python Data Science Handbook.
Package: geostatil
# GEOStatIL[![PyPi](https://img.shields.io/pypi/v/jsontableschema.svg)](https://pypi.python.org/pypi/geostatil)This library allows you to get detailed statistical data on any point in Israel.It uses the 2008 census (done by the LAMAS, Israel's statistical bureau). The census holds data for "statistical areas", which are is a fine grained partition of the land into areas with similar properties. Each statistical area describes a few 1000s of people.It accepts both latitude/longitude pairs as well as textual addresses (which are geocoded using an online service).## Usage```>>> from geostatil import GEOStatIL>>> gsi = GEOStatIL()>>> gsi(address=u'דיזנגוף 99, תל אביב'){'אוכלוסייה – סך הכל (אלפים)': '1.9','אזור טבעי': 'אזור תל אביב','אזור סטטיסטי': '343','אחוז  בני 15 ומעלה שעבדו בשנת 2008 בענף "מסחר סיטוני וקמעוני, תיקון כלי רכב מנועיים, אופנועים, קטנועים וטובין לשימוש אישי וביתי"': '11.0','אחוז  גברים בני 15 ומעלה בעלי תעודות אחרות': '4.3','אחוז בני  17-0 ': '5.0','אחוז בני  64-18  ': '85.4','אחוז בני  65+ ': '9.5','אחוז בני 15 ומעלה בכוח העבודה האזרחי השנתי': '86.2','אחוז בני 15 ומעלה בעלי תואר אקדמי ראשון': '35.9',### ...Omitting another ~250 rows...'סמל מחוז': '5','סמל מטרופולין': '110','סמל מעמד מוניציפלי': '0','סמל נפה': '51','סמל צורת יישוב': '130','צורת יישוב': 'יישוב יהודי 499,999-200,000 תושבים','צפיפות אוכלוסייה (לקמ"ר)': '','צפיפות דיור ממוצעת למשק בית': '0.8','רובע': '3','שטח היישוב (קמ"ר)': '','שנת יסוד': '1909','תת-רובע': '34'}```## Installation```$ pip install geostatil```
Package: geostatista
Current release infoNameDownloadsVersionPlatformsgeostatista - Statistics packagegeostatistais a statistics packagegeostatistaMain Featuresspatial interpolation (IDW/ISDW)Future workKrigingInstalling geostatistaInstallinggeostatistafrom theconda-forgechannel can be achieved by:conda install -c conda-forge geostatistaIt is possible to list all of the versions ofgeostatistaavailable on your platform with:conda search geostatista --channel conda-forgeInstall from Githubto install the last development to time you can install the library from githubpip install git+https://github.com/MAfarrag/geostatistapipto install the last release you can easly use pippip install geostatista==0.1.6Quick start>>> import geostatistaother code samples=======
History0.1.0 (2022-05-24)First release on PyPI.0.1.4 (2022-12-26)use environment.yml and conda instead of pyproject.toml and poetry0.1.5 (2022-12-27)fix bug in pypi package names in the requirements.txt file0.1.6 (2023-01-31)bump up versions
Package: geostats
GeostatsThis repository provides a suite of geostatistical tools, including functions to compute the ground-motion correlation structure according to the relations by Bodenmann et al. (2023), and to do Kriging interpolation.ExampleExample 1: Ordinary Kriging using the $\rho_{E}$ correlation model by Bodenmann et al. (2023)Example 2: Ordinary Kriging using the $\rho_{EA}$ correlation model by Bodenmann et al. (2023)Example 3: Ordinary Kriging using any preferred correlation structureExample 4: Krige a mapAcknowledgementsImplementation of the Kriging code benefited from Scott Brandenberg'srandom fieldpython package.Some of the cython functions to compute ground-motion correlation are based on Lukas Bodenmann'spython functions.CitationIf you use these codes, please cite:Renmin Pretell. (2023). RPretellD/geostats: Initial release (0.1.0). Zenodo.https://doi.org/10.5281/zenodo.10253691
Package: geostatsmodels
geostatsmodelsThis is an implementation of ideas from Clayton V. Deutsch and Michael Pyrcz's bookGeostatistical Reservoir Modelingin order to better understand geostatistics. I am working through things first in Python in order to prototype things quickly and make sure I understand them.There is also an R package for geostatistics named gstat, but I have not used it much.This software is licensed under theMIT License.Installation and DependenciesThis package depends on:numpy, the fundamental package for scientific computing with Python.matplotlib, a Python 2D plotting library which produces publication quality figures.scipy, a Python library which provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.pandas, a Python library which implements excellent tools for data analysis and modeling.The best/easiest way to install these packages is to use one of the Python distributions describedhere. Anaconda has been successfully tested with geostatsmodels.Most of those distributions should includepiporconda, which are command line tools for installing and
managing Python packages.  You can usepipto install geostatsmodels itself.You may need to open a new terminal window to ensure that the newly installed versions of python and pip
are in your path.To install geostatsmodels:pip install git+git://github.com/cjohnson318/geostatsmodels.gitUninstalling and UpdatingTo uninstall:pip uninstall geostatsmodelsTo update:pip install -U git+git://github.com/cjohnson318/geostatsmodels.gitUsageSome notebooks exploring the functionality ofgeostatsmodelsare included below.Variogram AnalysisKriging ExampleMore to come!
Package: geostream
GeostreamGeostreamis a Python tool to interact with OGC APIs with ease and download geospatial data to Geopandas GeoDataFrames.Status: Currently only works with WFS API and is very experimental by nature. Use at own risk.Installationgeostreamis available via PyPi and can be installed with:pip install geostreamBasic usagefromgeostreamimportWFS# Endpoint for WFS API of the City of Helsinkiendpoint="https://kartta.hel.fi/ws/geoserver/avoindata/wfs"# Initialize WFS reader# Note: API version can vary between data providers (by default 1.0.0 is used)wfs=WFS(endpoint,version="2.0.0")# See all available layerswfs.get_layers()# Store the id of the first layerfirst_layer=wfs.get_layers()[0]# Find info for specific layerwfs.get_layer_info(first_layer)# Download a GeoDataFrame with Helsinki as the extenthelsinki_gdf=wfs.get_gdf_from_place("Helsinki")# Download the data into GeoDataFrame from the Helsinki Regiondata=wfs.load_wfs(layer_id=first_layer,bounding_box=helsinki_gdf)
Package: geosupport-suggest
geosupport-suggestRetrieve address suggestions from Geosupport usingpython-geosupportand a single input address.Install$pipinstallgeosupport-suggestor clone this repo,cdinto it and$pipinstall.UsagefromgeosupportimportGeosupportfromsuggestimportGeosupportSuggest# create a Geosupport objectg=Geosupport()# create a GeosupportSuggest object using Geosupports=GeosupportSuggest(g)Get SuggestionsRequires at least a house number and the first character of a street name. Each suggestion returns the full geosupport result.>>s.suggestions('100 Gold')[{'First Borough Name':'STATEN IS','House Number - Display Format':'100','House Number - Sort Format':'000100000AA','B10SC - First Borough and Street Code':'52981501010','First Street Name Normalized':'GOLD AVENUE',...},{'First Borough Name':'MANHATTAN','House Number - Display Format':'100','House Number - Sort Format':'000100000AA','B10SC - First Borough and Street Code':'12135001010','First Street Name Normalized':'GOLD STREET',...},{'First Borough Name':'BROOKLYN','House Number - Display Format':'100','House Number - Sort Format':'000100000AA','B10SC - First Borough and Street Code':'34453001010','First Street Name Normalized':'GOLD STREET',...}]ContributeIssues and PRs welcome.LicenseMIT
Package: geosys
GEOSYSGeosys is the acronymGeologicalSystems. In current version, it is small package which contains only one function.
The function converts GPS coordinates (longitude, latitude and altitude) to cartesian system. Used referenced ellipsoid isWSG 84(also known as WGS 1984, EPSG:4326).InstallationpipinstallgeosysExampleimportgeosyslatitude=49.2002211longitude=16.6078411altitude=0vertex=geosys.to_cartesian(latitude,longitude,altitude)# Vertex attributes# vertex.x# vertex.y# vertex.zprint(vertex)>>> [4001412.65349505, 1193469.21673459, 4805137.77147738]
Package: geotagger
Geotag your photos from GPS-less cameras with your phone’s location
history data.Is it for me?Do you take photos with a camera without a GPS tracking feature?Do you havehttps://moves-app.com/installed on your phone?Do you want to have your photos geotagged?…if your answer is 3x yes, then this tool is for you.How does it work?You already walk around with your location-aware phone and Moves
records your location.You snap photos with your GPS-less camera.GeoTagger uses the creation timestamps from photos
to find your location at the time of taking them in your Moves log.GeoTagger is able to retrospectively add GEO tags to even old photos,
as long as your location for the period is logged with Moves.How is it implemented?When you ask GeoTagger to tag your photos, this process takes places:Unique creation dates are extracted from the photo files.Your Moves history for those days is fetched from the Moves API.AGPXfile is
generated from the data.exiftool-geotagis used behind to scene to apply that location
log to your photos.StatusBeta quality.InstallationInstallexiftool, for example, with:$ brew install exiftoolInstallgeotaggerfrom PyPi with:$ pip install geotaggerUsageSee$ geotagger--helpand$ geotaggersub-command--help.Moves authenticationCreate a new app under your Moves account:https://dev.moves-app.com/apps/newSpecifyhttp://127.0.0.1:7777/redirectasRedirect URI.Create~/.geotagger.jsonwith credentials for your app:{"MOVES_ID":"<CLIENT_ID>","MOVES_SECRET":"<CLIENT_SECRET>"}Rungeotagger authand follow the instruction to authenticate the app.GeotaggingGeotag all images in a folder:$geotaggertag./photosYou can also just generate a GPX log for the dates without applying it:$geotaggergpx./photos>log.gpxThetagsub-command also optionally accepts a path to a GPX log file:$geotaggertag./photoslog.gpxTODOImprove UX: simplify installation and setupMake the external metadata updates play well with photos already imported to LightroomAdd Geosync supporthttp://www.sno.phy.queensu.ca/~phil/exiftool/geotag.html#geosyncConsider Electron-based GUI appConsider additional GPS log sources than MovesChangelog0.0.3 (2017-09-09)Fixedmissing configuration: MOVES__ID#1ContactJakub Roztocilhttps://github.com/jakubroztocilhttps://twitter.com/jakubroztocilhttps://roztocil.coContributehttps://github.com/jakubroztocil/geotaggerLicenceMIT. SeeLICENCE.
Package: geotagging-alexr96
Database Package for Geotag ProjectThis is a package to help manage the database for DevPipeline's geotagging project, it containse dataclass functionallity and functions for database creation and updating.Database CreationFirst make sure that postgress is downloaded$ psql, if not -$ brew install postgresql**If you do not have Brew:**$ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"$ brew install postgresqlThen run the command$ pg_ctl -D /usr/local/var/postgres startto start postgresDownload the package at %3CPIP LINK HERE%3EOpen the python interpreter and runimport %3NAME_HERE%3EthenNAME_HERE.db_ops.create_db(username=YOUR_PSQL_USERNAME, passwork=YOUR_PSQL_PASSWORD)thenNAME_HERE.db_ops.update_db()
Package: geot-cluster
Name of the module stands for geographical transactions clustering. This module is an implementation of the method, developed for the third course project in HSE University. It takes dataframe with clients transactions history of the
specified format and returns list of clusters.For the record, it was intended to be for public usage in this form, as it is a research project seeking to find a way to deal with the described problemInstallationRun the following to install:‘’’pythonpip install geot_cluster’’’UsageBefore using make sure, that your dataset corresponds with requirements. Csv file must contain the following columns in order to work correctlyuser_id   :   string type, example: “423156821”event_dt  :   string type, example: “20190312”event_time:   string type, example: “2019-03-12 06:24:00.279”lattitude :   float  type, example: 49.862621longtitude:   see lattitudeWorkflow:import geot_cluster

import markov_clustering as mc
import matplotlib.pyplot as plt
import networkx as nx


path = [path to file with data]
data, names = geot_cluster.data_load(path)


%matplotlib notebook
base = [path to the folder, where to store libs with information about clients]

archivate = True
libs= True
graph_f = True
cluster_f = True


if(archivate):
    geot_cluster.archivate_maps(data, names, levels=4)

if(libs):
    lib = geot_cluster.graph_preparation(data, names, base)
    prob_lib = geot_cluster.znakomstvo_by_lib(lib,data)

lib, prob_lib = load_libs(base = base)

if(graph_f):
    graph = geot_cluster.graph_forming(lib, prob_lib, treshold=0.9)

if(cluster_f):
    result = mc.run_mcl(graph,pruning_threshold=0.7, inflation=2,expansion=2)
    clusters = mc.get_clusters(result)

    clust_0 = clusters_to_ids(lib=lib, prob_lib=prob_lib, clusters = clusters, number = 0)
    maps = get_cluster_maps(data = data, clust = clust_0)
    print("Number of clusters", len(clusters))

    plt.figure(figsize=(10,10))
    mc.drawing.draw_graph(result, clusters, edge_color="red",node_size=15,width = 1, with_labels=True, font_size = 8)
Package: geotechnicalprofile
A Python package to load raw DTS files, perform a calibration, and plot the resultFree software: BSD 3-Clause LicenseInstallationpip install geotechnicalprofileCurrent version on pip is vv0.3.0. . This is probably several commits behind this repository. Pip
install directly from github to obtain the most recent changes.Learn by examplesInteractively run the example notebooks online by clicking the launch-binder button.Documentationhttps://python-geotechnical-profile.readthedocs.io/DevelopmentTo run the all tests run:toxTo bump version and docs:gitstatus# to make sure no unversioned modifications are in the repositorytox# Performes tests and creates documentation and runs notebooksgitstatus# Only notebook related files should be showngitadd--all# Add all notebook related files to local versiongitcommit-m"Updated notebook examples to reflect recent changes"bumpversionpatch# (major, minor, patch)gitpushrm-rfbuild# Clean local folders (not synced) used for pip wheelrm-rfsrc/*.egg-inform-rfdist/*pythonsetup.pyclean--allsdistbdist_wheeltwineupload--repository-urlhttps://upload.pypi.org/legacy/dist/geotechnicalprofile*Changelog0.1.0 (2018-10-03)First release on PyPI.
Package: geotech-pandas
geotech-pandasThegeotech-pandasis aPandasextension that provides a suite of functions specifically
tailored for geotechnical engineering calculations, seamlessly integrating with the powerful data
manipulation and analysis capabilities of Pandas. This open-source extension empowers geotechnical
engineers to streamline their workflow, enhance data analysis, and improve productivity.UsingSee theUser Guideto get started
in usinggeotech-pandas.ContributingSee theContribution Guideto
learn more on how you can help improvegeotech-pandas. By participating, you are expected to
upholdgeotech-pandas'Code of Conduct.Licensegeotech-pandasis licensed under theBSD 3-Clause "New" or "Revised" License.