Saravanan666/pypi-org huggingface dataset:
Package: geocamUtil
geocamUtilWebis a set of utilities used by Django web apps in the
GeoCam Share app collection.  It includes the following utilities.MultiSettingsA settings container object built out of an ordered list of child
settings objects.  When you request the value of an attribute, it
returns the value found in the first child that defines that attribute.We typically useMultiSettingswhen apps extend Django settings by
defining new app-specific variables.  For example, if you have an appgeocamAwesomeyou can put the following ingeocamAwesome/defaultSettings.py:GEOCAM_AWESOME_ENABLED = Trueand ingeocamAwesome/__init__.py:import django.conf.settings
from geocamUtil.MultiSettings import MultiSettings
from geocamAwesome import defaultSettings

settings = MultiSettings(django.conf.settings, defaultSettings)then you can run:$ ./manage.py shell
>>> from geocamAwesome import settings
>>> settings.GEOCAM_AWESOME_ENABLED
Truebut if a site administrator adds this line to their site-levelsettings.py:GEOCAM_AWESOME_ENABLED = Falseyou would see:$ ./manage.py shell
>>> from geocamAwesome import settings
>>> settings.GEOCAM_AWESOME_ENABLED
FalseThe advantage of this approach is that site administrators don’t need to
add all of your app’s extended settings to theirsettings.pyfile if
they like the defaults, but they can override any setting in a uniform
way.Actually,MultiSettingsdoes not depend on Django at all.  It will
work with any kind of child container object as long as its fields can
be accessed using dot notation.models.UuidFieldA Django model field that stores auniversally unique identifier.
When you first save a model with aUuidField, if the UUID value is
not already set, it is automatically populated with a random (or “type
4”) UUID encoded as aCharFieldin the standard UUID display format,
which is a series of hex digits separated by hyphens.You might want to use aUuidFieldif you have multiple instances of
your Django app on different hosts and you need to identify the same
object across instances.  We typically donotuse aUuidFieldas the
primary key for a model to avoid a performance penalty.forms.UuidFieldA Django form field corresponding to the same-name model field.
Validates that the user entered a sequence of hex digits separated by
hyphens.models.ExtrasFieldA Django model field for storing extra schema-free data.  You can get
and set arbitrary properties on the extra field, which can be comprised
of strings, numbers, dictionaries, arrays, booleans, andNone.
These properties are stored in a databaseTextFieldas a
JSON-encoded set of key-value pairs.
Package: geocat.comp
CIDocsPackageLicenseCitingGeoCAT-comp is the computational component of theGeoCATproject. GeoCAT-comp provides computational
functions for operating on geosciences data. Many of these functions originated
in NCL and were translated into Python in GeoCAT-comp; however, developers are
welcome to suggest novel computational functions for geosciences data.DocumentationGeoCAT HomepageGeoCAT Contributor's GuideGeoCAT-comp documentation on Read the DocsInstallation and build instructionsPlease see our documentation forinstallation and build instructions.Citing GeoCAT-compIf you use this software, please cite it as described at theGeoCAT-comp - Citationpage.
Package: geocat.f2py
CIDocsPackageLicenseCitingGeoCAT-comp is both the whole computational component of theGeoCATproject and a single Github repository as described inGeoCAT-comp.
As the computational component ofGeoCAT, GeoCAT-comp provides implementations of
computational functions for operating on geosciences data. Many of these functions originated in NCL are pivoted into
Python with the help of GeoCAT-comp; however, developers are welcome to come up with novel computational functions
for geosciences data.Many of the computational functions in GeoCAT are implemented in a pure Python fashion. However,
there are some others that are implemented in Fortran but wrapped up in Python. To facilitate
contribution, the whole GeoCAT-comp structure is split into two repositories with respect to
being pure-Python or Python with compiled codes (i.e. Fortran) implementations. While pure Python
implementation as well as user API are implemented withinGeoCAT-comp, Python codes that calls the Fortran functionality
with the help ofNumpy's f2pyare handled within GeoCAT-f2py
(i.e. this repository).GeoCAT-f2pyGeoCAT-f2py wraps, in Python, the compiled language implementations of some of the computational functions
found under theGeoCAT-compumbrella. The compiled language functions
contained in GeoCAT-f2py (i.e. this repository) as Fortran subroutines are wrapped up in corresponding
Python wrapper files in the same repository with the help of Numpy.f2py's signature files (.pyf).Not all computational functions in GeoCAT computational component have compiled language implementations;
therefore, developers basing their implementations entirely in Python need not concern themselves with this repo;
instead, they should engage withGeoCAT-comprepo as it invisibly
importsGeoCAT-f2py. However, for those functions that are implemented
in Fortran, this repo provides a Python interface to those functions via a Numpy.f2py wrapper.DocumentationGeoCAT HomepageGeoCAT Contributor's GuideGeoCAT-comp documentation on Read the DocsInstallation and build instructionsPlease see our documentation forinstallation and build instructions.Xarray interface vs NumPy interfaceGeoCAT-f2py provides a high-levelXarrayinterface under thegeocat.f2pynamespace. However, a stripped-down NumPy interface is used under the hood to bridge
the gap between NumPy arrays and the compiled language data structures. These functions are
accessible under thegeocat.comp.f2pynamespace, but are minimally documented and are
intended primarily for internal use.Citing GeoCAT-f2pyIf you use this software, please cite it as described at theGeoCAT-f2py - Citationpage.
Package: geocat.viz
GeoCAT-vizGeoCAT-viz contains tools to help plot geoscience data, including convenience and plotting functions that are
used to facilitate plotting geosciences data with Matplotlib, Cartopy, and other visualization packages.
GeoCAT-viz functionality is used byGeoCAT-examples.CIDocsPackageLicenseDOIDocumentationGeoCAT HomepageGeoCAT-viz Contributor's GuideGeoCAT-viz Documentation on Read the DocsInstallation instructionsPlease see our documentation forinstallation instructions.Citing GeoCAT-vizIf you use this software, please cite it as described at theGeoCAT-viz - Citationpage.
Package: geocell
TheGoogle Maps Geolocation APIactually lets you define multiple gsm
cell towers into its request. But it will just return the location and
accuracy based on the first cell(serving cell), doing nothing meaningful
about the neighbour cells, rssi and timing advance values.So this is an attempt to do something a bit more meaningful…InstallationUse pip:pip install geocellOr clone the repo and:pip setup.py installSetting your Google Maps API KeyThe key will be used in:Google Maps Geolocation APIfor requesting cell locationsGoogle Maps Javascript APIfor drawing mapsIf you don’t have a google maps api key. Gohereand follow the instructions to get your key.Set your api key using either of the following methods:Set your key as theGOOGLE_MAPS_API_KEYenvironment variableimport geocellthengeocell.api_key="<yourapi key>"UsageSimple location request>>>importgeocell>>>sample_cell={"mnc":2,"mcc":286,"cid":51861,"lac":54110}>>>geocell.locate(sample_cell){'location':{'lat':40.7018889,'lng':29.8912834},'accuracy':3250.0}Location EstimationWe need the rssi values and multiple cell information to make an
estimation>>>sample_cells=[...{"rssi":-82,"mnc":2,"mcc":286,"cid":51861,"lac":54110},...{"rssi":-85,"mnc":2,"mcc":286,"cid":16116,"lac":54110},...{"rssi":-93,"mnc":2,"mcc":286,"cid":0,"lac":54108},...{"rssi":-94,"mnc":2,"mcc":286,"cid":38344,"lac":54110},...{"rssi":-97,"mnc":2,"mcc":286,"cid":52555,"lac":54110},...{"rssi":-98,"mnc":2,"mcc":286,"cid":51857,"lac":54108},...{"rssi":-99,"mnc":2,"mcc":286,"cid":39684,"lac":54110}...]>>>geocell.estimate(sample_cells){'location':{'lat':40.70356939393244,'lng':29.88564243119295},'accuracy':251.3839360809747}NoteYou will notice that this function takes couple of seconds to
return, the reason is that theGoogle Maps Geolocation APIrequests take some time. But the cell requests are cached internally
in the module so the next time you request the same cell, the value
will be returned instantly. If you like, you can also setgeocell.is_multiprocess = Trueto do multiple requests, just
don’t use multiprocessing inside a interpreter, it doesn’t like it.Let’s see it in action now:>>>geocell.estimate(sample_cells,"map.html"){'location':{'lat':40.70356939393244,'lng':29.88564243119295},'accuracy':251.3839360809747}Looking at the generated map:Light yellow circleis the serving cellRed coloredare the neighbour cellsThe blue linesare the flight path showing estimation done at
each step. Starting point of the flight path is the center of the
serving cellThe markershows the end of the flight path and the last
estimated locationLet’s take a closer look:>>>geocell.estimate(sample_cells,"map_estimate.html",cell_display="estimate"){'location':{'lat':40.70356939393244,'lng':29.88564243119295},'accuracy':251.3839360809747}Now the map shows only the estimated location(marker) and estimated
accuracy(radius). The arrow points to the actual location.
Package: geocensusapi
CensusAPIImplementation of US Census API calls in form of a python library.CensusAPI allows to output an augmented geojson file containing census information at the US census tract block level given a geojson file in the US.RequirementsKeyObtain a key fromhttp://api.census.gov/data/key_signup.htmlto acess the Census dataset
and place it in a.env'file:secretKey = YourkeyInstallSee therequirements.txtfor all the packages.census==0.8.19censusgeocode==0.5.2geopandas==0.9.0pandas==1.5.1UsageDefined incensusgf.py.add_census_to_geojson: Returns a geojson with columns containing Census Tract Level Data regarding building tract level ownership, population and income.Parameters:in_pth: str. The file location of the geojson building file.out_pth: str. The file location in which to save the augmented file.key: str. The 40 digit text string. Can be obtained fromthe US Census site.census_variables: tuple[str]. Default = None.
An optional tuple of strings identifying ACS 5 Census variables to augment the dataframe. If custom_variables is not specified the function will return an augmented geojson with default columns.add_census_to_geojson_df:  Returns a geojson with additional columns containing Census Tract Level Data regarding building tract level ownership, population and income.Parameters:df: GeoDataFrame. The input geodataframe.key: str. The 40 digit text string. Can be obtained fromthe US Census site.census_variables: tuple[str]. Default = None.
An optional tuple of strings identifying ACS 5 Census variables to augment the dataframe. If custom_variables is not specified the function will return an augmented geojson with default columns.Default Census Variables:Default variables used to augment the geojson are specified below.
They are taken from theACS 5, 2020dataset. Default variables can be found in theDetailed Tables:Census VariableReadable Column NameVariable DescriptionB01003_001ETotPopTotal PopulationB25003_003ERentOccRenter occupiedB25003_002EOwnOccOwner OccupiedB25121_001EIncomeHousehold income in the past 12 months (in 2020 inflation-adjusted dollars) by valueB25121_002Eless10kHousehold income the past 12 months (in 2020 inflation-adjusted dollars) --!!Less than $10,000B25121_017E10to20kHousehold income the past 12 months (in 2020 inflation-adjusted dollars) --!!$10,000 to $19,999B25121_032E20to35kEstimate!!Total:!!Household income the past 12 months (in 2020 inflation-adjusted dollars) --!!$20,000 to $34,999B25121_047E35to50kEstimate!!Total:!!Household income the past 12 months (in 2020 inflation-adjusted dollars) --!!$35,000 to $49,999B25121_062E50to75kEstimate!!Total:!!Household income the past 12 months (in 2020 inflation-adjusted dollars) --!!$50,000 to $74,999B25121_077E75to100kEstimate!!Total:!!Household income the past 12 months (in 2020 inflation-adjusted dollars) --!!$75,000 to $99,999B25121_092Emore100kEstimate!!Total:!!Household income the past 12 months (in 2020 inflation-adjusted dollars) --!!$100,000 or moreExampleExample can be found inexample.py.
Package: geochemistrypy
GeochemistryPy isa Python frameworkfor data-driven geochemistry discovery. It provides an extendable tool and
one-stop shop forgeochemical data analysis.The goal of the Geochemistry Py is to create user-friendly and extensible products of high automation for the full cycle
of geochemistry research, including apython packageand asoftwarewith a command-line interface in the first stage.The layout of the Geochemistry Py Version 1.0.0:The whole package is under construction and the documentation is progressively evolving.Team LeaderCan He (Sany, National University of Singapore)Email:sanyhew1097618435@163.comTeam MembersYang Lyv (Daisy, China University of Geosciences, Wuhan)Shengxin Wang (Samson, Lanzhou University)Qiuhao Zhao (Brad, Zhejiang University)Jianhao Sun (Jin, China University of Geosciences，Wuhan)Wenyu Zhao (Molly, Gaussian Robotics)Xunxin Liu (Tante, China University of Geosciences, Wuhan)Aixiwake·Janganuer (Ayshuak, Sun Yat-sen University)Jianming Zhao (Jamie, Jilin University, Changchun)Ting Liu (Kira, Sun Yat-sen University)Kaixin Zheng (Hayne, Sun Yat-sen University)Fang Li (liv, Shenzhen University)Join UsDo you want to contribute to this open-source program? Contact with your CV:sanyhew1097618435@163.comWhat can you get? Please refer to:https://person.zju.edu.cn/zhangzhou#0Related VideosTechnical record videos are on Bilibili and Youtube synchronously while other meeting videos are internal materials.ZJU_Earth_Data Introduction (Geochemical Data, Python, Geochemistry Py) - Prof. ZhangHow to Collaborate and Provide Bug Report on Geochemistry Py Through GitHub - Can He (Sany)How to Run Geochemistry Py v1.0.0-alpha - Can He (Sany)How to Create and Use Virtual Environment on Geochemistry Py - Can He (Sany)How to use Github-Desktop in conflict resolution - Qiuhao Zhao (Brad)Virtual Environment & Packages On Windows-- Jianming Zhao (Jamie)ContributorsAnzhou Li (Andrian, Zhejiang University)
Package: geoclaw-landspill
geoclaw-landspillNote: if looking for content ofgeoclaw-landspill-cases, please checkout tagv0.1. This repository has been converted to a fully working solver package.geoclaw-landspillis a package for running oil overland flow simulations for
applications in pipeline risk management. It includes a numerical solver and
some pre-/post-processing utilities.The numerical solver is a modified version ofGeoClaw.
GeoClaw solves full shallow-water equations. We added several new features and
utilities to it and make it usable to simulate the overland flow from pipeline
ruptures. These features include:adding point sources to mimic the rupture pointsadding evaporation modelsadding Darcy-Weisbach bottom friction models with land roughnessadding temperature-dependent viscosityrecording detail locations and time of oil flowing into in-land waterbodiesdownloading topography and hydrology data automatically (the US only)generating CF-1.7 compliant NetCDF filesDocumentationDependencies, installation, and testsUsageConfiguration file:setrun.pyExample casesContainers: Docker and SingularityQuick startWe only maintain compatibility with Linux. Though usingpipor building from
source may still work in Mac OS or Windows (e.g., through WSL), we are not able
to help with the installation issues on these two systems.Beyond this quick start, to see more details, please refer to thedocumentationsection.1. InstallationThe fast way to installgeoclaw-landspillis throughAnaconda'scondacommand. The following command
creates a conda environment (calledlandspill) and installs the package and
dependencies:$ conda create \
    -n landspill -c barbagroup -c conda-forge \
    python=3.8 geoclaw-landspillThen useconda activate landspillorsource <conda installation prefix>/bin/activate landspillto activate the
environment. Typegeoclaw-landspill --helpin the terminal to see ifgeoclaw-landspillis correctly installed.2. Running an example caseTo run an example case under the foldercases, users have to clone this
repository. We currently don't maintain another repository for cases. After
cloning this repository, run$ geoclaw-landspill run <path to an example case folder>For example, to runutal-flat-maya:$ geoclaw-landspill run ./cases/utah-flat-mayaUsers can use environment variableOMP_NUM_THREADSto control how many CPU
threads the simulation should use for OpenMP parallelization.3. Creating a CF-compliant NetCDF raster fileAfter a simulation is done, users can convert flow depth in raw simulation data
into a CF-compliant NetCDF raster file. For example,$ geoclaw-landspill createnc ./case/utah-flat-mayaReplace./cases/utah-flat-mayawith the path to another desired case.QGIS and ArcGIS should be able to read the resulting NetCDF raster file.Third-party codes and licensesamrclaw:https://github.com/clawpack/amrclaw(BSD 3-Clause License)geoclaw:https://github.com/clawpack/geoclaw(BSD 3-Clause License)pyclaw:https://github.com/clawpack/pyclaw(BSD 3-Clause License)clawutil:https://github.com/clawpack/clawutil(BSD 3-Clause License)riemann:https://github.com/clawpack/riemann(BSD 3-Clause License)ContributingSeeCONTRIBUTING.md.ContactPi-Yueh Chuang:pychuang@gwu.edu
Package: geocli
Geocoding CLIInstallationUsing Pip$pipinstallgeocliManual$gitclonehttps://github.com/expandaventures/geocoding-cli$cdgeocoding-cli$pythonsetup.pyinstallUsage$geocliGeocode a single addressUsage: geocli geocode [OPTIONS] ADDRESS [STATE] [CITY]$geocligeocode--dry_run'Av. Reforma 222'cdmxmexicoBatch: geocode a addresses in a fileUsage: geocli batch [OPTIONS] INPUT_FILE OUTPUT_PATH$geoclibatch--dry_runsample.csvout.csvThe expected schema in the file is:id, state, unused, city, ... , address
1, CDMX,,Mexico,,Av. Reforma 222TheONLYcolumns used are 2nd, 4th and last.
(e.g. row[1], row[3], row[-1])
Package: geoclip
🌎 GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization📍 Try out our demo!DescriptionGeoCLIP addresses the challenges of worldwide image geo-localization by introducing a novel CLIP-inspired approach that aligns images with geographical locations, achieving state-of-the-art results on geo-localization and GPS to vector representation on benchmark datasets (Im2GPS3k, YFCC26k, GWS15k, and the Geo-Tagged NUS-Wide Dataset). Our location encoder models the Earth as a continuous function, learning semantically rich, CLIP-aligned features that are suitable for geo-localization. Additionally, our location encoder architecture generalizes, making it suitable for use as a pre-trained GPS encoder to aid geo-aware neural architectures.MethodSimilarly to OpenAI's CLIP, GeoCLIP is trained contrastively by matching Image-GPS pairs. By using the MP-16 dataset, composed of 4.7M Images taken across the globe, GeoCLIP learns distinctive visual features associated with different locations on earth.🚧 Repo Under Construction 🔨📎 Getting Started: APIYou can install GeoCLIP's module using pip:pip install geoclipor directly from source:git clone https://github.com/VicenteVivan/geo-clip
cd geo-clip
python setup.py install🗺️📍 Worldwide Image GeolocalizationUsage: GeoCLIP InferenceimporttorchfromgeoclipimportGeoCLIPmodel=GeoCLIP()image_path="image.png"top_pred_gps,top_pred_prob=model.predict(image_path,top_k=5)print("Top 5 GPS Predictions")print("=====================")foriinrange(5):lat,lon=top_pred_gps[i]print(f"Prediction{i+1}: ({lat:.6f},{lon:.6f})")print(f"Probability:{top_pred_prob[i]:.6f}")print("")🌐 Worldwide GPS EmbeddingsIn our paper, we show that once trained, our location encoder can assist other geo-aware neural architectures. Specifically, we explore our location encoder's ability to improve multi-class classification accuracy. We achieved state-of-the-art results on the Geo-Tagged NUS-Wide Dataset by concatenating GPS features from our pre-trained location encoder with an image's visual features. Additionally, we found that the GPS features learned by our location encoder, even without extra information, are effective for geo-aware image classification, achieving state-of-the-art performance in the GPS-only multi-class classification task on the same dataset.Usage: Pre-Trained Location Encoderimporttorchimporttorch.nnasnnfromgeoclipimportLocationEncodergps_encoder=LocationEncoder()gps_data=torch.Tensor([[40.7128,-74.0060],[34.0522,-118.2437]])# NYC and LA in lat, longps_embeddings=gps_encoder(gps_data)print(gps_embeddings.shape)# (2, 512)AcknowledgmentsThis project incorporates code from Joshua M. Long's Random Fourier Features Pytorch. For the original source, visithere.Citation@article{cepeda2023geoclip,
  title={GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization},
  author={Vivanco, Vicente and Nayak, Gaurav Kumar and Shah, Mubarak},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2023}
}
Package: geocode-lite
# geocode-liteBasic python tool to get lat/long of address using Google Map API.## AuthorsCamille Daum-Lobko <camille@daum-lobko.com>## CopyrightCopyright (c) CoreLogic _2017_. All rights reserved.## General InformationCreate distribution file:` python setup.py bdist_wheel `Upload distributions` twine upload dist/* `## Dependencies[Twine](https://github.com/pypa/twine) is a utility for interacting with PyPI, that offers a secure replacement forsetup.py upload.` pip install twine `## Usage Instructions_Installation and build instructions for developpers.__A step by step series of examples that tell you how to get a development environment running.__Add an example of getting some data out of the system or using it for a little demo._## Documentation_Add a link to user and technical documentations._## Change log_Write change log each time a new version is published._## LicenseSee [LICENSE](LICENSE) file.
Package: geocoder-reverse-natural-earth
geocoder_reverse_natural_earthReverse lookup of locations using Natural-Earths administrative boundary shp-database. This allows
a simple lookup from geo-location in latitude/longitude to a countries ISO2 code.LicenseThe python code in this package is licensed under the LGPL >= 2.1, see LICENSE.The Natural-Earth database included as zip file is in public domain,
seehttps://www.naturalearthdata.com/about/terms-of-use/.InstallationThe following will install geocoder_reverse_natural_earth and all dependencies.python -m pip install geocoder_reverse_natural_earthor for the latest development version:python -m pip install 'geocoder_reverse_natural_earth@git+https://github.com/metno/geocoder_reverse_natural_earth.git'Usagegeocoder_reverse_natural_earth is small helper to identify country codes for obs networks that don't mention the
countrycode of a station in their location datafromgeocoder_reverse_natural_earthimport(Geocoder_Reverse_NE,Geocoder_Reverse_Exception,)geo=Geocoder_Reverse_NE()print(geo.lookup(60,10)["ISO_A2_EH"])lat=78.2361926lon=15.3692614try:geo.lookup(lat,lon)exceptGeocoder_Reverse_Exceptionasgrex:dummy=geo.lookup_nearest(lat,lon)ifdummyisNone:print(f"error:{lat},{lon}")else:print(dummy["ISO_A2_EH"])
Package: geocodertools
Installation# pip install geocodertools$geocodertools--lng8.40--lat49.3UsageCommand line$geocodertools--lng8.40--lat49.3
admin1code:08admin2code:00admin3code:07338admin4code:07338007alternatenames:asciiname:Dudenhofen
cc2:countrycode:DE
dem:105elevation:featureclass:P
featurecode:PPLA4
geonameid:2934737latitude:49.31861
longitude:8.38861
modificationdate:2011-04-25
name:Dudenhofen
population:5709timezone:Europe/BerlinPythonimportgeocodertools.reverseasgeocity=geo.get_city({'latitude':8.2,'longitude':49.2})