Saravanan666/pypi-org huggingface dataset:
Package: geoapivietnam
Geo API Vietnam 🇻🇳Geo API Vietnam is a package that helps you find locations in Vietnam with strongly cleaned data.Main fuction:Clean up and correct spelling for Province and District.Search location by keyword or coordinates.Location object with properties:address: The address has been formatted correctly.province: Province is spelled correctly.district: District is spelled correctly.ward: Ward is spelled correctly.latitude: Latitude of the location.longitude: Longitude of the location.source: Origin oforiginal_address, including Excel, SQLite, GeoPy, and Google Maps API.original_address: Address from the above sources.json_data: The location's data is in the form of a dictionary.How it worksUse Vietnam location data to clean up and correct typos.Use APIs fromGeoPyand Google Maps (Google Geocoding) to search for locations online.Use Excel and SQLite to save data locally.Search location in smart order: Excel > SQLite > GeoPy > Google Maps.How to installInstall with PipInstall new package.pip install geoapivietnamUpgrade to the latest version.pip install geoapivietnam --upgradeInstall with GitHubInstall new package.pip install git+https://github.com/tranngocminhhieu/geo-api-vietnam.gitUpgrade to the latest version.pip install git+https://github.com/tranngocminhhieu/geo-api-vietnam.git --upgradeHow to useImport module and readSample usage notebookto explore this module.from geoapivietnam import Correct, GetLocation
Package: geoapps
Welcome togeoapps- Open-source applications in geosciencesIn shortThegeoappsproject has been created byMira Geosciencefor the development and sharing of open-source
applications in geoscience. Users will be able to directly leverage the powerful visualization capabilities ofGeoscience ANALYSTalong with open-source code from the Python ecosystem.LinksDownload the latestGetting startedDocumentationCurrent sponsorsAnglo AmericanBarrickBHPCamecoGlencoreMira GeoscienceRio TintoTeckValeThird Party SoftwareThe geoapps Software may provide links to third party libraries or code (collectively “Third Party Software”)
to implement various functions. Third Party Software does not comprise part of the Software.
The use of Third Party Software is governed by the terms of such software license(s).
Third Party Software notices and/or additional terms and conditions are located in theTHIRD_PARTY_SOFTWARE.rstfile.LicenseMIT LicenseCopyright (c) 2024 Mira GeosciencePermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: geoarea
geoareaThis is a fork fromgeojson-area (python).
Calculate the area from lists of latitude and longitude coordinates.Installation$ pip install geoareaUsageSimply pass a list of latitude and longitude>>> from geoarea import area
>>> latitude_world = [-90, 90, 90, -90, -90]
>>> longitude_world = [-180, -180, 180, 180, -180]  
>>> area(latitude_world, longitude_world)
511207893395811.06Test$ python test.pyCreditgeojson-area (python)geojson-areaReferenceshttps://trs.jpl.nasa.gov/bitstream/handle/2014/41271/07-0286.pdf
Package: geoarray
Fast Python interface for geodata - either on disk or in memory.The geoarray package facilitates reading and writing of all GDAL compatible image file formats
and provides functions for geospatial processing.Free software: Apache 2.0Documentation:https://danschef.git-pages.gfz-potsdam.de/geoarray/doc/StatusSee also the latestcoveragereport and thepytestHTML report.Features and usageThere is an example notebook that shows how to use geoarray:here.InstallationUsing Anaconda or Miniconda (recommended)Usingconda(latest version recommended), geoarray is installed as follows:Create virtual environment for geoarray (optional but recommended):$condacreate-cconda-forge--namegeoarraypython=3$condaactivategeoarrayThen install geoarray itself:$condainstall-cconda-forgegeoarrayThis is the preferred method to install geoarray, as it always installs the most recent stable release and
automatically resolves all the dependencies.Using pip (not recommended)There is also apipinstaller for geoarray. However, please note that geoarray depends on some
open source packages that may cause problems when installed with pip. Therefore, we strongly recommend
to resolve the following dependencies before the pip installer is run:cartopy >=0.20gdal >=2.1.0geopandasholoviews  # optional, in case you want to use interactive plottingmatplotlibnumpypandaspyproj >2.2.0scikit-imageshapelyThen, the pip installer can be run by:$pipinstallgeoarrayIf you don’t havepipinstalled, thisPython installation guidecan guide you through the process.History / ChangelogYou can find the protocol of recent changes in the geoarray packagehere.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: geoarrow-rust
geoarrow-rustThis is a metapackage for thegeoarrow.rustnamespace.Both thegeoarrowandgeoarrow-rustPython libraries are distributed withnamespace packaging, meaning that each python packagegeoarrow-[submodule-name]andgeoarrow-rust-[submodule-name](imported asgeoarrow.[submodule-name]orgeoarrow.rust.[submodule-name]) can be published to PyPI independently.In order to obtain relevant modules, you should install them from PyPI directly, e.g.:pip install geoarrow-rust-core
Package: geoarrow-rust-core
geoarrow.rust.corePython bindings togeoarrow-rsOverviewThis library contains Python bindings to theGeoArrow Rust implementation.Fast: Connects to algorithms implemented inGeoRust, which compile to native code.Parallel: Multi-threading is enabled out-of-the-box for all operations on chunked data structures.Self-contained: Zero Python dependencies.Easy to install: Distributed as static binary wheels with zero C dependencies.Strong, static typing: geometry arrays have a known typeInteroperable ecosystem: Data is shared at zero cost with other Python libraries in the burgeoningGeoArrow ecosystem, such asgeoarrow-corlightning-fast map rendering.More specifically, it contains:Classes to represent GeoArrow arrays:PointArray,LineStringArray, etc.Classes to representchunkedGeoArrow arrays:ChunkedPointArray,ChunkedLineStringArray, etc.A spatial table representation,GeoTable, where one column is a geospatial type, to enable future support for geospatial joins.Rust-based algorithms for computations on GeoArrow memory.Rust-based parsers for various geospatial file formats.DocumentationRefer to the documentation atgeoarrow.github.io/geoarrow-rs/python.Future work:3D coordinates. Only 2D geometries are supported at this time.More algorithms, including spatial indexes and spatial joins.CRS management. This currently loses the CRS information in theGeoArrow metadata.Background readingRefer to theGeoArrow Python module proposalfor more background information.
Package: geoAssembler
Shape Overlay Based Detector Geometry CalibrationThis repository provides a tool to calibrate AGIPD detector geometry informationThe tool can be seen as an alternative to the calibration mode of CrysFEL's
hdfsee. The calibration can either be based on a starting geometry that needs
to be refined or a completely new geometry. In the latter case the initial
conditions for the geometry are defined so that all modules are
29px apart from each other.The geometry calibration is supported by two modes of graphical user interfaces.
A Qt-based and a jupyter notebook based interface.Using the Qt-GuiIt is recommended to use this Gui application through the pre-installed path
on the on- and offline cluster.source/gpfs/exfel/sw/software/modules
moduleloadxfelThe following optional arguments can be set via the command line:ArgumentDescription-h, --help-nb, --notebookDo not start gui, create a notebook-no_kernelDo not try to attempt creating a xfel notebook kernel-nb_foldernb_folderSet default directory to save notebooks-nb_filenb_fileSet file name of the notbook-r, --runrun_dirThe path name to a exp folder-g, --geometrygeomfilePath to geometry file-c, --clenclenDetector distance [m]-e, --energyenergyPhoton energy [ev]-l, --levelmin maxDisplay range for plottingIf no run directory, using-r/--run, option has been preselected a
directory can to be set by clicking the Run-dir button. Train id's can be
selected after a run has been selected. The user can either choose to display
images by pulses or if the signal is to week/noisy by applying a Maximum or
Mean across the entire train to all images.
To do so the user can just select theMaxorMeanbutton
instead of the defaultSel #. After an image number / function has been
selected the image can be assembled using theAssemblebutton.
Optionally a pre-defined geometry file can be loaded using theLoadbutton.After the image is displayed quadrants can be selected by clicking on them.
They can be moved by using theCtrl+arrow-up/down/left/rightkey combination. Circles that can help to align quadrants are added
by theDraw Helper Objectsbutton. The radii of the circles an be adjusted
using the radiusspin boxin the top left.Once the quadrants have been positioned a geometry file can be saved by
using theSavebutton.Calibration Using JupyterThe -nb, --notebook flag creates a jupyter notebook uses the home direcory.SetupUsing Maxwell Cluster (recommended)It is recommended to use the already setup application available in xfel's
anaconda3 distirbution.
All packages should be available via the xfel module. At the moment
the module has to be activated by$:moduleloadexfelexfel_anaconda3
$:geoAssemblerUsing pipThe software is can be installed by pip:$:pipinstall.
$:geoAssemblerDependenciesIf the user doesn't want or cannot use the xfel module and wants to install the
tool the following python packages should be available:numpycfelpyutilspyqtgraphmatplotlibipywidgetspyqt5pyFAITesting:Testing the implementation is done py pytest. To apply the test suite run$:pytest-vgeoAssembler/tests
Package: geobench
GEO-Bench: Toward Foundation Models for Earth MonitoringGEO-Bench is aServiceNow Researchproject.GEO-Bench is a General Earth Observation benchmark for evaluating the performances of large pre-trained models on geospatial data. Read thefull paperfor usage details and evaluation of existing pre-trained vision models.InstallationYou can install GEO-Bench withpip:pip install geobenchNote: Python 3.9+ is required.Downloading the dataSet$GEO_BENCH_DIRto your preferred location. If not set, it will be stored in$HOME/dataset/geobench.Next, use thedownload script. This will automatically download fromHugging FaceRun the command:geobench-downloadYou need ~65 GB of free disk space for download and unzip (once all .zip are deleted it takes 57GB).
If some files are already downloaded, it will verify the md5 checksum. Feel free to restart the downloader if it is interrupted.Test installationYou can run tests.
Note: Make sure the benchmark is downloaded before launching tests.pip install pytestgeobench-testLoading DatasetsSeeexample_load_dataset.pyfor how to iterate over datasets.importgeobenchfortaskingeobench.task_iterator(benchmark_name="classification_v1.0"):dataset=task.get_dataset(split="train")sample=dataset[0]forbandinsample.bands:print(f"{band.band_info.name}:{band.data.shape}")Visualizing ResultsSee the notebookbaseline_results.ipynbfor an example of how to visualize the results.
Package: geo-benchmark
GEO-Bench: Toward Foundation Models for Earth MonitoringGeoBench is aServiceNow Researchproject.GEO-Bench is a General Earth Observation benchmark for evaluating the performances of large pre-trained models on geospatial data. Read thefull paperfor usage details and evaluation of existing pre-trained vision models.InstallationYou can install GEO-Bench withpip:pip install geo-benchmarkNote: Python 3.9+ is required.Downloading the dataSet$GEO_BENCH_DIRto your preferred location. If not set, it will be stored in$HOME/dataset/geobench.Next, use thedownload script. This will automatically download fromZenodoRun the command:geobench-downloadThe current version of the benchmark is 0.9.1. It will soon be updated to incorporate minor changes.You need ~65 GB of free disk space for download and unzip (once all .zip are deleted it takes 57GB).
If some files are already downloaded, it will verify the md5 checksum. Feel free to restart the downloader if it is interrupted.Test installationYou can run tests.
Note: Make sure the benchmark is downloaded before launching tests.pip install pytestgeobench-testLoading DatasetsSeeexample_load_dataset.pyfor how to iterate over datasets.importgeobenchfortaskingeobench.task_iterator(benchmark_name="classification_v0.9.1"):dataset=task.get_dataset(split="train")sample=dataset[0]forbandinsample.bands:print(f"{band.band_info.name}:{band.data.shape}")Visualizing ResultsSee the notebookbaseline_results.ipynbfor an example of how to visualize the results.
Package: geobinning
Package DescriptionUsegeobinningto bucket [lng,lat] points based on a geojson geometry.  Any geojson that contains ID and polygon geometries will work, and the function returns back a dataframe with (ID,bins).Installation$ pip install geobinningUsageimport requests
import geobinning as gb

df_places  = gpd.read_file('https://opendata.arcgis.com/datasets/af500b5abb7240399853b35a2362d0c0_0.geojson')

polygons = []
for shp in df_places['geometry']:
    lng,lat = shp.exterior.coords.xy
    lng= list(lng)
    lat= list(lat)
    coords = list(map(list,zip(*[lng,lat])))
    polygons.append(coords)

bins = gb.geobin(polygons,[[-79,43],[-78,43.22]])```


### Example ###
Package: geobipy
This package uses a Bayesian formulation and Markov chain Monte Carlo sampling methods to
derive posterior distributions of subsurface and measured data properties.
The current implementation is applied to time and frequency domain electromagnetic data.
Application outside of these data types is in development.CitationFoks, N. L., and Minsley, B. J. 2020. GeoBIPy - Geophysical Bayesian Inference in Python. 10.5066/P9K3YH9OBackground scientific referencesMinsley, B. J., Foks, N. L., and Bedrosian, P. A. 2020. Quantifying model structural uncertainty using airborne electromagnetic data. Geophys. J. Int. 224, 1, 590–607.https://doi.org/10.1093/gji/ggaa393Minsley, B. J. 2011. A trans-dimensional Bayesian Markov chain Monte Carlo algorithm for model assessment using frequency-domain electromagnetic data. Geophys. J. Int. 187, 252–272. 10.1111/j.1365-246X.2011.05165.xDocumentation is here!This 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.
Package: geobootstrap
Geobootstrapgeobootstrapis a novel computer simulation method that extends the classic bootstrap method (Efron 1979) in statistical analysis, to generate representative statistical measures e.g. standard deviations, confidence intervals and probability based values).
Instead of passing the default None weights to pandas.sample, which results in equal probability weighting, kernel-based weights are used.
The weights are determined by a distance decay function, that determines how quickly weights decrease as distances increase.
This means that key statistical measures are computed by pooling or borrowing strength from neighbouring units.
Thus, geobootstrap takes advantage of the spatial structure of GeoDataFrames.Geobootstrap is based onpandas.sampleand operates ongeopandas.GeoDataFrames.
The resampling takes place for each spatial entity and the fraction of samples returned is based on the fraction of the original dataset.
So a GeoDataFrame with 10 spatial entities and a fraction of 1.0 (default) will return a list of 10 entries, each containing 10 different spatial entities.
If the fraction was set to 0.8, then a list of 10 entries, each containing 8 different spatial entities would be returned.Further descriptions about the methodology will be later provided but the contents of the package should provide enough detail for now.
The potential use cases of this methodology include areal interpolation, which are illustrated in thecoss package.
Package: geobr
geobr: Download Official Spatial Data Sets of Brazilgeobr is a computational package to download official spatial data sets of Brazil. The package includes a wide range of geospatial data in geopackage format (like shapefiles but better), available at various geographic scales and for various years with harmonized attributes, projection and topology (see detailed list of available data sets below).READ FULL DOCSContributeTo start the development environment runpoetryinstallTest withpoetry run pytestYou can use a helper to translate a function from R.
If you want to addread_biomes, just runpoetryshell
pythonhelpers/translate_from_R.pyread_biomesIt will scrape the original R function to get documentation and metadata.
It adds:default yearfunction namedocumentation one linerlarger documentationvery basic tests! Be aware that if the function that you are adding is more complicated than the template. So, always double check !Translation StatusFunctionTranslated?Easy?read_amazonYesSuperread_biomesYesSuperread_census_tractYesNoread_comparable_areasYesYesread_conservation_unitsYesSuperread_countryYesSuperread_disaster_risk_areaYesSuperread_health_facilitiesYesSuperread_health_regionYesSuperread_immediate_regionYesYesread_indigenous_landYesSuperread_intermediate_regionYesYesread_meso_regionYesNoread_metro_areaYesSuperread_micro_regionYesNoread_municipal_seatYesSuperread_municipalityYesNoread_regionYesSuperread_semiaridYesSuperread_stateYesSuperread_statistical_gridNoNoread_urban_areaYesSuperread_urban_concentrationsNoSuperread_weighting_areaYesNolist_geobrYesYeslookup_muniYesNoread_neighborhoodYesYesRelease new versionpoetry version [patch|minor|major]
poetry publish --build
Package: geobuf
Geobuf is a compact binary geospatial format forlosslesscompression
of GeoJSON and TopoJSON data.Note well: this project is no longer actively developed. Issues and
pull requests will be attended to when possible, but delays should be
expected.Advantages over using GeoJSON and TopoJSON directly (in thisrevised
version):Very compact: typically makes GeoJSON 6-8 times smaller and
TopoJSON 2-3 times smaller.Smaller even when comparing gzipped sizes: 2-2.5x compression for
GeoJSON and 20-30% for TopoJSON.Easyincremental parsing— you can get features out as you read
them, without the need to build in-memory representation of the whole
data.Partial reads— you can read only the parts you actually need,
skipping the rest.Trivialconcatenation: you can concatenate many Geobuf files
together and they will form a valid combined Geobuf file.Potentiallyfaster encoding/decodingcompared to native JSON
implementations (i.e. in Web browsers).Can still accommodate any GeoJSON and TopoJSON data, including
extensions with arbitrary properties.Think of this as an attempt to design a simple, modern Shapefile
successor that works seamlessly with GeoJSON and TopoJSON.UnlikeMapbox Vector
Tiles, it aims forlosslesscompression of datasets — without tiling, projecting
coordinates, flattening geometries or stripping properties.pygeobufThis repository is the first encoding/decoding implementation of this
new major version ofGeobuf(in
Python). It serves as a prototyping playground, with faster
implementations in JS and C++ coming in future.Sample compression sizes| normal    | gzippedus-zips.json101.85 MB26.67 MBus-zips.pbf12.24 MB10.48 MBus-zips.topo.json15.02 MB3.19 MBus-zips.topo.pbf4.85 MB2.72 MBidaho.json10.92 MB2.57 MBidaho.pbf1.37 MB1.17 MBidaho.topo.json1.9 MB612 KBidaho.topo.pbf567 KB479 KBUsageCommand line:geobufencode<example.json>example.pbfgeobufdecode<example.pbf>example.pbf.jsonAs a module:importgeobufpbf=geobuf.encode(my_json)# GeoJSON or TopoJSON -> Geobuf stringmy_json=geobuf.decode(pbf)# Geobuf string -> GeoJSON or TopoJSONTheencodefunction accepts a dict-like object, for example the
result ofjson.loads(json_str).Bothencode.pyandgeobuf.encodeaccept two optional arguments:precision— max number of digits after the decimal point in
coordinates,6by default.dimensions— number of dimensions in coordinates,2by
default.Testspy.test-vThe tests run through all.jsonfiles in thefixturesdirectory,
comparing each original GeoJSON with an encoded/decoded one.
Package: geoca
geocaA Python module for CA models based on geo-raster dataFree software: MIT LicenseDocumentation:https://Haorui-Jiang.github.io/geocaFeaturesRead raster data from a file, convert the data into a Python list.Create a raster template based on the input raster, and replace the data from list into the new raster.Running CA models based on raster data to analyze population and other resource migration.File Templatecookiecutter-pypackage: Cookiecutter template creating a Python package with mkdocs.AcknowledgementSpecial thanks toDr. Qiusheng Wufrom the University of Tennessee for his generous sharing. I have learned a great deal fromDr. Wu's YouTube videos, and with his guidance, I was able to develop geoca step by step. Once again, I express my deepest gratitude to Dr. Wu.
Package: geo-calcs
A Python package for caclulations and visualizations in geological sciences.Free software: MIT licenseDocumentation:https://geo-calcs.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.# History## 0.1.1 (2021-06-10)
### Changed
- Built a few tests.
- Reorganized file structures
- Ran flake8 for style corrections
- Attempted to run tox, but tox currently fails.## 0.1.0 (2021-06-09)
- First release on PyPI.