Saravanan666/pypi-org huggingface dataset:
Package: geometatool
Geospatial Metadata Toolkit (GeoMetaTool)The Geospatial Metadata Toolkit is a Python package designed to streamline geospatial metadata operations. Whether you're working with GIS data, satellite imagery, or any other geospatial information, GeoMetaTool provides a convenient set of tools for three primary tasks:metadata extraction,metadata conversion, andmetadata validation.Metadata extractionGeoMetaTool simplifies the process of extracting essential metadata from various types of geospatial data. It allows you to access key information about spatial datasets, including details such as coordinate reference systems, temporal extents, data sources, and more. With this feature, you can save time and avoid the hassle of manually retrieving metadata from your geospatial assets. Specifically, it extract geospatial metadata inISO 19115-3:2023schema embeddingISO 19110:2016as the contentInfo element. ISO 19115-3:2023 defines an integrated XML implementation of ISO 19115-1 and ISO 19115-2 while ISO 19110:2016 defines the methodology for cataloguing feature types.The package is currently under development to support the following geospatial dataVector: shp, kml, mif, gml, dxf, geojsonRaster: tif, tiffCollection: gdb, gpkgimportgeometatooliso_metadata=geometatool.extract_metadata(input_file_path,default_metadata_values=[])iso_metadata.save_to_file(output_file_path)Metadata conversionDealing with different metadata standards across geospatial projects can be cumbersome. GeoMetaTool offers a solution by enabling the conversion of metadata between different schemas. This is achieved through a conversion table between ISO 19115-3 and other predefined schemas. This feature promotes interoperability and consistency across diverse geospatial data sources.The package is currently under development to support the following geospatial metadataXML schema: ISO 19115-3/19110, QGIS metadataJSON schema: NADA geospatial metadata, World Bank DDH (Development Data Hub) metadataimportgeometatoolconverted_metadata=geometatool.convert_metadata(input_file_path,conversion_table_file_path,target_schema_file_path)withopen(output_file_path,"w")asf:json.dump(converted_metadata,f,indent=4)Metadata validationGeoMetaTool allows you to validate the correctness of metadata syntax, ensuring that your geospatial metadata adheres to ISO 19115-3 or predefined json schema.importgeometatool

geometatool.validate_xml_ISO19115_3(instance_file_path)# The XML is well formed.# The instance is valid aginst the ISO 19115-3 schemas.geometatool.validate_json(instance_file_path,schema_file_path)# The instance is valid aginst the provided schema.
Package: geomet-data-registry
geomet-data-registryOverviewgeomet-data-registry provides a searchable real-time inventory of MSC weather,
climate and water data.InstallationRequirementsPython 3virtualenvDependenciesDependencies are listed inrequirements.txt. Dependencies
are automatically installed during installation.Installing geomet-data-registry# setup virtualenvpython-mvenvgeomet-data-registrycdgeomet-data-registry
.bin/activate# clone codebase and installgitclonehttps://github.com/ECCC-MSC/geomet-data-registry.gitcdgeomet-data-registry
pipinstall-rrequirements.txt
pipinstall-rrequirements-dev.txt
pipinstall-e.# configure environmentcpgeomet-data-registry.envdev.env
videv.env# edit paths accordingly.dev.envRunning# helpgeomet-data-registry--help# get versiongeomet-data-registry--version# setup tileindexgeomet-data-registrytileindexsetup# teardown tileindexgeomet-data-registrytileindexteardown# setup storegeomet-data-registrystoresetup# list all store keysgeomet-data-registrystorelist# list all store keys filtering on a regexgeomet-data-registrystorelist--pattern="RADAR*"# list all store keys filtering on a fancier regexgeomet-data-registrystorelist--pattern="RADAR*time$"# teardown storegeomet-data-registrystoreteardown# set key/value in storegeomet-data-registrystoreset--key=somekey--config=/path/to/file# setup metadatageomet-data-registrymetadatasetup# start upsr_subscribepath/to/amqp.confforeground# dev workflows# process a test filegeomet-data-registrydataadd--file=/path/to/file# process a test directory of files (recursive)geomet-data-registrydataadd--directory=/path/to/directoryDevelopmentRunning TestsTODOReleasingpythonsetup.pysdistbdist_wheel--universal
twineuploaddist/*Code ConventionsPEP8Bugs and IssuesAll bugs, enhancements and issues are managed onGitHub.ContactTom Kralidis
Package: geometric
This is a geometry optimization code for molecular structures.
The code works by calling external software for the energy and
gradient through wrapper functions.  Q-Chem, TeraChem, Psi4,
Molpro, and Gaussian 09/16 are supported quantum chemistry
codes through the command line interface.  The PySCF and
QCArchive packages also provide interfaces to geomeTRIC for
optimization. MM optimizations using OpenMM and Gromacs are
also supported through the command line interface.Documentation is hosted athttps://geometric.readthedocs.io/.Authors: Lee-Ping Wang, Chenchen SongContributors: Yudong Qiu (Psi4 engine, error handling); Daniel G. A. Smith (Testing framework, QCEngine JSON API); Sebastian Lee (Molpro engine); Chaya Stern (Travis, Conda); Qiming Sun (Custom engine); Alberto Gobbi (Batch energy/gradient, logging); Josh Horton (Convergence criteria, Gaussian engine)Contact Email:leeping@ucdavis.eduIf this code has benefited your research, please support us by citing:Wang, L.-P.; Song, C.C. (2016) "Geometry optimization made simple with translation and rotation coordinates", J. Chem, Phys. 144, 214108.http://dx.doi.org/10.1063/1.4952956Quick HelpPackage dependencies are:
Python 2.7, 3.5+
NumPy, Scipy, NetworkXTo install the code from source, run "python setup.py install".
To install the latest release from pip, run "pip install geometric".
To install the latest release from conda-forge, run "conda install -c conda-forge geometric".To execute the geometry optimizer, run "geometric-optimize".
Use "-h" to see the list of command line options.Generally, you will need a .xyz file for the coordinates and
one of the supported quantum chemistry software packages installed
on your system.Supported QM packages are: TeraChem, Q-Chem, Molpro, Psi4, and Gaussian 09/16.
Supported MM packages are: OpenMM, Gromacs.Please refer to the example calculations for how to run the code.The commands to execute the code are contained in "command.sh".CitationIf this code has benefited your research, please support us by citing our publication:Wang, L.-P.; Song, C.C. (2016) "Geometry optimization made simple
with translation and rotation coordinates", J. Chem, Phys. 144, 214108.
DOI: 10.1063/1.4952956
Package: geometricalgebra
geometricalgebraLibrary implementing conformal geometric algebra.The key features are:Fast numerical implementation of multivector and its exterior algebraThe library supports various backends (numpy, tensorflow, jax)Full support of autograd works when using jax and tensorflowAll operation work for single multivector or tensors of multivector. Broadcasting is also supported.InstallationFrom source:git clone git@github.com:wandelbotsgmbh/geometricalgebra.git
pip install ./geometricalgebra/Examplefrom geometricalgebra import cga3d
a = cga3d.e_0
b = cga3d.e_1.up()
c = cga3d.e_2.up()
circle = a ^ b ^ c
# The radius of a circle going through [0, 0, 0], [1, 0, 0], and [0, 1, 0]
radius = circle.circle_to_center_normal_radius()[2].to_scalar()
Package: geometric-algebra-attention
IntroductionThis is a library in development for implementing geometric algebra
attention mechanisms (as detailed in the paperGeometric Algebra
Attention Networks for Small Point
Cloudsin tensorflow, keras,
pytorch, and jax.InstallationThe latest tagged version ofgeometric-algebra-attentionis
available on PyPI for installation via pip:$ pip install geometric-algebra-attentionAlternatively, install the package from source:$ git clone https://github.com/klarh/geometric_algebra_attention
$ pip install ./geometric_algebra_attentionDocumentationThe documentation is available as standard sphinx documentation:$ cd doc
$ pip install -r requirements.txt
$ make htmlAutomatically-built documentation is available athttps://geometric-algebra-attention.readthedocs.io.ExamplesJupyter notebook examples for various backends are available in theexamplesdirectory of the source repository.
Package: geometrical-layout
Dowell Geometrical Layout Python SDK LibraryThis client is designed for the Dowell Geometrical Layout of Big Data APIGeometrical Layout Api.
By specifying the radius, length, and width, users can determine the number of circles
that can be arranged in a triangular packaging, minimizing wastage of space.
The coordinates of the circle can also be determine.InstallationThis library currently supports Python 3.5+. Therequestspackage is required.We recommend usingpipandvirtualenvfor Installation. Please note
that the Python package is calledgeometrical-layout.Installing a Released Versionvirtualenvenvsourceenv/bin/activatepipinstallgeometrical-layoutExamplefromgeometrical_layoutimportGeometricalLayoutApireq=GeometricalLayoutApi()Methodspost_objectThe endpoint receive a json object of radius, length, width.
Resonse isdictcontaining number of circles and coordinates.Parametersradius- Afloator anintthat identifies a circle radius, is needed
to make API calls.length- Anintdenoting the length of the space in canvaswidth- Aintdenoting the length of the space in canvas.
is the last parameter needed to make API request.Examples# Get the number of circles from the response.numberOfcircle=req.post_object(radius,length,width)print(numberOfcircle)LicensingThis library uses theApache License, version 2.0. Please see the library’s
individual files for more information.Support & DiscussionFor Support and Discussion, visit the official DoWellUXLab website.
Documentation is available athttps://geometrical-layouts.readthedocs.io/en/latest/index.html
Package: geometric-calibration
Geometric CalibrationA utility to perform Geometric Calibration of a C-Arm Structure mounted on a robotic systemFree software: MIT licenseDocumentation:https://geometric-calibration.readthedocs.io.FeaturesC-arm calibration in two supported modality: 3D (CBCT), planar (AP/RL)Support for reading projection in .raw or .hnc (Varian) formatPlot calibration resultsSave LUT in .txt fileCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.2.0 (2020-07-22)Added support for planar calibrationAdded –config parameter to easily setup calibration with an .ini fileAdded support to read automatically .raw and .hnc projection files0.1.2 (2020-04-01)Fixed bugs in release 0.1.10.1.1 (2020-04-01)First release on PyPI.
Package: geometric-graphs
Geometric GraphsGenerate knowledge graphs with interesting geometries, like lattices.
Works on Python 3.9+ because it uses cool new features. Get out of that 2020
funk and install the most recent version of Python.💪 Getting StartedSeehttps://github.com/cthoyt/translational-toysfor example usage to generate
charts like.🚀 InstallationThe most recent release can be installed fromPyPIwith:$pipinstallgeometric_graphsThe most recent code and data can be installed directly from GitHub with:$pipinstallgit+https://github.com/cthoyt/geometric-graphs.gitTo install in development mode, use the following:$gitclonegit+https://github.com/cthoyt/geometric-graphs.git
$cdgeometric-graphs
$pipinstall-e.👐 ContributingContributions, whether filing an issue, making a pull request, or forking, are appreciated. SeeCONTRIBUTING.rstfor more information on getting involved.👋 Attribution⚖️ LicenseThe code in this package is licensed under the MIT License.🍪 CookiecutterThis package was created with@audreyfeldroy'scookiecutterpackage using@cthoyt'scookiecutter-snekpacktemplate.🛠️ For DevelopersSee developer instrutionsThe final section of the README is for if you want to get involved by making a code contribution.❓ TestingAfter cloning the repository and installingtoxwithpip install tox, the unit tests in thetests/folder can be
run reproducibly with:$toxAdditionally, these tests are automatically re-run with each commit in aGitHub Action.📦 Making a ReleaseAfter installing the package in development mode and installingtoxwithpip install tox, the commands for making a new release are contained within thefinishenvironment
intox.ini. Run the following from the shell:$tox-efinishThis script does the following:Uses BumpVersion to switch the version number in thesetup.cfgandsrc/geometric_graphs/version.pyto not have the-devsuffixPackages the code in both a tar archive and a wheelUploads to PyPI usingtwine. Be sure to have a.pypircfile configured to avoid the need for manual input at this
stepPush to GitHub. You'll need to make a release going with the commit where the version was bumped.Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can
usetox -e bumpversion minorafter.
Package: geometricLib
Geometric LibraryWhat is this?The module allows you to calculate the area of a circle by radius and a triangle by three sidesUsingLet's import it first:
First, import everything from the library (use thefrom...import GeometryCalculatorconstruct).The library has 2 functions:calculate the area of a circle by radius:circle_area(radius)calculate the area of a triangle on three sidestriangle_area(side1, side2, side3)Examples of all operations:radius = 5
circle_area = GeometryCalculator.circle_area(radius)
print(f"The area of a circle with radius {radius} is equal to {circle_area:.2f}")

side1 = 3
side2 = 4
side3 = 5
triangle_area = GeometryCalculator.triangle_area(side1, side2, side3)
print(f"the area of a triangle with sides {side1}, {side2} and {side3} is equal to {triangle_area:.2f}")`DeveloperMy github:link
Package: geometric-smote
geometric-smoteCategoryToolsDevelopmentPackageDocumentationCommunicationIntroductionThe packagegeometric-smoteimplements the Geometric SMOTE algorithm, a geometrically enhanced drop-in replacement for SMOTE. It
is compatible with scikit-learn and imbalanced-learn. The Geometric SMOTE algorithm can handle numerical as well as categorical
features.InstallationFor user installation,geometric-smoteis currently available on the PyPi's repository, and you can
install it viapip:pipinstallgeometric-smoteDevelopment installation requires cloning the repository and then usingPDMto install the
project as well as the main and development dependencies:gitclonehttps://github.com/georgedouzas/geometric-smote.gitcdgeometric-smote
pdminstallUsageAll the classes included ingeometric-smotefollow theimbalanced-learnAPI using the
functionality of the base oversampler. Usingscikit-learnconvention, the data are represented
as follows:Input dataX: 2D array-like or sparse matrices.Targetsy: 1D array-like.The clustering-based oversamplers implement afitmethod to learn fromXandy:gsmote_oversampler.fit(X,y)They also implement afit_resamplemethod to resampleXandy:X_resampled,y_resampled=gsmote.fit_resample(X,y)Citinggeometric-smoteIf you usegeometric-smotein a scientific publication, we would appreciate citations to the following paper:Douzas, G., Bacao, B. (2019). Geometric SMOTE: a geometrically enhanced
drop-in replacement for SMOTE. Information Sciences, 501, 118-135.https://doi.org/10.1016/j.ins.2019.06.007Publications using Geometric-SMOTE:Fonseca, J., Douzas, G., Bacao, F. (2021). Increasing the Effectiveness of
Active Learning: Introducing Artificial Data Generation in Active Learning
for Land Use/Land Cover Classification. Remote Sensing, 13(13), 2619.https://doi.org/10.3390/rs13132619Douzas, G., Bacao, F., Fonseca, J., Khudinyan, M. (2019). Imbalanced
Learning in Land Cover Classification: Improving Minority Classes’
Prediction Accuracy Using the Geometric SMOTE Algorithm. Remote Sensing,
11(24), 3040.https://doi.org/10.3390/rs11243040
Package: geometricus
Geometricus Represents Protein Structures as Shape-mers derived from Moment InvariantsA structure-based, alignment-free embedding approach for proteins. Can be used as input to machine learning algorithms.See thedocumentation.InstallationGeometricus is a Python (3.7+) package with NumPy, SciPy, Numba and ProDy as dependencies.Install withpip install geometricusUsageSee theGetting Startedpage for example usage.PublicationsJanani Durairaj, Mehmet Akdel, Dick de Ridder, Aalt D J van Dijk, Geometricus represents protein structures as shape-mers derived from moment invariants,
Bioinformatics, Volume 36, Issue Supplement_2, December 2020, Pages i718–i725,https://doi.org/10.1093/bioinformatics/btaa839Janani Durairaj, Mehmet Akdel, Dick de Ridder, Aalt D.J. van Dijk, Fast and adaptive protein structure representations for machine learning,
bioRxiv 2021.04.07.438777; doi:https://doi.org/10.1101/2021.04.07.438777
Package: geometriq
GeometriqGeometriq is an extensible library for creating 2D geometrical art.It provides a single, unified API for creating a variety of shapes
and transforms, and abstracts away the implementation details of
numerous backend systems, including:CoreGraphics(macOS and iOS)Pythonista'scanvas module(partial, for iOS)Pillow(partial)PyX(rudimentary)As well as planned:Cairo(on Linux, macOS, and Windows)
Package: geometry1
Библиотека ‘geometry1’ обеспечивает следующий
интерфейс доступа к классам:
1.1 Доступ к точке (class Point)
1.2 Доступ к линии (class Line)
1.3 Доступ к треугольнику (class Triangle)
В классе ‘Line’ реализуется метод length, который
находит расстояние между двумя точками.
В классе ‘Triangle’ реализуется метод perimeter, которые
находит периметр треугольника по координатам его вершин.
В этом классе также реализуется метод square,
который находит площадь треугольника по координатам его
вершин.
Package: geometry-analysis-step
SEAMM Geometry Analysis Plug-inA SEAMM plug-in for analysis of the geometry of particularly small moleculesFree software: BSD-3-ClauseDocumentation:https://molssi-seamm.github.io/geometry_analysis_step/index.htmlCode:https://github.com/molssi-seamm/geometry_analysis_stepFeaturesThe Geometric Analysis plug-in provides the following functionality:Generating lists of all valence terms – bonds, angles, dihedrals, and
out-of-planes – plus their value for the current structure. This functionality
relies on the description of the bonds in the structure.Creating lists of terms and their values as specified in the input. Note that in
this case the atoms do not need to be connected by bonds in the structure.Printing the tables of results to the output.Outputing the data to one or more tables.Storing the data in the internal SEAMM database.AcknowledgementsThis package was created with themolssi-seamm/cookiecutter-seamm-plugintool, which
is based on the excellentCookiecutter.Developed by the Molecular Sciences Software Institute (MolSSI),
which receives funding from theNational Science Foundationunder
award CHE-2136142.History2022.12.24 (2022-12-24)Plug-in created using the SEAMM plug-in cookiecutter.
Package: geometry-box
A simple python package for geometrical operations.importgeometry_boxasgbcircle=gb.Circle(radius=2.0,cent=(3.0,6.0))print(circle.area)# prints circle areaprint(circle.perimeter)# prints circle perimetercircle.shape_factor()# returns shape factor: perimeter/equivalent circle perimeter.circle.eval_locus(num_points=50)# finds 50 points along the periphery of circlecircle.eval_locus(num_points=250).plot()# plots a circle displays using `matplotlib.pyplot.show()`circle.eval_locus().plot(f_path='/path/to/file')# saves a plot at the specified path#importmatplotlib.pyplotaspltfig,axs=plt.subplots()circle.eval_locus().plot(axis=axs)# plots circle on the axs objectA sample code for plotting various shapesimportgeometry_boxasgbfromosimportpathimportmatplotlib.pyplotaspltfig,axs=plt.subplots(2,4)gb.Circle().eval_locus().plot(axis=axs[0,0])gb.Ellipse().eval_locus().plot(axis=axs[0,1])gb.RegularPolygon(3).eval_locus().plot(axis=axs[0,2])gb.Rectangle().eval_locus().plot(axis=axs[0,3])gb.BoundingBox2D().eval_locus().plot(axis=axs[1,0])gb.CShape().eval_locus().plot(axis=axs[1,1])gb.Capsule().eval_locus().plot(axis=axs[1,2])gb.NLobeShape(3).eval_locus().plot(axis=axs[1,3])plt.tight_layout()plt.savefig(path.join(path.dirname(__file__),"shapes.pdf"))plt.close()It produces the following figure
Package: geometrydash
GeometryDash.pyA simple API wrapper for Geometry Dash 2.1 written in Python.Key FeaturesModern Pythonic API usingasyncandawaitsyntaxHigh coverage of the supported Geometry Dash APIInstallingPython 3.6 or higher is requiredpipinstallgeometrydashExamplesLevel Exampleimportgeometrydashasgdlevel=awaitgd.search_level("zodiac")level.featured# Truelevel.coins# 0Profile Exampleimportgeometrydashasgduser=awaitgd.search_user("robtop")user.name# 'RobTop'user.moderator# 'elder'LicenceGNU General Public Licence v3.0LinksGeometry Dash API
Package: geometryIO
Here is a GDAL wrapper for reading and writing geospatial data to a variety of vector formats.  For a list of supported vector formats and driver names, please seehttp://www.gdal.org/ogr/ogr_formats.htmlInstallationHere are instructions for installinggeometryIOin avirtualenv# Prepare isolated environment
ENV=$HOME/Projects/env
virtualenv $ENV
mkdir $ENV/opt

# Activate isolated environment
source $ENV/bin/activate

# Install GDAL
sudo yum -y install swig
GDAL_VERSION=1.9.0
GDAL_FILENAME=gdal-$GDAL_VERSION
cd $ENV/opt
wget http://download.osgeo.org/gdal/$GDAL_FILENAME.tar.gz
tar xzvf $GDAL_FILENAME.tar.gz
cd $GDAL_FILENAME
./configure --prefix=$ENV --with-python
make install

# Install package
export LD_LIBRARY_PATH=$ENV/lib:$LD_LIBRARY_PATH
easy_install -U geometryIOUsagePrepare environmentENV=$HOME/Projects/env
source $ENV/bin/activate
export LD_LIBRARY_PATH=$ENV/lib:$LD_LIBRARY_PATH
ipythonRun codeimport geometryIO
import datetime
import itertools
from osgeo import ogr
from shapely import geometry

geometryIO.save(
    # Save to a compressed shapefile
    targetPath='polygons.shp.zip',
    # Declare that source coordinates are in longitude and latitude
    sourceProj4=geometryIO.proj4LL,
    # Specify geometries using shapely
    shapelyGeometries=[
        geometry.Polygon([(0, 0), (0, 10), (10, 10), (10, 0), (0, 0)]),
        geometry.Polygon([(10, 0), (10, 10), (20, 10), (20, 0), (10, 0)]),
    ],
    # Specify attributes for each geometry
    fieldPacks=[
        ('xxx', 11111, 44444.44, datetime.datetime(1980, 1, 1)),
        ('yyy', 22222, 88888.88, datetime.datetime(1990, 1, 1)),
    ],
    # Define attributes
    fieldDefinitions=[
        ('Name', ogr.OFTString),
        ('Population', ogr.OFTInteger),
        ('GDP', ogr.OFTReal),
        ('Date', ogr.OFTDate),
    ],
    # Specify desired vector format
    driverName='ESRI Shapefile',
    # Transform coordinates to spherical mercator
    targetProj4=geometryIO.proj4SM)

proj4, shapelyGeometries, fieldPacks, fieldDefinitions = geometryIO.load(
    # Load from a compressed shapefile
    sourcePath='polygons.shp.zip',
    # Transform coordinates to longitude and latitude
    targetProj4=geometryIO.proj4LL)
for shapelyGeometry, fPack in itertools.izip(shapelyGeometries, fieldPacks):
    print
    for fValue, (fName, fType) in itertools.izip(fPack, fieldDefinitions):
        print '%s = %s' % (fName, fValue)
    print shapelyGeometry

geometryIO.save_points(
    # Save to a compressed shapefile
    targetPath='points.shp.tar.gz',
    # Declare that source coordinates are in longitude and latitude
    sourceProj4=geometryIO.proj4LL,
    # Specify coordinates
    coordinateTuples=[
        (0, +1),
        (+1, 0),
        (0, -1),
        (-1, 0),
    ])
print geometryIO.load_points('points.shp.tar.gz')[1]0.9.7Fix Python 3 compatibility0.9.5Prioritized shapefiles when loading archives containing multiple formats0.9.4Improved handling of empty datetime fields0.9.3Added support for datetime attributesReplaced feature.SetField() with feature.SetField2()0.9.2Ported code from geometry_store