Saravanan666/pypi-org huggingface dataset:
Package: geomapviz
buy me caffeine🗺️🐍 Geomapviz - Python Library for Beautiful and Interactive Geospatial Tabular Data Visualization 🚀Geomapviz is a Python library for visualizing geospatial tabular data. It aggregates tabular data at the geoid level, merges it with the shapefile, and provides a simple API to plot the average for single or multiple columns. The library is designed to create beautiful and interactive visualizations that help users better understand geospatial data. Geomapviz can produce a single map or a panel of maps, making it useful for comparing how different models capture geographical patterns. The package also supports returning average values either raw or automatically binned. Additionally, it allows users to customize the background color, including the option to switch from a black background to a light one. The styling is handled by a DataClass, PlotOptions, object is used to specify various arguments for creating a geospatial plot of a datasetGeomapviz ReadTheDocsInstallation$ conda install -c conda-forge cartopythen$ pip install geomapviz -UIf you face acartopyinstallation error, try to install first from pre-built binariesconda install -c conda-forge cartopy. For installing on Linux platform, some dependencies are required, seethe cartopy documentationfor details.If you face a geoviews installation error, tryconda install -c pyviz geoviewsDocumentationThedocumentation notebookillustrates the functionality ofgeomapvizChangelog1.0Complete refactoring of the library, including modular features and simpler code base0.6Including files in source distributions0.5[Bug] Capital letter in importing the BE shapefile[Bug] Changed default values of arguments0.4Make Belgian shp available using load_be_shpMore decimalUser defined alpha for the interactive maps0.3Bound functions to the upper level0.2First version0.1First version
Package: geomark
A small python library that provides implementations of the BC
GovernmentsGeomark Web
ServiceInstallationOption 1 - Clone this repository and install manuallyWe will do our best to keep the master branch of this repository
stable. However you could also checkout the tag corresponding to the
release you would like…git clonehttps://github.com/greg-and-adam/python-geomark.gitthe cd into the directory where this repository was clonedcd/path/to/python-geomarkInstall using setup.pypython setup.py installOr…Follow step 1 above then install using pippip install/path/to/cloned/python-geomarkTODO List more installation methods here as they become supported…UsageA Geomark object can be instantiated with either a Geomark ID or a full
Geomark URL.We recommend using the Geomark ID.fromgeomarkimportGeomarkgm=Geomark('gm-abcdefghijklmnopqrstuv0bcislands')# or...gm=Geomark('https://apps.gov.bc.ca/pub/geomark/geomarks/gm-abcdefghijklmnopqrstuv0bcislands')This library supports all of the basic read functions from the Geomark
API.Reponse results are returned as a bytes string. It can be parsed using
the appropriate library.The default format is ‘json’ which will return a json parsable byte
string. When using the JSON format any geometries will be formatted as
EWKT.Any of thesupported file
formatsmay be requested.importjsonfromgeomarkimportGeomarkgm=Geomark('gm-abcdefghijklmnopqrstuv0bcislands')info=json.loads(gm.info())parts=json.loads(gm.parts('geojson'))# geojson is also supported.Data can also be requested in any of thesupported coordinate
systems.importjsonfromgeomarkimportGeomarkgm=Geomark('gm-abcdefghijklmnopqrstuv0bcislands')parts_bcalbers=json.loads(gm.parts('geojson',3005))If you get data in a format you wish to write to a file you may do so by
simply opening a file location as writable in binary mode. (wb)fromgeomarkimportGeomarkgm=Geomark('gm-abcdefghijklmnopqrstuv0bcislands')feature_file=gm.feature('shpz')withopen('bc_islands.shpz','wb')asfile:file.write(feature_file)TestingUsing toxThe recommended way to run the tests is by usingtox, which can be installed
usingpip install tox.You can usetox-lto list the available environments, and then e.g.
use the following to run all tests with Python 3.6tox -e py36Running tests manuallyPlease refer to thetox.inifile for reference/help in
case you want to run tests manually / without tox.ContributingFork it!Create your feature branch:git checkout-bmy-new-featureCommit your changes:git commit-am'Add some feature'Push to the branch:git push originmy-new-featureSubmit a pull request :DHistoryRecent changes can be viewed in theCHANGELOG.mdfile.CreditsAdam Valair(Primary
Developer/Maintainer)Greg Sebastian(Primary
Developer/Maintainer)LicenseThis project is licensed under the BSD 3-Clause License - see theLICENSEfile for details
Package: geomate
Features:Batch geocoding large amount of data.Address or coordinates (reverse geocoding) supported.Automatically handle input type, so you can mix address and coordinate in an array.Automatically store data in Sqlite database.Smart API keys quota management, you would never stuck at getting GeocoderQuotaExceeded error.Built in logging system, or you can plug on yours.Quick result lookup.Quick Link:GitHub HomepageOnline DocumentationPyPI downloadInstallIssue submit and feature requestUsage ExampleBasically, you only need to do three things, and geomate will take care of everything else for you:Prepare your API keys.Give the path of your database file.Create a list of address or coordinate you want to geocode.importgeomateimportpprint# put your google map API key here, what you see here is all fakeapi_keys=["GoogleGeoCodingApiKey01",# user1"GoogleGeoCodingApiKey02",# user2..."GoogleGeoCodingApiKey99",# user991ihu8",]# create a google geocode API clientgooglegeocoder=geomate.GoogleGeocoder(api_keys=api_keys)googlegeocoder.check_usable()# show you which keys are available# bind to a geocoder and databasebatch=geomate.BatchGeocoder(googlegeocoder,db_file="geocode.sqlite3")# construct your todo address list anyway you wantlist_of_address=["675 15th St NW Washington, DC 20005","2317 Morgan Ln Dunn Loring, VA 22027","1201 Rockville Pike Rockville, MD 20852",(39.085801,-77.084513),(38.872719,-77.306417),(38.902027,-77.053536),]batch.process_this(list_of_address)# process everything# see what's we have doneforaddressinlist_of_address:pprint.pprint(batch.lookup(address))Advance UsageSuppose you got 10000 address, but 5000 of them are invalid address, which is not able to geocoded. And invalid address happens to be at begin of the queue. You probably don’t want to waste API quota on that. geomate provide a keywordshuffleto randomlly perform the geocoding, so in average, you can get most of valid address geocoded in a short time:batch.process_this(list_of_address,shuffle=True)Here’s something may helpful. Basically there are two steps in the processing:Insert to-do addresses as primary key into a table, and skip address already in database.Do geocoding.So you can take advantage of this for more flexible batch geocoding process.batch.add_addresses(list_of_address)batch.process_all(shuffle=True)Lookup the geocoded dataYou can easily retrieve the geocoded data by:foraddressinlist_of_address:data=batch.lookup(address)pprint(data)Of course you can also manually read it from sqlite database.Installgeomateis released on PyPI, so all you need is:$pipinstallgeomateTo upgrade to latest version:$pipinstall--upgradegeomatePrerequisit:geopy, to install:$pipinstallgeopy
Package: geomathlib
📚 Geomathlibgeomathlib - это Python библиотека, предоставляющая функциональность для работы с геометрическими фигурами. Библиотека предоставляет абстрактные классы и методы для расчета площадей фигур, а также определения типа треугольника.Основные компонентыКлассFigureFigure- это абстрактный базовый класс для геометрических фигур. Он определяет абстрактный методarea(), который должен быть реализован в подклассах.КлассCircleCircleпредставляет круг и содержит методы для расчета площади круга. Он также имеет строковое представление круга.КлассTriangleTriangleпредставляет треугольник и содержит методы для расчета площади треугольника. Он также имеет строковое представление треугольника.КлассTriangleTypeTriangleTypeпредоставляет методis_right_triangle(), который проверяет, является ли треугольник прямоугольным.КлассFigureAreaFigureAreaпредоставляет методcalc_area(), который вычисляет площадь фигуры с использованием методаarea()фигуры.ИспользованиеПример использования библиотеки:fromsrc.area_calculatorimportCircle,Triangle,FigureArea# Создаем кругcircle=Circle(5)print(f"Площадь круга:{circle.area()}")# Создаем треугольникtriangle=Triangle(3,4,5)print(f"Площадь треугольника:{triangle.area()}")# Проверяем, является ли треугольник прямоугольнымifTriangleType.is_right_triangle(triangle):print("Треугольник является прямоугольным")else:print("Треугольник не является прямоугольным")# Вычисляем площадь фигуры через экземпляр классаfigure_area=FigureArea(circle)print(f"Площадь фигуры:{figure_area.calc_area()}")
Package: geomcompare
Compare two sets of geometrical features.GeomCompareprovides multiple tools for comparing two independant
sets of geometrical features. It can be used to identify features with
similar geometry (based on pre-defined similarity functions) found in
both sets, as well features with geometry that are found in only one
of the sets.GeomComparedefines a few similarity functions, but it
possible for the user to define its own customized similarity
functions.InstallationRequirementsGeomComparerequiresPython >= 3.9.In addition, for a fully fledged installation ofGeomCompareand
to have access to all functionalities provided by the library, the
user need to install the following:shapelynumpypsycopg2rtreepyprojgdal(core libraries and Python bindings)spatialiteNotemod_spatialitemust be installed and accessible fromsqlite3:importsqlite3conn=sqlite3.connect(":memory:")conn.enable_load_extension(True)conn.load_extension("mod_spatialite")PIPIf you usepip, you can installGeomComparewith:pip install geomcompareDockerADockerimage forGeomCompareis also available onDockerHub:Run the geomcompare image and start an iPython session inside the container:docker run-itmtachon/geomcompareRun the geomcompare image, and mount the current directory into thedatafolder of the container:docker run--entrypointbash-v`pwd`:/data-w/data-itmtachon/geomcompareFor more information onDockerand command-line arguments, see:https://docs.docker.com/andhttps://docs.docker.com/engine/reference/run/.UsageFor guidance on how to useGeomCompare, a user guide and API
documentation are available on the project’s
homepage (https://geomcompare.readthedocs.io/en/latest/).
Package: geomdl
Introductiongeomdlis a pure Python, object-oriented B-Spline and NURBS library. It is compatible with Python versions 2.7.x,
3.4.x and later.Featuresgeomdlprovides convenient data structures and highly customizable API for rational and non-rational splines along
with the efficient and extensible implementations of the following algorithms:Spline evaluationDerivative evaluationKnot insertionKnot removalKnot vector refinementDegree elevationDegree reductionCurve and surface fitting via interpolation and least squares approximationgeomdlalso provides customizable visualization and animation options via Matplotlib, Plotly and VTK libraries.
Please refer to thedocumentationfor more details.Further readingDocumentation:https://nurbs-python.readthedocs.io/Examples:https://github.com/orbingol/NURBS-Python_ExamplesWiki:https://github.com/orbingol/NURBS-Python/wikiDevelopment:https://github.com/orbingol/NURBS-PythonModules extending geomdlgeomdl-cligeomdl-shapesAdditional file format supportrw3dmrwsatLicensegeomdlis licensed under the terms of theMIT License.
Package: geomdl.cli
geomdl.climodule provides a set of commands for usinggeomdllibrary from
the command line.InstallationFrom PyPIInstalling viapipis the easiest and the recommended method$pipinstall--usergeomdl.cliFrom the repositoryClone the repository:git clonehttps://github.com/orbingol/geomdl-cli.gitInside the directory containing the cloned repository, run:pip install--user.The setup script will install all required dependenciesUsing geomdl-cliAfter the package installation, the command-line applicationgeomdl-cliwill be automatically available. If you are
getting file not found errors, please make sure that Python and its scripts directory is listed under the PATH
environmental variable.The following structure explains the most basic usage of the command-line application.geomdl-cli{command} {options}{command}represents the functionality thatgeomdl-cliapplication will be running. Please refer to the list
below for the commands which come with the package by default.{options}represents the parameters that is being input to the command. Every command comes with a different set of
options. Please refer to the individual command help for more details.Available commandshelp:displays the package help text, e.g. available commands, how to use them, etc.version:displays the package versionconfig:displays the configurationplot:plots single or multiple NURBS curves and surfaces using Matplotlibeval:evaluates NURBS shapes and exports the evaluated points in supported formats, e.g. csv, txt and vtkexport:exports NURBS shapes in supported CAD exchange formatsIndividual command helpgeomdl-cli{command}--helpDocumentationhttps://geomdl-cli.readthedocs.io/AuthorOnur Rauf Bingol (@orbingol)Licensegeomdl-cli is licensed under theMIT License
Package: geomdl.shapes
geomdl.shapesmodule provides a set of shortcut functions to generate common spline and analytic shapes usinggeomdl.InstallationInstalling viapipis the easiest and the recommended method:$pipinstall--usergeomdl.shapesAuthorOnur Rauf Bingol (@orbingol)Licensegeomdl-shapes is licensed under theMIT License
Package: geome
geomeThe repo provides a set of tools for creating PyTorch Geometric (PyG) data objects from AnnData objects, which are commonly used for storing and manipulating single-cell genomics data. In addition, the repo includes functionality for creating PyTorch Lightning (PyTorch-Lightning) DataModule objects from the PyG data objects, which can be used to create graph neural network (GNN) data pipelines. The PyG data objects represent graphs, where the nodes represent cells and the edges represent relationships between the cells, and can be used to perform GNN tasks such as node classification, graph classification, and link prediction. The repo is written in Python and utilizes the PyTorch, PyTorch Geometric, and PyTorch-Lightning libraries.Getting startedPlease refer to thedocumentation. In particular, theAPI documentation.InstallationYou need to have Python 3.8 or newer installed on your system. If you don't have
Python installed, we recommend installingMambaforge.There are several alternative options to install geome:Install the latest development version:pipinstallgit+https://github.com/theislab/geome.git@mainRelease notesSee thechangelog.ContactFor questions and help requests, you can reach out in thescverse discourse.
If you found a bug, please use theissue tracker.CreditsSome of the data forDatasetHartmannis distributed in this package.
It was originally retrieved from:https://zenodo.org/record/3951613#.Y9flQS-B1qvThis project was generated from@cjolowicz'sHypermodern Python Cookiecuttertemplate.Citationt.b.a
Package: geo-measures
G_measuresThe "Geometric Measures" script that was developed to carry out geometric analysis on protein structures.
This script support as input MultiPDB files, and the avaliable analysis are:1 - Pincer angle.
                        
Select the residues that will be used to measure the pincer angle. 


            (1)                 (3)
                \              /
                  \          /
                    \      /
                      \  / 
                      (2)

2 - Dihedral angle.

Select the residues that will be used to measure the dihedral angle. 


                 (1)          (4)
                   |           |
                   |           |
                   |           |
                   |           |
                 (2)----------(3)
    
3 - Triangle area.

Select the residues that will be used to measure the triangle area. 


                      AC
              (1)-----------(3)
                \           /
        AB        \        /     BC
                   \      /
                     \  / 
                      (2)
                        
                       


4 - PDF (Probability Density Function)
The Functional Density Function is calculated using different values â€‹â€‹of mainchain dihedral angles from the considered residue, the mainchain conformation of the equivalent residue between frames.

5 - RMSD

Compares two protein structures frames (first frame the first frame with the consecutive frames) by computing the root mean square deviation (RMSD).

6 - RG

Computes the radius of gyration of the protein and the radii of gyration about the x-, y- and z-axes, as a function of frames.

7 - FEL
FEL represents a mapping of all possible conformations a molecule adopted during a simulation, together with their corresponding energy reported as the Gibbs Free Energy. FEL are represented using two variables that reflect specific properties of the system and measure conformational variability. RG measure the torsion angle around a specific bond or the radius of gyration of the protein, and the the RMSD measure the deviation with respective native state (First frame).Contributors:Luciano Porto Kagami, Gustavo Machado das Neves, LuÃ­s Fernando Saraiva Macedo Timmers, Rafael Andrade CÃ¡ceres and Vera Lucia Eifler-LimaThe GROMACS program (http://www.gromacs.org/) is required for the 'Converter' and 'FEL' tools.
The only recognized trajectory file format for G_Measures is multi-PDB.
If the user has only XTC trajectory files, the 'Converter' tool can be used.
Click Browse ..., select the multi-PDB file and follow the steps shown in the status bar.Thank you for using G_Measures!
G_Measures Team
Package: geomeffibem
GeomEffiBEMA small library to facilitate some building energy modeling geometry operations withOpenStudioandEnergyPlusin mind. This started as a test bed while I was implementing source code changes to both aforementioned projects and I realized this had value in creating geometry in an efficient manner as well as being able to visualize simple geometry in 2D via matplotlib.A Jupyter NotebookGeomEffiBEM_Demonstration.ipynbcan serve as a small demonstration of the features.Documentation:https://jmarrec.github.io/geomeffibemGitHub:https://github.com/jmarrec/geomeffibemPyPI:https://pypi.org/project/geomeffibem/This is free software (MIT License) contributed byEffiBEM.Leveraging software,EffiBEMspecializes in providing new ways to streamline your workflows and create new tools that work with limited inputs for your specific applications. We also offer support and training services on BEM simulation engines (OpenStudio and EnergyPlus).
Package: geomeppy
GeomEppyfor Python 2.7, 3.7 via Travisvia CodeCovon Read the DocsGeomEppy is a scripting language for use with Eppy, which in turn is a scripting language for EnergyPlus IDF files and output files.It primarily adds functions to do with IDF geometry, including zones, surfaces, constructions, etc.InstallationIn most cases, all that is needed is:pip install geomeppyIf you have problems installing, you may need to installshapelyfirst. To ensure the required libraries are installed, we recommend installingshapelyusing acondaenvironment, particularly on Windows.DocumentationComplete documentation is hosted atRead the Docs. This covers the internals of geomeppy,
and may be subject to change.Features documented in theBasicssection can be relied upon to remain stable.ReferencingIf you rely on this package in academic work, and would like to include a citation (which we greatly appreciate), please refer to this project as:Bull J, et al. GeomEppy, 2016-,https://github.com/jamiebull1/geomeppy[Online; accessed 2019-09-21].
Package: geomesh
GeomeshA Python package for processing DEM data into georeferenced unstructured meshes using thejigsaw-pythonlibrary.InstallationThe Jigsaw library must be initialized first by running./setup.py install_jigsaw, then, the package can be installed normally by doing./setup.py install:./setup.pyinstall_jigsaw# installs the Jigsaw C-library to the current Python environment./setup.pyinstall# Installs the geomesh library to the current Python environment./setup.pydevelop# run this if you are a developer.RequirementsPython>=3.6CMakeC/C++ compilers
Package: geomesher
GeoMesher: Meshing a GeoDataFrame using GmshFeaturesGeoMesher is a fork ofpandamesh. The original
package included two mesh generators:TriangleandGmsh. This fork only includes the Gmsh mesh generator since
Triangle seems to be not maintained anymore. Also, GeoMesher adds the following new
functionalities:A new method for returning the generated mesh as a GeoDataFrame.A new function calledgdf_mesherthat can generate a mesh from a GeoDataFrame
with a single function call and with sane defaults for the mesh generator.Remap a scalar field from the source GeoDataFrame to the generated mesh,
using an area weighted interpolation method
(based onTobler).HandleMultiPolygongeometries in the source GeoDataFrame.Note that the remapping functionality of GeoMesher is a simple areal interpolation method.
For more advanced interpolation methods, please useTobler.InstallationYou can install GeoMesher usingpip:$pipinstallgeomesheror usingconda(mamba):$condainstall-cconda-forgegeomesherQuick startThe following example shows how to generate a mesh from a GeoDataFrame
using both thegdf_mesherfunction and theMesherclass.We start by getting a GeoDataFrame of South America from the Natural Earth website.
Then, we reproject it to a projected coordinate system (UTM zone 20S).
Finally, we add a new column calledcellsizethat will be used to set the
maximum size of the mesh elements.We use thegdf_mesherfunction to generate the mesh with default parameters
and useMesherto generate the mesh withMESH_ADAPTalgorithm.
We also use thearea_interpolatefunction to remap thePOP_ESTcolumn
from the source GeoDataFrame to the generated mesh.importgeopandasasgpdimportgeomesherasgmworld=gpd.read_file("https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip")south_america=world[world["CONTINENT"]=="South America"]south_america=south_america.explode(ignore_index=True).to_crs(32620)south_america["cellsize"]=500_000.0mesh_auto=gm.gdf_mesher(south_america,intensive_variables=["POP_EST"])mesher=gm.Mesher(south_america)mesher.mesh_algorithm="MESH_ADAPT"mesh_adapt=mesher.generate()mesh_adapt=gm.area_interpolate(south_america,mesh_adapt,intensive_variables=["POP_EST"])ContributingContributions are very welcomed. Please readCONTRIBUTING.rstfile for instructions.CreditsGeoMesher is a fork ofpandamesh(MIT License)
and uses one of the modules inTobler(BSD-3-Clause License).
Package: geometadp
Geophysical Metadata Management using a Juypter NotebookUsageA full Anaconda install should provide all packages required to run the
metadata manager.Open a new Jupyter Notebook (Python 3 >= 3.7) and execute the
following code within on cell:!pip install git+https://github.com/m-weigand/geometadp
import IPython
IPython.display.clear_output()
%gui qt
import geometadp
obj = geometadp.geo_metadata()
obj.manage().. note::The first three lines do install the metadata manager and can be omitted
once it is installed.Wishlistimport functionality: import a given JSON-file and edit it