Saravanan666/pypi-org huggingface dataset:
Package: geoengine
Geo Engine Python PackageThis package allows easy access to Geo Engine functionality from Python environments.TestCreate a virtual environment (e.g.,python3 -m venv env).
Then, install the dependencies with:python3-mpipinstall-e.
python3-mpipinstall-e.[test]Run tests with:pytestDependenciesSince we usecartopy, you need to have the following system dependencies installed.GEOSPROJFor Ubuntu, you can use this command:sudoapt-getinstalllibgeos-devlibproj-devBuildYou can build the package with:python3-mpipinstall-e.[dev]python3-mbuildFormattingThis packages is formatted according topycodestyle.
You can check it by calling:python3-mpycodestyleOur tip is to installautopep8and use it to format the code.LintsOur CI automatically checks for lint errors.
We usepylintto check the code.
You can check it by calling:python3-mpylintgeoengine
python3-mpylinttestsOur tip is to activate linting withpylintin your IDE.DocumentationGenerate documentation HTML with:pdoc3--html--output-dirdocsgeoengineExamplesThere are several examples in theexamplesfolder.
It is necessary to install the dependencies with:python3-mpipinstall-e.[examples]Distribute to PyPITest-PyPIpython3 -m build
python3 -m twine upload --repository testpypi dist/*PyPIpython3 -m build
python3 -m twine upload --repository pypi dist/*Try it outStart a python terminal and try it out:importgeoengineasgefromdatetimeimportdatetimege.initialize("https://nightly.peter.geoengine.io/api")time=datetime.strptime('2014-04-01T12:00:00.000Z',"%Y-%m-%dT%H:%M:%S.%f%z")workflow=ge.workflow_by_id('4cdf1ffe-cb67-5de2-a1f3-3357ae0112bd')print(workflow.get_result_descriptor())workflow.get_dataframe(ge.Bbox([-60.0,5.0,61.0,6.0],[time,time]))AuthenticationIf the Geo Engine server requires authentication, you can set your credentials in the following ways:in the initialize method:ge.initialize("https://nightly.peter.geoengine.io/api", ("email", "password"))as environment variablesexport GEOENGINE_EMAIL="email"andexport GEOENGINE_PASSWORD="password"in a .env file in the current working directory with the content:GEOENGINE_EMAIL="email"GEOENGINE_PASSWORD="password"
Package: geoenrich
geoenrich 0.5.8Package descriptionGeoEnrich provides functionalities to enrich georeferenced events (such as species occurrences) with environmental data from satellites or models. Users can specify a geographic or temporal buffer to include data in the neighbourhood of occurrences into their analyses. Two main outputs are available: a simple summary of the variable in the requested area, or the full data (as a geotiff raster, a png image, or a numpy array).Sea surface temperature, chlorophyll, and 40 other environmental variables are available natively, and others can easily be added by the user. This package is intended for large numbers of occurrences: local storage is implemented to avoid redundant requests to remote servers.The package provides functions to retrieve occurrence data directly from GBIF, or open a custom dataset from any source. Arbitrary areas defined by the user can also be enriched.Documentation onRead the Docs.This project is being developed as part of the G2OI project, cofinanced by the European union, the Reunion region, and the French Republic.InstallationInstallation instructions are in the documentation, forpythonandR.Using the pluginJupyter Notebook tutorials are available forpythonandR.Issues and further developmentsUser suggestionsPlease feel free to raise issues or suggest improvements in theIssues tab.Planned improvementsEnrich an area defined by a shapefile.Add bathymetry from GEBCO.
Package: geo-espresso
EspressoRelated repositories byInLabCoFICoFI ExamplesIntroductionEarthSciencePRoblems for theEvaluation ofStrategies,Solvers andOptimizers (Espresso) is a collection of datasets, and
associated simulation codes, spanning a wide range of geoscience problems.
Together they form a suite of real-world test problems that can be used to
support the development, evaluation and benchmarking of a wide range of tools
and algorithms for inference, inversion and optimisation. All problems are
designed to share a common interface, so that changing from one test problem
to another requires changing one line of code.The Espresso project is a community effort - if you think it sounds useful,
please consider contributing an example or two from your own research. The project
is currently being coordinated by InLab, with support from the CoFI development
team.For more information, please visitour documentation.Installation$pipinstallgeo-espressoCheck Espresso documentation -installation pagefor details on dependencies and setting up with virtual environments.Basic usageOnce installed, each test problem can be imported using the following command:fromespressoimport<testproblem>Replace<testproblem>with an actual problem class in Espresso, such asSimpleRegressionandFmmTomography. Seeherefor a full list of problems Espresso currently includes.Once a problem is imported, its main functions can be called using the same
structure for each problem. For instance:fromespressoimportFmmTomographyproblem=FmmTomography(example_number=1)model=problem.good_modeldata=problem.datapred=problem.forward(model)fig_model=problem.plot_model(model)You can access related metadata programatically:print(FmmTomography.metadata["problem_title"])print(FmmTomography.metadata["problem_short_description"])print(FmmTomography.metadata["author_names"])Other problem-specific parameters can be accessed through the problem instance. For instance:print(problem.extent)print(problem.model_shape)Which additional values are set is highly problem-specific and we suggest to
consult theEspresso Documentation on the problems.ContributingInterested in contributing? Please check out ourcontributor's guide.LicenceEspresso is a community driven project to create a large suite of forward
simulations to enable researchers to get example data without the need to
understand each individual problem in detail.Licensing is done individually by each contributor. If a contributor wants to freely share their code example we recommend the MIT licence or a
2-clause BSD licence. To determine the licence of an existing Espresso
problem, please consult the documentation section of that problem.All the other core functions of Espresso written by InLab Espresso developer
team are distributed under a 2-clause BSD licence. A copy of this licence is
provided with distributions of the software.
Package: geoextent
geoextentPython library for extracting geospatial extent of files and directories with multiple data formatsThis project is developed as part of theDFG-fundedresearch project Opening Reproducible Research (o2r,https://o2r.info).InstallationSystem requirementsPython:3.xThe package relies on common system libraries for reading geospatial datasets, such as GDAL and NetCDF.
On Debian systems, theUbuntuGISproject offers easy installation of up to date versions of those libraries.See thepackageslist intravis.ymlfor a full list of dependencies on Linux.Install from PyPIYou must install a suitable version ofpygdalmanually first, seeinstructionsandthis related SO thread with different helpful answers.
We usepygdalfor better compatibility with virtual environments.# pip install pygdal=="`gdal-config --version`.*"pipinstallgeoextentSource installationgitclonehttps://github.com/o2r-project/geoextentcdgeoextent
pipinstall-rrequirements.txt

pipinstall-e.UseRungeoextent--helpto see usage instructions.Supported data formatsGeoJSON (.geojson)Tabular data (.csv)Shapefile (.shp)GeoTIFF (.geotiff, .tif)ContributeAll help is welcome: asking questions, providing documentation, testing, or even development.Please note that this project is released with aContributor Code of Conduct.
By participating in this project you agree to abide by its terms.SeeCONTRIBUTING.mdfor details.How to citeNüst, Daniel; Garzón, Sebastian and Qamaz, Yousef. (2021, May 11). o2r-project/geoextent (Version v0.7.1). Zenodo.https://doi.org/10.5281/zenodo.3925693See also theCITATION.cffandcodemeta.jsonfiles in this repository, which can possibly be imported in the reference manager of your choice.Licensegeoextentis licensed under MIT license, see fileLICENSE.Copyright (C) 2020 - o2r project.
Package: geofabrics
GeoFabricsThegeofabricspackage includes routines and classes for combining point (i.e. LiDAR), vector (i.e. catchment of interest, infrastructure), and raster (i.e. reference DEM) to generate a hydrologically conditioned raster.A peer-reviewed journal article of the package and methodogy can be found at:https://www.sciencedirect.com/science/article/pii/S1364815223002281. This is sadly not Open Access. Please emailrose.pearson@niwa.co.nzif you would like a copy of the article.Installationgeofabricsis avaliable onconda-forgeandPyPI. Conda is recommended due to difficulties installing geopandas (a dependency) with pip on Windows. See theWiki Install Instructionsfor more information.General workflowDocumentationWikiFor detailed instructions on installation, usage, and testing please check out the repowiki.API documentationFor auto-generated API documentation, checkout the repopages.ContributionsPlease see ourIssue Trackerfor details on coming features and additions to the software.There is no current expectations of contributions to this project. We accept input in code reviews now. If you would like to be involved in the project, please contact the maintainer.LicenseGNU GPLContactsMaintainer: Rose Pearson @rosepearsonrose.pearson@niwa.co.nzContributorsMaxime RioCyprien BosserelleRose PearsonLuke ParkinsonMartin NguyenAlice HarangPoojaKhoslaXander CaiBruno P. Kinoshita
Package: geofactory
GeoFactoryThis is a faker provider that generates fake geojson data. It simulates all the different
types of features and geometries in the geojson specification. There is also a test
suite for the class and it's methods run with pytest.SetupInstall from source:pip install git+https://github.com/scook12/geofactory.git@masterORpipenv install git+https://github.com/scook12/geofactory.git@master#egg=geofactoryInstall with pip:pip install geofactoryUseGeoFactory is just an additional community provider for geojson.An example might look like this:from faker import Fakerfrom geofactory import GeoFactoryfactory = Faker()factory.add_provider(GeoFactory)pt = factory.point()ptis now a valid geojson Point feature. All methods follow this pattern.MethodslnglatReturns a given number of random longitude/latitude pairs.pointReturns a single geojson point object with random coordinatesmultipointReturns a multipoint geojson object with a random number of random points
:param: count_limit - determines the maxiumum number of points generatedlinestringReturns a linestring geojson object with a random number of nodes
:param: node_limit - determines the maxiumum number of nodes generatedmultilinestringReturns a multilinestring with a random number of segments with a random number of nodes
:param: node_limit - determines maximum number of nodes for a given linestring
:param: count_limit - determines the maximum number of line segments generatedpolygonReturns a randomly arranged polygon with a specific number of nodes
:param: node_limit - determines how many nodes will be in the return polygonmultypolygonReturns a geojson multipolygon with a specific number of randomly arranged polygons
:param: count_limit - determines how many polygons will be createdgeometry_collectionReturns a specific number of random geojson geometries as a collection
:param: count_limit - determines how many geometries will be generatedfeatureReturns a random geojson feature objectfeature_collectionReturns a specific number of random features in a feature collection object
:param: count_limit - determines how many features will be generatedto_fileWrites geojson object to file
:param: path - full path and filename for data to be written to
:param: data - valid geojson object to dump in pathContributeOpen an issue, fork the repo, submit a PR. This is a tiny side project I built to be
used while developing an app that ingested geojson, so there aren't a ton of guidelines.If you make changes to existing methods, be sure to runpytestto make sure they all
are still passing. If you write a new method or class, please include some basic pytest
cases.
Package: geo-factory
geo-factory一个Python工具，可以操作GEO MINiML formatted family文件。功能：1. 合并每个样本的tbl文件
2. 根据平台文件把探针ID转换为gene symbol
3. 从family xml中提取样本表型信息安装使用pip安装：$ pip install geo-factory使用源码安装：$ git clone git@github.com:taishengxin/geo-factory.git
$ cd geo-factory
$ python setup.py install合并每个样本的tbl文件$ geo-factory merge-tbls --help
Usage: geo-factory merge-tbls [OPTIONS]

  合并tbl文件到探针表达矩阵

Options:
  -w, --wildcard TEXT  MINiML tbl文件通配符, 例如：'GSE124647/GSM*txt'，注意一定要加引号
                       [required]

  -o, --outfile PATH   输出探针表达谱文件  [required]
  --help               Show this message and exit.例如：$ geo-factory merge-tbls -w 'GSE124647/GSM*txt' -o probe_exp_GSE124647.tsv关于输出的探针表达谱文件：1. 一行代表一个探针、一列代表一个样本
2. 第一列是探针ID
3. 以tab键分割根据平台文件把探针ID转换为gene symbol$ geo-factory probe2gene --help
Usage: geo-factory probe2gene [OPTIONS]

  根据GEO平台文件把探针表达谱文件转换为基因表达谱文件

Options:
  -p, --probe-expression-matrix-file PATH
                                  探针表达谱文件  [required]
  -g, --geo-platform-file PATH    GEO平台文件  [required]
  -c, --col INTEGER               GEO平台文件哪一列是gene symbol  [required]
  -a, --aggregation-function [min|max|first|last|mean|median]
                                  当有多个探针对应同一个基因的时候使用什么方法合并，默认是median
  -o, --outfile PATH              输出基因表达谱文件  [required]
  --help                          Show this message and exit.例如：geo-factory probe2gene -p probe_exp_GSE124647.tsv -g GSE124647/GPL96-tbl-1.txt -c 11 -o gene_exp_GSE124647.tsv关于输出的基因表达谱文件：1. 一行代表一个基因、一列代表一个样本
2. 第一列是gene symbol
3. 以tab键分割从family xml中提取样本表型信息$ geo-factory parse-pheno --help
Usage: geo-factory parse-pheno [OPTIONS]

  从family XML中获取表型信息

Options:
  -f, --family-xml-file PATH  family XML文件  [required]
  -o, --outfile PATH          输出表型信息文件  [required]
  --help                      Show this message and exit.例如：$ geo-factory parse-pheno -f GSE124647/GSE124647_family.xml -o pheno_GSE124647.tsv关于输出表型文件：1. 行是样本、列是表型属性（例如，性别、年龄、生存时间）
2. 以tab键分割
Package: geofeather
geofeatherA faster file-based format for geometries withgeopandas.This project capitalizes on the very fastfeatherfile format to store geometry (points, lines, polygons) data for interoperability withgeopandas.Introductory post.Why does this exist?This project exists because reading and writing standard spatial formats (e.g., shapefile) ingeopandasis slow. I was working with millions of geometries in multiple processing steps, and needed a fast way to read and write intermediate files.In our benchmarks, we see about 5-6x faster file writes than writing from geopandas to shapefile via.to_file()on aGeoDataFrame.We see about 2x faster reads compared to geopandasread_file()function.How does it work?Thefeatherformat works brilliantly for standardpandasdata frames. In order to leverage thefeatherformat, we simply convert the geometry data fromshapelyobjects into Well Known Binary (WKB) format, and then store that column as raw bytes.We store the coordinate reference system using JSON format in a sidecar file.crs.InstallationAvailable on PyPi at:https://pypi.org/project/geofeather/pip install geofeatherUsageWriteGiven an existingGeoDataFramemy_gdf, pass this intoto_geofeather:to_geofeather(my_gdf, 'test.feather')Readmy_gdf = from_geofeather('test.feather')TEMPORARYpygeosprovides much faster operations of geospatial operations over arrays of geospatial data.geopandasis in the process of migrating to usingpygeosgeometries as its internal data storage instead ofshapelyobjects.Untilpygeosis fully integrated, there are shims ingeofeatherto support interoperability with pandas DataFrames containingpygeosgeometries. If you are already usingpygeosagainst data you read fromgeofeather, using the following shims will generate 3-7x speedups reading and writing data compared togeofeatherreading into GeoDataFrames.Internally, the feather file is identical to the one created above.pygeosis required in order to use this functionality.WARNING: this will be deprecated as soon aspygeosis integrated intogeopandas.from geofeather.pygeos import to_geofeather, from_geofeather

# given a DataFrame df containing pygeos geometries in 'geometry' column
# and a crs object

to_geofeather(df, 'test.feather', crs=crs)

df = from_geofeather('test.geofeather')Note: no CRS information is returned when reading from geofeather into a DataFrame, in order to keep the function signature the same as abovefrom_geofeatherIndexesRight now, indexes are not supported infeatherfiles. In order to get around this, simply reset your index before callingto_geofeather.Changes0.3.0allow serializing to / from pandas DataFrames containingpygeosgeometries (see notes above).use new CRS object in geopandas data frames (#4)droppedto_shp; use geopandasto_file()instead.0.2.0allow reading a subset of columns from a feather filestore geometry in 'geometry' column instead of 'wkb' column (simplification to avoid renaming columns)0.1.0Initial releaseCreditsEverything that makes this fast is due to the hard work of contributors topyarrow,geopandas, andshapely.
Package: geofem
GEOFEM:Modelagem Geofísica EletromagnéticaGEOFEM é um pacote python open-source de modelagem geofísica dos campos eletromagnéticos, via volumes finitos, dos métodos MT e MCSEM. Constitui a primeira parte do pacote MiGEO - em desenvolvimento -, que visa abranger modelagem e inversão eletromagnética.Geração de MalhaO gerador de malha em volumes finitos utilizado é do pacote open-sourcediscretizeque possui as seguintes malhas disponíveis:Malha TensorMalha OctreeModelagem direta: MTVolumes FinitosModelagem 1D, 2D e 3DDisponível na malha tipo Tensor e OctreeIsotrópica (anisotropia em andamento)Modelagem direta: MCSEMVolumes FinitosModelagem 1D, 2D e 3DDisponível na malha tipo TensorAnisotrópicaInstalaçãoRecomendada a criação de um ambiente virtual conda com python > v3.7 para fazer a instalação via pip.$pipinstallgeofemLicenseMIT
Package: geofetch
geofetchis a command-line tool that downloads sequencing data and metadata from GEO and SRA and createsstandard PEPs.geofetchis hosted atpypi. You can convert the result of geofetch into unmappedbamorfastqfiles with the includedsraconvertcommand.Key geofetch features:Works with GEO and SRA metadataCombines samples from different projectsStandardizes output metadataFilters type and size of processed files (from GEO) before downloading themEasy to useFast execution timeCan search GEO to find relevant dataCan be used either as a command-line tool or from within Python using an APIFor more information, seecomplete documentation at geofetch.databio.org(source in the/docsfolder).
Package: geoffrey-cheesecake
rank Python packages based on various empirical “kwalitee” factorsGeoffrey wrapper for python cheesecake utility:https://github.com/griggheo/cheesecake.gitFree software: GPLv3Documentation:http://geoffrey-cheesecake.readthedocs.org.ConfigurationTo correctly use this plugin you must define the variablecheesecake_pathin the plugin’s section of your projectFeaturesTODO
Package: geoffrey-clonedigger
tool for finding software clonesGeoffrey wrapper for python clonedigger utility:http://sourceforge.net/projects/clonedigger/Free software: GPLv3Documentation:http://geoffrey-clonedigger.readthedocs.org.ConfigurationTo correctly use this plugin you must define the variableclonedigger_pathin the plugin’s section of your projectFeaturesTODO
Package: geoffrey-csslint
CSSLint is a tool to help point out problems with your CSS code.Geoffrey wrapper for CSSLint utility:https://github.com/CSSLint/csslint.gitFree software: GPLv3Documentation:http://geoffrey-csslint.readthedocs.org.ConfigurationTo correctly use this plugin you must define the variablecsslint_pathin the plugin’s section of your projectFeaturesTODO
Package: geoffrey-jshint
JSHint is a community-driven tool to detect errors and potential problems in JavaScript codeGeoffrey wrapper for JSHint utility:http://www.jshint.com/Free software: GPLv3Documentation:http://geoffrey-jshint.readthedocs.org.ConfigurationTo correctly use this plugin you must define the variablejshint_pathin the plugin’s section of your projectFeaturesTODO
Package: geoffrey-pyflakes
pyflakes is a passive checker of Python programs.Geoffrey wrapper for pyflakes utility:https://github.com/pyflakes/pyflakes.gitFree software: GPLv3Documentation:http://geoffrey-pyflakes.readthedocs.org.ConfigurationTo correctly use this plugin you must define the variablepyflakes_pathin the plugin’s section of your projectFeaturesTODO
Package: geoffrey-pyreverse
Pyreverse : UML Diagrams for PythonGeoffrey wrapper for pyreverse utility:http://www.logilab.org/6883Free software: GPLv3Documentation:http://geoffrey-pyreverse.readthedocs.org.ConfigurationTo correctly use this plugin you must define the variablepyreverse_pathin the plugin’s section of your projectFeaturesTODO
Package: geofileops
geofileopsGeofileops is a python toolbox to process large vector files faster.Most typical GIS operations are available: e.g.buffer,dissolve,erase/difference,intersection,union,...The spatial operations are tested on geopackage and shapefile input files, but
geopackage is recommended as it will give better performance. Generallayerandfile operationscan be used on the file formats supported byGDAL.The full documentation is available onreadthedocs.Different techniques are used under the hood to be able to process large files as fast
as possible:process data in batchessubdivide/merge complex geometries on the flyprocess data in different passesuse all available CPUsThe following chart gives an impression of the speed improvement that can be expected
when processing larger files. Thebenchmarksran on a Windows PC with 12 cores and include I/O.
Package: geofinder
# GeoFinder
GeoFinder reads the place names in a GEDCOM or Gramps XML genealogy file, validates and corrects them and adds their latitude and longitude.  For unrecognized places, it supports wildcard searches and phonetic searches from the 20M entry geoname.org database.<a name=”features”></a>
## Features
* Rich place name database from geonames.org optimized for Genealogy including cemeteries, historic locations, and religious locations.
* Designed for correcting large GEDCOM files
* Automatic matching wherever possible adds missing information such as missing state/province or county.
* Cleans up and standardizes placenames
* Output is to a new GEDCOM file
* Adds latitude/longitude
* Wildcard search
* Phonetic search
* Highlights locations in the US and Canada where the event date is before European settlement[See User Guide Wiki for details](https://github.com/corb555/GeoFinder/wiki/User-Guide)