Saravanan666/pypi-org huggingface dataset:
Package: satsense
Satsense is an open source Python library for patch based land-use and
land-cover classification, initially developed for a project on deprived
neighborhood detection. However, many of the algorithms made available
through Satsense can be applied in other domains, such as ecology and
climate science.Satsense is based on readily available open source libraries, such as
opencv for machine learning and the rasterio/gdal and netcdf libraries
for data access. It has a modular design that makes it easy to add your
own hand-crafted feature or use deep learning instead.Detection of deprived neighborhoods is a land-use classification problem
that is traditionally solved using hand crafted features like HoG,
Lacunarity, NDXI, Pantex, Texton, and SIFT, computed from very high
resolution satellite images. One of the goals of Satsense is to
facilitate assessing the performance of these features on practical
applications. To achieve this Satsense provides an easy to use open
source reference implementation for these and other features, as well as
facilities to distribute feature computation over multiple cpu’s. In the
future the library will also provide easy access to metrics for
assessing algorithm performance.satsense - library for analysing satellite images, performance
evaluation, etc.notebooks - IPython notebooks for illustrating and testing the usage
of SatsenseWe are using python 3.6/3.7 and jupyter notebook for our code.DocumentationCan be found onreadthedocs.InstallationPlease see theinstallation guide on readthedocs.ContributingContributions are very welcome! Please seeCONTRIBUTING.mdfor our contribution guidelines.Citing SatsenseIf you use Satsense for scientific research, please cite it. You can
download citation files fromresearch-software.nl.ReferencesThe collection of algorithms made available trough this package is
inspired byJ. Graesser, A. Cheriyadat, R. R. Vatsavai, V. Chandola,
J. Long and E. Bright, “Image Based Characterization of Formal and
Informal Neighborhoods in an Urban Landscape”, in IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensing,
vol. 5, no. 4, pp. 1164-1176, Aug. 2012. doi:
10.1109/JSTARS.2012.2190383Jordan Graesser himself also maintainsa
librarywith many of these
algorithms.Test DataThe test data has been extracted from the Copernicus Sentinel data 2018.
Package: satsure-cloud-utils
Thesatsure_cloud_utilsis a python package that contains all the functionality to browse and navigate AWS infrastrucure used in SatSure.Installation$ pip install satsure_cloud_utilsUsage>> from satsure_cloud_utils import GetS3Handler
>> s3_handler = GetS3Handler(config_file_path="<config-file-path>")
>> output = s3_handler.list_buckets()
>> print(output)
Package: satsure-ds-utils
Satsure DS Common UtilsRepository containing common utilities used across the data science team of SatSure.Folders & Files:./satsure_ds_utils: Core components of DS Utils../examples: Folder containing examplesmain.py: main file for testing.requirements.txt: Python requirements file.setup.py: Setup file for pip wheel.List of Algorithms/Operations:Split RasterJoin RasterArray to RasterFastTileFastTile NDVILULC AreaVillage Geo AreaClip to Boundary
Package: sat-toolkit
SAT-toolkitA tool for efficiently handling CNF and DNF formulas in PythonInstallationTo install from pypi use:pipinstallsat-toolkitTo simplify CNFs and convert DNFs to CNFs, you need to compileespressoand make it
available on inside your $PATH.Usage ExampleThe following example shows how to create a minified CNF for a boolean function that is true at values 0, 6, 9, and 15.fromsat_toolkit.formulaimportCNF,Truthtableimportnumpyasnptable=np.zeros(16,int)table[[0,6,9,15]]=1tt=Truthtable.from_lut(table)cnf=tt.to_cnf()print(cnf)
Package: satumut
About[Satumutsatumutis a python package, wrappped around thepmx packagethat performs saturated mutations on proteins to study their effects on protein-ligand interactions viaPELE simulations.Given a position of a residue within a protein system:it mutates to all the other 19 aminoacids by creating 19 PDbsThen, it will create the files necessary for the PELE simulations the .yaml and the .sh files for each of the protein systems.Software requirementsnumpyscipymatplotlibpmxseabornfpdfbiopython
Package: saturatedplaces
saturated-placesA python package for effeciently downloading Point of Interest (POI) data from Google Places API for large geographies. Currently, this is done by creating a fishnet grid within a custom polygon and
running Delauny Triangulation for each grid cell till the API result is saturated i.e the API returns < 60 results.We are open to contributors who wish to further optimize this logic.InstallationYou can either go the latest release, download the tarball to your local directory and run:pip install saturated-places-X.Y.Z.tar.gzOr you can go simply download it fromPyPiby running:pip install saturatedplacesUsageimport saturatedplaces as sp
from shapely.geometry import Polygon
import json

# Put Google API key here with Places API service enabled.
api_key = "#######"


# Custom GeoJson created using geojson.io
with open('lahore_sample.geojson','r') as file:
    poly = json.load(file)


# Convert GeoJson coordinates feature into shapely.geometry.Polygon object.
# This is important since later functions only accepts this data type.
poly = poly['features'][0]['geometry']['coordinates'][0]
poly = Polygon(poly)


# resolution determines the space between each Point of the mesh. The same variable 
# is also used in the 'radius' parameter for the Places API call, as used the saturate
# function. You could set this variable independently for both get_grid_from_poly and
# and saturate function.
resolution = 500

# Creates a mesh of equally spaced points within a polygon 'poly', resolution controls
# distance between each point of the mesh.
mesh = sp.get_grid_from_poly(poly=poly,resolution=resolution)

# Generates a fishnet grid using the mesh of point generated using mesh get_grid_from_poly function
vor_gpd = sp.generate_fishnet(mesh,poly)


# Runs saturation algorithm to return a dataframe with places data for area within a polygon.
places_df = sp.saturate(grid_df=vor_gpd,boundary_poly=poly,
                        fp_working_file='working.shp',
                        fp_outpt='final.pkl',
                        api_request_limit=2000,
                        api_key=api_key,
                        resolution = resolution)

# Saves places data as flat csv file.
places_df.to_csv('places_data.csv',index=False)ContributionPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
I will also be creating issues to chalk out what features, or improvements need to be worked on next. Feel free to pick
up any one of these issues after aligning.For any queries feel free to reach out tohishamsajid113@gmail.comor drop a DM@hishamsajid
Package: saturnin-core
saturnin-coreSaturnin core servicesThe core set of services for Saturnin currently contains next services:Text file reader microservice -saturnin.text.readerText file writer microservice -saturnin.text.writerText line filter microservice -saturnin.text.linefilterBinary data file reader microservice -saturnin.binary.readerBinary data file writer microservice -saturnin.binary.writerProtobuf printer microservice -saturnin.proto.printerProtobuf filter microservice -saturnin.proto.filterProtobuf aggregator microservice -saturnin.proto.aggregatorFirebird log from server provider microservice -saturnin.firebird.log.fromsrvFirebird log parser microservice -saturnin.firebird.log.parserFirebird trace parser microservice -saturnin.firebird.trace.parserFirebird trace session provider microservice -saturnin.firebird.trace.sessionFor information how to use these services, seeSaturnin CORE documentation.Table of ContentsLicenseInstallationDocumentationLicensesaturninandsaturnin-coreare distributed under the terms of theMITlicense.InstallationFor installation instructions, see theSaturnin documentation.DocumentationDocumentation related to Saturnin:Firebird ButlerSaturninSaturnin COREservicesSaturnin SDK
Package: saturnin-sdk
saturnin-sdkThis repository contains SDK for Saturnin, and example services and applications.Table of ContentsLicenseInstallationDocumentationLicensesaturninandsaturnin-sdkare distributed under the terms of theMITlicense.InstallationThesaturnin-sdkpackage (released on PyPI) contains the SDK itself, without examples.To work with the SDK, it's necessary to install and properly initialize thesaturnin(seeSaturnindocumentation for details).Examples are not distributed via PyPI. You can either download the ZIP package fromgihub releasesand unpack it into
directory of your choice, or checkout the "examples" directory directly.You may also checkout the wholesaturnin-sdkrepository, and install the SDK into your
Saturnin site directly using:saturnin install package -e .To register (example and your own) services and application for use with Saturnin in
"development" mode, usesaturnin install package -e .from root directory of service
package. For example to registerTextIOsample service:CD toexamples/textioRunsaturnin install package -e .DocumentationDocumentation related to Saturnin:Firebird ButlerSaturninSaturnin COREservicesSaturnin SDK
Package: saturn-proxy-server
Socks5 async proxy serverSaturn is a SOCKS5 server based on asyncio protocolsInstallationFrompypi.orgpipinstallsaturn-proxy-serverFrom this repogitclonehttps://git.best-service.online/yurzs/saturn.gitcdsaturn
python3./setup.pyinstallUsagePlease editconfig.pyfile before starting your server.By default proxying allowed to all hosts (ALLOWED_DESTINATIONS =  ["0.0.0.0/0"]).  You can specify single IP addresses
(both IPv4 and IPv6) and subnets.importsaturnsaturn.config.ALLOWED_DESTINATIONS=["192.168.1.0/24"]saturn.config.AUTHENTICATION_METHODS=["saturn.auth.none"]saturn.start_server("127.0.0.1",8080)This config will allow passwordless connections with allowed proxying for192.168.1.0-192.169.1.255IP range.
You can use multiple auth methods at once like["saturn.auth.none", saturn.auth.dict"]Authentication methodsCurrent SOCKS5 standart supportsNone ["saturn.auth.none"]GSSAPI ["saturn.auth.gssapi"]Login/Password (dict format) ["saturn.auth.dict"]Custom authentication methodsYou can implement your own authentication method (Login/Password)All you need to do is to implementAuthenticatorclass withasync def authenticate(self, data)method which will returnboolauthentication result. Then just import your module and use it in configsaturn.config.AUTHENTICATION_METHODS = ["your_auth_method"]You can see examples insaturn.authTODO listLoggingMax socket limitMore tests
Package: saturn-python
saturnA Python library that allows to rerun a script multiple times without stopping
the process and keeping the scope (like it is done inJupyter Notebookin code sections).
The goal is to keep in RAM the datasets that are expensive to load in each run.Installationpip install saturn-pythonBasic example# example.pyimportsaturn@saturn.maindefmain(scope):ifnotsaturn.defined('scope.a',scope):# OR if 'a' not in scope:scope.a=42scope.a+=1print(scope.a)Run:$ saturn
Welcome to Saturn terminal where you can reruns your Python module keeping its scope in RAM. Version 1.0.
S>>> run example
43
S>>> run example
44
S>>> print(scope)
{'a': 44}
S>>> exit
$Work with sections# example.pyimportsaturn@saturn.maindefmain(scope):print('main')ifsaturn.section('a'):print('section a')ifsaturn.section('b'):print('section b')Run:$ saturn
Welcome to Saturn terminal where you can reruns your Python module keeping its scope in RAM. Version 1.0.
S>>> run example
main
section a
section b
S>>> run example a
main
section a
S>>> run example c
main
S>>> exit
$
Package: saturnscrape
saturnscrapeScrapes information fromhttps://saturn.live, a collaborate school scheduling system.Currently, this Python package only aims to support the scheduling and members of a school.Examplefromasyncioimportrunfromtypingimportcastfromsaturnscrapeimport*asyncdefrunner():client=SaturnLiveClient("jwt","refresh token")print(cast(FullStudent,awaitclient.get_student("me")).phone_number)awaitclient.close()run(runner())Also provides a simple command line tool for downloading the schedule and members of a school to vCard and vCalendar/iCalendar files.
Package: saturn-toolbox
SATURN TOOLBOXsaturn-toolboxis a free open source toolbox, that has many tools for resllers!InstallationRunpip install saturn-toolbox, the commandsaturn-toolboxshould now be available.ContactJoin us ondiscord!SupportIf you encounter a problem, please feel free to open an issue here on github.TODOExport usable APIProper CLI command
Package: sat-utils
SAT UtilitiesThis repository contains a collection of shared utility functions.Slack: A class to upload files to our Slack workspaceSATLogger: A standard logger for SAT projectsInstallation# Install the package from private PyPI (CLI)$pipinstall-ihttps://pypi.ehps.ncsu.edusat-utils# Install the package from private PyPI (requirements.txt)$pipinstall-rrequirements.txt--extra-index-urlhttps://pypi.ehps.ncsu.eduUsageSlackfromsat.slackimportSlackmessage=Slack(token="abc123-8dkhnna-97hasdj-xyz")message.upload_file(channel="support",file_path="C:/files",file_name="support.pdf",file_type="",title="Support manual v3.2",initial_comment="Woot!")SATLoggerfromsat.logsimportSATLoggerlogger=SATLogger(__name__)...logger.info("Hello, world!")Gravity FormsThree environment variables are required to authenticate with the Gravity Forms API.GRAVITY_FORMS_CONSUMER_KEYGRAVITY_FORMS_CONSUMER_SECRETGRAVITY_FORMS_BASE_URLAlternatively, these values can be passed into the GravityForms initialization as parameters.fromsat.gravity_formsimportGravityFormsgravity=GravityForms()cards_requested=gravity.get("/forms/3/entries")DevelopmentSetupEnsure you are in a virtual environment with Python 3.9.6 or higher.>makesetupAdd dependenciesUpdating RequirementsThis project usespip-toolsto manage requirements. To update the requirements add your requirement
to thepyproject.tomlfile.For dependencies required to run the app in production, add them to thepyproject.tomlfile under the[project]section.[project]...dependencies=["fastapi>=0.95.1, <1.0.0","pyjwt>=2.6.0, <3.0.0","...","<YOUR NEW REQUIREMENT HERE>","...",]For developer dependencies required or nice to have for development, add them to thepyproject.tomlfile under the[project.optional-dependencies]section.[project.optional-dependencies]dev=["pytest>=6.2.5, <7.0.0","...","<YOUR NEW DEV REQUIREMENT HERE>","...",]When you have add the dependency run:>makeupdate-requirementsBuild and PublishUpdate the version inpyproject.tomlbefore building.Build>flitbuildPublishAs long as your PyPI credentials are set up correctly, you can publish to PyPI with the following command:>flitpublish
Package: satveg-api
satveg-apisatveg-apié uma biblioteca Python que requisita aos servidores da Embrapa a série NDVI ou EVI de uma localidade (latitude, longitude). Tem capacidade de fazer múltiplas requisições dado um arquivo.csv, retornando um Pandas.DataFrame.Utilização>>>importsatveg_api>>>serie=satveg_api.Series('SEU_TOKEN_DA_API_SATVEG')>>>serie.get_json(-18.92803,-40.09281){'success':True,'status_code':200,'message':'Sucesso.','data':{'listaSerie':[0.6923,...,0.6638],'listaDatas':['2000-02-18',...,'2022-06-26']}}>>>serie.get(-18.92803,-40.09281,label='Café')'pandas.core.frame.DataFrame'>>>df=serie.from_csv('input.csv')'pandas.core.frame.DataFrame'>>>satveg_api.to_learn(df)'pandas.core.frame.DataFrame'Exemplo deinput.csv:labellatitudelongitudeCafé-18.92803-40.09281Instalando a biblioteca satveg-apisatveg-api está disponível no PyPI:$python-mpipinstallsatveg-api
Package: sat-ws
SAT WSLibrería para usar el servicio web del SAT de Descarga Masiva:us: The documentation of this project is in spanish as this is the natural language for intented audience.:mexico: La documentación del proyecto está en español porque ese es el lenguaje principal de los usuarios.Basada en gran parte del proyectohttps://github.com/phpcfdi/sat-ws-descarga-masivaCódigo fuentehttps://gitlab.com/HomebrewSoft/sat_ws_apiEsta librería contiene un cliente (consumidor) del servicio del SAT deServicio Web de Descarga Masiva de CFDI y Retenciones.InstalaciónEjemplo básico de usoAcerca del Servicio Web de Descarga Masiva de CFDI y RetencionesEl servicio se compone de 4 partes:Autenticación: Esto se hace con tu fiel y la libería oculta la lógica de obtener y usar el Token.Solicitud: Presentar una solicitud incluyendo la fecha de inicio, fecha de fin, tipo de solicitud
emitidas/recibidas y tipo de información solicitada (cfdi o metadata).Verificación: pregunta al SAT si ya tiene disponible la solicitud.Descargar los paquetes emitidos por la solicitud.CompatilibilidadContribucionesLas contribuciones con bienvenidas. Por favor lee [CONTRIBUTING][] para más detalles
y recuerda revisar el archivo de tareas pendientes [TODO][] y el [CHANGELOG][].Copyright and LicenseThesat-wslibrary is copyright ©HomebrewSoftand licensed for use under the MIT License (MIT). Please see [LICENSE][] for more information.
Package: satysfi-notebook-tweaks
satysfi_notebook_tweaksA small tweaks for SATySFi Notebook environment.RequirementsJupyterLab >= 4.0.0InstallTo install the extension, execute:pipinstallsatysfi_notebook_tweaksUninstallTo remove the extension, execute:pipuninstallsatysfi_notebook_tweaksContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the satysfi_notebook_tweaks directory# Install package in development modepipinstall-e"."# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstallpipuninstallsatysfi_notebook_tweaksIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedsatysfi-notebook-tweakswithin that folder.Testing the extensionFrontend testsThis extension is usingJestfor JavaScript code testing.To execute them, execute:jlpm
jlpmtestIntegration testsThis extension usesPlaywrightfor the integration tests (aka user level tests).
More precisely, the JupyterLab helperGalatais used to handle testing the extension in JupyterLab.More information are provided within theui-testsREADME.Packaging the extensionSeeRELEASE
Package: satzmetzger
SatzmetzerSplit German text to sentence! Uses TRIE-Regex to filter ordinal numbers (23.04., 2.5. ...), roman numbers, (II., XI. ...) 15,000 abbreviations (z.B., Abk. ...) , about 7,500 second-level domains (.com.br, ac.at ...), 1500 file name extensions (hallo.docx, tabelle.xlsx ...)Before writing this class, I had tested about 20 classes, modules, functions, and methods to split German text to sentences. I wasn't happy with any of the results, so I wrote this class here! It doesn't use any AI, just old school Regex!It is very simple to use! Here is everything you need to know:textzumsplitten ='''Hallo, ich bin ein Text. Zerhack mich bitte! Ich halte es nicht mehr aus. Wenn du mich bis zum 23.04. nicht zerhackst, rufe ich Papst Hackerpeter X. an und schicke ihm das Dokument erhatmichnichtzerhackt.docx, er wird z. B. sehr bÃ¶se auf dich sein! Darauf kannst du einen lassen!'''

from satzmetzger import Satzmetzger
losgehts = Satzmetzger()
textfertig = losgehts.zerhack_den_text(textzumsplitten, debug=False)
for indi, zerhacktersatz in enumerate(textfertig):
    print(indi, end='\t\t')
    print(zerhacktersatz)#Output:
#0		Hallo, ich bin ein Text.
#1		Zerhack mich bitte!
#2		Ich halte es nicht mehr aus.
#3		Wenn du mich bis zum 23.04. nicht zerhackst, rufe ich Papst Hackerpeter X. an und schicke ihm das Dokument erhatmichnichtzerhackt.docx, er wird z. B. sehr bÃ¶se auf dich sein!
#4		Darauf kannst du einen lassen!
Package: sauce
Parser for SAUCE or Standard Architecture for Universal Comment Extensions,
invented by Tasmaniac and Rad Man from ACid:ANSi’s used to be just ANSi’s, pictures were just pictures, loaders were just
loaders and quite frankly, every file was just as plain tasting as every
other. This is about to change, how- ever, because ACiD has decided to give
their files an extra “je-ne-sais-quoi.” In reality, we’ll be adding SAUCE to
every file you can imagine.Now, before we thoroughly confuse you, let us explain what we are doing here.
SAUCE stands for “Standard Architecture for Universal Comment Extensions.”
Although originally intended for personal use in ANSi’s and RIP screens,
early in the developement of EFI (Extended File Information) it was decided
that EFI should be extended to have support for more than just ANSi and RIP
screens. Our brainchild was born and the specs were designed. The only aspect
left undecided was the name, and after rejecting some very funny candidates,
SAUCE was unanimously chosen. This leads us to the big question in the sky,
“What is sauce?” SAUCE is a universal process to incorporate a full
description for any type of file. The most outstanding aspect of this concept
is that you have access to the complete file name, the file’s title, the
creation date, the creator of the file, the group that the creator is
employed by, and much, much, more.To put it short, SAUCE is a block of meta data that can be appended to various
art works made by the scene.UsageLoad the interface:>>> from sauce import SAUCEExtract metadata of an ANSI file:>>> piece = SAUCE('artwork.ans')
>>> print piece.group
'ACiD'Modify metadata of an ANSI file:>>> import datetime
>>> piece = SAUCE('artwork.ans')
>>> piece.date = datetime.datetime.now()
>>> piece.write('artwork.new')
Package: saucebindings
Hello, welcome to the Sauce Python bindings! Let’s make using Sauce Labs Simple!Example UsageThe goal of the Sauce bindings is to provide a straightforward approach to connecting your automated scripts to Sauce. Here’s an example. The Sauce Python bindings can be used like this:from saucebindings.session import SauceSessionsession = SauceSession()driver = session.driver
driver.get(“www.saucedemo.com”)session.stop(True)RequirementsSet your Sauce username and Sauce access key asenvironment variables.InstallationClone this project to build from sourcegit clonehttps://github.com/saucelabs/sauce_bindingspython setup.py installor install frompippip install saucebindingsDevelopmentThis project will be developed initially in Python 3.x so please create avirtual environment:python3 -m venv venvsource venv/bin/activateTo install dependencies, do the following:pip install -r requirements.txtTestingTo run all tests, run the following:pytestPackage and Releasepython setup.py sdist
twine check dist/*
twine upload dist/*