Saravanan666/pypi-org huggingface dataset:
Package: sinlingua
SinLingua: Sinhala Language Data Processing LibrarySinLinguais a Python library designed for Sinhala language data processing tasks. This library provides various components to assist in Sinhala language processing, including audio to text conversion, grammar conversion, Singlish to Sinhala text conversion, data cleaning and preprocessing, and Sinhala summarization.Table of ContentsContributingInstallationUsageLibrary FunctionsLicenseAuthorsVisit the GitHub Pages WebsiteDownload SinLingua ToolkitContributingContributions to improve and expand SinLingua are welcome! Feel free to submit issues or pull requests on theGitHub Bug Tracker.InstallationTo install SinLingua, use the following command:pipinstallsinlinguaUsageimportsinlinguaLibrary FunctionsThis library has 5 functions:Singlish to Sinhala Text ConversionSinhala Audio to Text ConversionSinhala Grammar ConversionSinhala Text PreprocessingSinhala Text SummarizationFor detailed documentation on each function, visit theSinLingua Documentation.LicenseThis library is licensed under the MIT License. See theLICENSEfile for details.You can take this Markdown code and paste it into your README file on GitHub or any other platform that supports Markdown rendering. Make sure to replace the placeholder content with your actual library information and links.AuthorsSupun GurusingheSandaruwini GalappaththiSupun WijesingheBinura YasodyaFor any inquiries or feedback, you can contact us atsssbprojects@gmail.com.
Package: sinner
SINNER - Simplest Implementation of Neural Networks for Effortless RunsI worked with Neural Networks more years ago than I'd like to remember. I'm returning to study "black box models", and I felt that there is no simple way of creating a neural network, and that was a mistake. That's why I've created this Python library.Creating a new neural network is (and always will be) as simple asNeuralNetwork(list), wherelistis a list of integers with the number of neurons on every layers (the first one being the input, the last one the output, and the rest the hidden ones). Of course there are and will be optional parameters, but it will always work with a standard view for starters.Of course "simplest" is not the same as "simplistic", and every aspect of a neural network that makes this implementation more robust is welcome.Public methodsThis list needs to be as short as possible, always. Nowadays we have three methods only:eval(inputs): eval an array of inputs with current configuration of the neural network.train(trainingInputs, trainingOutputs): train the network from a set of inputs and outputsexport(filePath): export a JSON representation of the neural network to an external fileYou can also create a neural network from a JSON file by callingNeuralNetwork.fromFile(filePath).UsageThis project is available as a package onPyPI.orgTo Do Listadd a log system for training outputsadd usage examples on Gitmake transfer functions selectable on creatingimprove public methods documentation (swagger?)add automated testsThe original version of this implementation was loosely based onJason Brownlee's "How to Code a Neural Network with Backpropagation In Python (from scratch)"Comments and suggestions, feel free to contact me!--Friar Hob
Package: sinner-friarhob
SINNER - Simplest Implementation of Neural Networks for Effortless RunsI worked with Neural Networks more years ago than I'd like to remember. I'm returning to study "black box models", and I felt that there is no simple way of creating a neural network, and that was a mistake. That's why I've created this Python library.Creating a new neural network is (and always will be) as simple asNeuralNetwork(list), wherelistis a list of integers with the number of neurons on every layers (the first one being the input, the last one the output, and the rest the hidden ones). Of course there are and will be optional parameters, but it will always work with a standard view for starters.Of course "simplest" is not the same as "simplistic", and every aspect of a neural network that makes this implementation more robust is welcome.Public methodsThis list needs to be as short as possible, always. Nowadays we have two methods only:eval(inputs): eval an array of inputs with current configuration of the neural network.train(trainingInputs, trainingOutputs): train the network from a set of inputs and outputsTo Do Listimport/export the neural networkadd a log system for training outputscreate a packaging for PIPadd usage examples on Gitmake transfer functions selectable on creatingThe original version of this implementation was loosely based onJason Brownlee's "How to Code a Neural Network with Backpropagation In Python (from scratch)"Comments and suggestions, feel free to contact me!--Friar Hob
Package: sinnia-ig-fetcher
Sinnia IG FetcherTo publish changes to this library in PyPi:https://packaging.python.org/tutorials/packaging-projects/#generating-distribution-archivesEdit the version in setup.py.first time?'python3 -m pip install --upgrade build'
'python3 -m pip install --user --upgrade twine''python3 -m build'
'python3 -m twine upload dist/sinnia_*'
Auth must have been configured in $HOME/.pypircDepend on this package: instagram-login-appUsing Instagram Graph API and Instagram Basic Display APIIncludes:For Businesses (Instagram Graph API):Get MediaGet Competitors MediaGet Top Media in HashtagFor Basic Users (Instagram Basic Display API):Get MediaUsage:from ig.get_business_media import BusinessMediaFetcher
from ig.get_business_competitors_media import CompetitorsMediaFetcher
from ig.get_hashtag_media import HashtagsMediaFetcher
from ig.get_user_media import UserMediaFetcherfetcher = BusinessMediaFetcher()
data_found = fetcher.process_business_media(db_connection, business_ig_id, access_token)fetcher = CompetitorsMediaFetcher()
data_found = fetcher.process_competitors_media(db_connection, business_ig_id, competitor_name, access_token)fetcher = HashtagsMediaFetcher()
data_found = fetcher.process_hashtags_media(db_connection, business_ig_id, hashtag, access_token)fetcher = UserMediaFetcher()
data_found = fetcher.process_user_media(db_connection, user_ig_id, access_token)
Package: sinnia-shared
Sinnia UtilsPython utilities for SinniaTo publish changes to this library in PyPi:https://packaging.python.org/tutorials/packaging-projects/#generating-distribution-archivesEdit the version in setup.py.first time?'python3 -m pip install --upgrade build'
'python3 -m pip install --user --upgrade twine'subsequent'python3 -m build'
'python3 -m twine upload dist/sinnia_*'Auth must have been configured in $HOME/.pypircDepend on this package:Includes:AMQP UtilsTimezone UtilsMiscellaneous Utils
Package: sinnn
sinnnSohiab Imran's Numpy Neural Network.sinnn is a numpy powered low code neural network library.Work in progress 🔨 .
To do:Add RegularisationAdd more InitialisersAdd more OptimisersInstallationpip install sinnnRequirements:
NumpyFeaturesSupport for a limited number of features is available as the library is still new.
Supported features are:Layers: Dense, ReLUInitialisers: Zeros, SmallRandomLosses: MSE, CrossEntropyMetrics: loss, accuracyOptimisers: SGD, Momentum, AdaGradutils: sigmoid, softmax, one_hot, batches, save_model, load_modelExamplesPlease refer to the examples directory for example deep learning problems solved using sinnn.
Package: sinomaps
This project provides a package resource for “High School Information Technology Textbook (2018)”, published by Sinomaps Press.
本项目提供了中国地图出版社《高中信息技术》2018版教材所需要的项目依赖库及代码。Please typepip install sinomapsin terminal/console to install the package.
请在终端或控制台输入pip install sinomaps安装此包。
Package: sinope
pronounced (sin-Oh-pee)A package for solving parabolic differential equations, in particular the
Schrodinger equation and the Gross-Pitaevskii equation using the Crank Nicolson
method.To install the package from PyPI usepip install sinope, or download the
source distribution sinope-0.1.tar.gz and runpip install sinope-0.1.tar.gz.Alternatively if you prefer to keep the code in its own directory, download the
github repository and compile the extension module withmake.To run tests usesinope_run_tests.py. This requires the packagehypothesis.
which can be installed withpip install hypothesis.Examples for how to use the code are available in scripts. The user code
should define a grid class which inherits from one of Grid1D or Grid2D. The
user then needs to define four methods:initialise_solvers(self, **kwargs):This is called when the grid is initialised using with the keyword
arguments that are passed to the grid constructor. In this method the
chosen solvers (to be found in the solvers module) should be constructed.set_time_step(self, dt):This method needs to initialise all of the needed solvers with a common
step size, which is dt. This method should have some mechanism to choose
the step size if it is called with dt as None. In which case the
longest_time_step methods of the solver classes should be used.advance_single(self):This advances the solution by a single time step using the previously
defined solvers.n_components(self):Additionally the methodn_components(self)can be overridden (default
function returns 1) if the solution u has more than one component. This
allows for multiple (possibly dependent) variables to be evolved together.
Note that each component is necessarily real, so if one of the variables is
complex then it requires two components (one for each of the real and
imaginary parts).The solvers are defined in the solver module. The available solvers are
Explicit (for a real parabolic PDE) using a FTCS explicit scheme. A Crank
Nicolson implicit solver also for real PDEs is also available using Cholesky
factorisation (cholesky module). This is implicit and is unconditionally stable
while the explicit one has a strict stability condition.  Crank Nicolson is
therefore faster overall, especially on large grids. On the other hand it is
less easily parallelised than an explicit scheme.The solvers are entirely one dimensional and are intended to be used on the 2D
grid with dimensional splitting. See the scripts folder for examples.The tst package implements the multiplying of TST (tridiagonal symmetric
Toeplitz) matrices by vectors using c++ and a swig interface. This is
significantly faster than the equivalent numpy code since it cannot be
implemented using elementwise operations, but requires slicing.
Package: sinsy-cli
sinsy-cliis a command line interface to the Synsi web service (http://sinsy.jp).Sinsyis an open source vocal synthetizer with a similar goal to vocaloid, cevio, alter/ego, acantorix, …sinsy-cliwill send a lyric annotated score inMusicXMLformat and retrieve the vocal synthesis result in wav format
(and accessorily other synthesis log assets).All online parameters found on the web service can be accessed via command line argumentsusage: sinsy-cli [-h] [-d] [-V] [-a] [-l {japanese,english,mandarin}]
                 [-b SPKR] [-g SYNALPHA] [-v VIBPOWER] [-p F0SHIFT]
                 score_filename [score_filename ...]

GPL v3+ 2016 Olivier Jolly

positional arguments:
  score_filename        input file in MusicXML format

optional arguments:
  -h, --help            show this help message and exit
  -d, --debug           debug parsing [default: False]
  -V, --version         show program's version number and exit
  -a, --all             Download all assets [wav + logs, defaults: download
                        only wav]
  -l {japanese,english,mandarin}, --language {japanese,english,mandarin}
                        Vocal bank language [defaults: japanese]
  -b SPKR, --bank SPKR  Vocal bank index for the given spoken language [from
                        0, max depends on language, default: 1]
  -g SYNALPHA, --gender SYNALPHA
                        Gender parameter [between -0.8 and 0.8, default: 0.55]
  -v VIBPOWER, --vibrato VIBPOWER
                        Vibrato intensity [between 0 and 2, default: 1]
  -p F0SHIFT, --pitch F0SHIFT
                        Pitch shift [in half tones, between -24 and 24,
                        default: 0]

Interact with SinsyInstructionsPreparation of lyrics annotated score suitable to be processed bySinsyis described in a blog post:http://blog.pcedev.com/2016/02/18/hands-sinsy-solution-song-vocal-synthesis-using-open-source-software/Installationsinsy-cliis installable from PyPI with a single pip command:pip install sinsy-cliAlternatively,sinsy-clican be run directly from sources after a git pull (recommended if you want to tweak
or read the source):git clone https://gitlab.com/zeograd/sinsy-cli.git
cd sinsy-cli && python setup.py installor directly from its git repository:pip install git+https://gitlab.com/zeograd/sinsy-cli.git
Package: sint
sintDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-nsint--fileenv.yml# $ mamba env update -n sint --file env.mac.ymlInstallpipinstall-e.# install from pypipipinstallsintnbdev# activate conda environment$condaactivatesint# make sure the sint package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the sint package$nbdev_prepareNote: it might be useful to use the following snippet to enable hot
reloading:%load_extautoreload%autoreload2Publishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/sint.gitor fromconda$condainstall-cdsm-72sintor frompypi$pipinstallsintExamplessign.P,sign.Z,sign.N(sign(int=1, chr='P', sym='+', syn={'positive', '+', 'pos'}),
 sign(int=0, chr='', sym='', syn=set()),
 sign(int=-1, chr='N', sym='-', syn={'negative', 'neg', '-'}))sint(2),sint(0),sint(-2),sint('2'),sint('0'),sint('-2'),sint('+'),sint('0'),sint('-')(1, 0, -1, 1, 0, -1, 1, 0, -1)sint(2).chr,sint(2).sym,sint(0).chr,sint(0).sym,sint.tochr(2)('P', '+', '', '', 'P')
Package: sintak-unika-parser
SINTAK PARSERHOST: sintak.unika.ac.id/id/USAGEfromlib.FetchimportFetchusername='15.N1.0020'password='tanggal lahir'sintak=Fetch(username,password)MethodgetDataBarChart(self)Provide all IPS until now, data return in JSONgetDataPieChart(self)Provide all academic value A/AB/etc and count it, data return in JSONgetKtm(self, saveTo)Get KTM Sementara and save to directoryUPDATEsoonContributingPull request is welcome and i so happy if you help me :)Error/IssuePlease create new issue if you encounter some problemLICENSE
Package: sinta-scraper
Sinta ScraperRetrieves information fromSinta (Science and Technology Index)via scraping.Installationpip install sinta-scraperDependencies (installed automatically using the above command):beautifulsoup4,requests,dicttoxml,dict2xml,python-string-utils, andflatdict.Importingimport sintaAvailable Functionsauthor()affiliation()Function Detailsauthor()Retrieves an author's information by Sinta ID. For example:author_id=6082456author=sinta.author(author_id)print(author)The output format is the Python dictionary. The structure is given in the following sample output.{'affiliation':{'id':404,'name':'Universitas Brawijaya','url':'https://sinta.kemdikbud.go.id/affiliations/profile/404'},'articles':{'scholar':103,'scopus':14,'wos':0},'citations':{'scholar':266,'scopus':52,'wos':0},'cited_docs':{'scholar':0,'scopus':9,'wos':0},'department':'S1 - Teknik Informatika','g-index':{'scholar':1,'scopus':1,'wos':''},'h-index':{'scholar':9,'scopus':4,'wos':''},'i10-index':{'scholar':9,'scopus':1,'wos':''},'id':6082456,'name':'RANDY CAHYA WIHANDIKA','score':{'3_years':122,'affiliation':0,'affiliation_3_years':0,'overall':451},'subjects':['Image Processing','Computer Vision'],'url':'https://sinta.kemdikbud.go.id/authors/profile/6082456'}Multiple authors can also be retrieved at once:author_ids=5975467,6019743authors=sinta.author(author_ids)affiliation()Retrieves information about an affiliation. For example:affiliation_id=404affiliation_info=sinta.affiliation(affiliation_id)Output:{'abbreviation':'UB','articles':{'garuda':8783,'scholar':100142,'scopus':9181,'wos':1260},'authors':2330,'citation_per_researcher':{'garuda':0.46,'scholar':462.17,'scopus':49.15,'wos':7.96},'citations':{'garuda':428,'scholar':431668,'scopus':45910,'wos':7432},'cited_documents':{'garuda':265,'scholar':44142,'scopus':5622,'wos':778},'code':'001019','departments':177,'id':404,'journals':67,'last_update':'2022-06-15 09:00:53','location':'KOTA MALANG - JAWA TIMUR, ID','name':'Universitas Brawijaya','sinta_score':{'3_years':220241,'overall':568684,'productivity':277,'productivity_3_years':107},'url':'https://sinta.kemdikbud.go.id/affiliations/profile/404'}Other Output FormatsOther formats can be used by specifying theoutput_formatargument. For example:author=sinta.author(id,output_format='json')Avalable output formats:'dict'(default)'dict-flat''json''json-pretty''xml''xml-flat'You can also pretty-print a dictionary usingpprint:frompprintimportpprintpprint(result_dict)
Package: sintegraIEValidator
sintegra-ie-validatorsintegraIEValidator é um pacote Python projetado para validar inscrições estaduais de diferentes estados brasileiros. Atualmente, suporta validações para os estados do Acre , Goias, Alagoas, São Paulo, Minas Gerais, Pernanbuco. Em breve mais Estados e FuncionalidadesInstalaçãoInstale o pacote via pip:pipinstallsintegraIEValidatorUsoPara usar o sintegraIEValidator, importe a classe de validação correspondente ao estado desejado. Por exemplo:Validando uma Inscrição Estadual de São PaulofromsintegraIEValidatorimportInscricaoEstadualSPvalidador_ac=InscricaoEstadualSP("652896367166")print(validador_ac.is_valid())Validando uma Inscrição Estadual do AcrefromsintegraIEValidatorimportInscricaoEstadualACvalidador_ac=InscricaoEstadualAC("0154899511703")print(validador_ac.is_valid())Validando uma Inscrição Estadual de AlagoasfromsintegraIEValidatorimportInscricaoEstadualALvalidador_al=InscricaoEstadualAL("248368818")print(validador_al.is_valid())DisclaimerEste pacote é fornecido "como está", sem garantias de qualquer tipo. Os desenvolvedores não são responsáveis por quaisquer bugs ou problemas.ContribuiçõesContribuições são bem-vindas, especialmente para expandir a validação para outros estados. Sinta-se à vontade para fazer um fork do projeto, adicionar sua contribuição e criar um pull request.LicençaEste projeto está sob a licença MIT.Ajude o ProjetoConsidere enviar um PIX parajosetiagobsouza@gmail.compara um café , ficarei muito grato.
Package: sintel
An open source project from Data to AI Lab at MIT.SintelSintel(Signal Intelligence) provides Restful APIs to process massive signal data for anomaly analysis in an efficient and user-friendly way.License:MITDevelopment Status:Pre-AlphaHomepage:https://github.com/signals-dev/sintelDocumentation:http://mtv.lids.mit.edu/apidocs(Swagger UI style)http://mtv.lids.mit.edu/redoc(Redoc UI style)PrerequisitesMake sure you have installed all of the following prerequisites on your development machine:Python (>= 3.0)- Sintel has been developed and runs onPython 3.6. Although it is not strictly required, the usage of avirtualenvis highly recommended in order to avoid interfering with other software installed in the system whereMTVis run. To this end,Anaconda pythonis suggested to maintain the virtual environments.Git-Download & Install Git. OSX and Linux machines typically have this already installed.MongoDB (>= 3.6)-Download & Install MongoDB, and make sure it's running on the default port (27017).Get StartedQuick InstallOnce you've downloaded the Sintel repository and installed all the prerequisites, you're just a few steps away from running your application. To install the project, create a virtualenv and execute$makeinstallThis command will install all the dependencies needed for the application to run. For development, use the following command instead, which will install some additional
dependencies for code linting and testing$makeinstall-developRunning Your ApplicationPlease activate your virtualenv, and then launch the API server:$sintelrun-vGo to the API playground (http://localhost:3000/apidocs) to have a try.DevelopmentRun the following command for the purpose of development$sintelrun-Edevelopment-vDataThe commandmake installormake install-develophas already pull the demo dataset and restore it into MongoDB. The database name by default issintel.Working withOrionto generate your own dataYou can type the following command to update the data from Orion to Sintel-supported formats. Note that you can configure the mongodb in the file./sintel/config.yaml.$mtvupdatedb-vUse Docker to deployInstallDockerandComposeLoad data into the mongo container$makedocker-db-up$makedocker-upGo to the API playground (http://localhost:3000/apidocs) to have a try. For further commands, please refer toMakefile, the session of Docker Installation.History0.1.0First release on PyPI.
Package: sinto
Sinto is a toolkit for processing aligned single-cell data. Sinto includes functions to:Subset reads from a BAM file by cell barcodeCreate a scATAC-seq fragments file from a BAM fileAdd read tags to a BAM file according to cell barcode informationAdd read groups based on read tagsCopy or move read tags to another read tagCopy cell barcodes to/from read names or read tagsAdd cell barcodes to FASTQ read namesRead the documentation athttps://timoast.github.io/sinto
Package: sinunika-parser
SINTAK PARSERHOST: sintak.unika.ac.id/id/USAGEfromlib.FetchimportFetchusername='15.N1.0020'password='tanggal lahir'sintak=Fetch(username,password)MethodgetDataBarChart(self)Provide all IPS until now, data return in JSONgetDataPieChart(self)Provide all academic value A/AB/etc and count it, data return in JSONgetKtm(self, saveTo)Get KTM Sementara and save to directoryUPDATEsoonContributingPull request is welcome and i so happy if you help me :)Error/IssuePlease create new issue if you encounter some problemLICENSE
Package: sinutils
sinutilsA Python Utitlity Modules containing various useful functions.Installpip install sinutilsUsage# -*- coding: UTF-8 -*'''Created on 2021-04-16'''from__future__importprint_functionimporttimeimportsinutils# 进度显示count=80withsinutils.Progress(total=count,title='TEST')aspg:foriinrange(count):pg.feed(status='C%d'%i)time.sleep(.01)# 任务分割器defdoit(datas):print('handle',datas)withsinutils.Feeder(doit,5)asfd:foriinrange(12):fd.feed(i)# watch modulesimporttestmtestm.test()mw=sinutils.ModulesWatcher([testm])whileTrue:# edit testm.py to test auto_reload functionmds=mw.auto_reload()ifmds:print('changed',mds)testm.test()time.sleep(1)Output:TEST...
TEST...  5% Finish:4/80 Spent:35ms Speed:112/s Left:676ms Status:C3
TEST... 10% Finish:8/80 Spent:80ms Speed:100/s Left:721ms Status:C7
TEST... 15% Finish:12/80 Spent:124ms Speed:96/s Left:704ms Status:C11
TEST... 20% Finish:16/80 Spent:169ms Speed:94/s Left:679ms Status:C15
TEST... 25% Finish:20/80 Spent:218ms Speed:91/s Left:656ms Status:C19
TEST... 30% Finish:24/80 Spent:266ms Speed:90/s Left:621ms Status:C23
TEST... 35% Finish:28/80 Spent:313ms Speed:89/s Left:583ms Status:C27
TEST... 40% Finish:32/80 Spent:359ms Speed:89/s Left:539ms Status:C31
TEST... 45% Finish:36/80 Spent:405ms Speed:89/s Left:495ms Status:C35
TEST... 50% Finish:40/80 Spent:453ms Speed:88/s Left:453ms Status:C39
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TEST... 65% Finish:52/80 Spent:588ms Speed:88/s Left:316ms Status:C51
TEST... 70% Finish:56/80 Spent:632ms Speed:89/s Left:270ms Status:C55
TEST... 75% Finish:60/80 Spent:677ms Speed:89/s Left:225ms Status:C59
TEST... 80% Finish:64/80 Spent:724ms Speed:88/s Left:181ms Status:C63
TEST... 85% Finish:68/80 Spent:773ms Speed:88/s Left:136ms Status:C67
TEST... 90% Finish:72/80 Spent:821ms Speed:88/s Left:91ms Status:C71
TEST... 95% Finish:76/80 Spent:867ms Speed:88/s Left:45ms Status:C75
TEST...100% Finish:80/80 Spent:916ms Speed:87/s Left:0ms Status:C79
TEST Finished Spent:927ms
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hi 43311Click to view more information!CHANGES0.0.1first version