Saravanan666/pypi-org huggingface dataset:
Package: sirutalib
This projectaims to create a
library that can import aSIRUTAdatabase and offer simple access to the different elements of the
database.What is SIRUTA?SIRUTAis the official clasification of the Romanian towns and villages
(hereafter called entity). It is maintained by theNational Statistics
Institute.It gives every entity a 6 digit code (5-digit unique code and 1-digit
checksum). The whole classification is hierachical, with Romania (the
country) as root, then 40 counties + Bucharest. Bucharest contains the
city of Bucharest, which in turn contains 6 sectors. Every county has
municipalities, citiess and communes, and each of those is comprised of
towns and villages.The SIRUTA archives contain detailed documentation about the whole
classification, including the algoritm for the checksum.::: {.note}
::: {.title}
Note
:::This library makes the assumption that SIRUTA codes shorter than 6
characters are filled with 0 to the left in order to calculate the
checksum. There are 77 codes that do not respect this assumption. Out of
those, 76 can be calculated if the code is filled with 0's to theright. The remaining code is 9026.
:::Getting the libraryYou can eitherdownload the tar
file(mirror) or
get the source code, as described in thedevelopment-label{.interpreted-text role="ref"} section.In both cases, you will also get a copy of the most recent SIRUTA
database inCSV (Comma-Separated Values){.interpreted-text
role="abbr"} format.Development {#development-label}Dependenciesa recent version ofpythonis required in order to develop
with SIRUTAlibthis library usesGitfor source control, so you'll need that
if you want to get the full source code.if you want to build the help files, you'll also needsphinxandmake(the latter is optional)Getting the sourceTo work on the SIRUTAlib code, you only need a local repository
checkout:$ git clone https://github.com/strainu/SIRUTA.git
$ cd sirutaYou will find 2 python files::   -sirutalib.pycontains the actual library
-testsiruta.pycontains the tests needed to check the code.That's it, enjoy!Using the libraryA simple usage example is available in the INSTALL file.ContributingIf you plan to contribute code to SIRUTAlib, please keep a few things in mind::   -   code should be formatted according to8{.interpreted-text
role="pep"}
-   tests should be written for all the new code, as long as you
don't need to change class internals to test itThen prepare a patch and submit a pull request ongithub.For more contact options, seecontact-label{.interpreted-text
role="ref"}.Feedback and contact {#contact-label}You can register a bug, feature request or pull request on github:https://github.com/strainu/SIRUTA/issuesIf you want to contact the author, you can do it by emailingsiruta [at] strainu.ro. All the latest information is available onthe project's page.
Package: sis
Sisyphus is a little helper script to re-run arbitrary commands over and over again. Poor Sisyphus will execute the argument list as a command, monitor the current directory for changes and re-run the command each time a modification is detected. Also, it will terminate a long-running command when a modification is detected and start over.It is based on the pyinotify example script “[autocompile.py](https://github.com/seb-m/pyinotify/blob/master/python2/examples/autocompile.py)”.You can use Sisyphus toauto-compile your programauto-run your test suiteauto-restart your development serverauto-refresh your browser (i.e., via [MozRepl](https://github.com/bard/mozrepl/wiki)) when doing web developmentauto-restart your unsuccessful shell command until it succeeds.It can be convenient to see the output of your program as soon as you save it, without leaving your editor. But remember: Your actions might put Sisyphus in an existential crisis where it is faced with questions about the absurdity of life.RequirementsLinux ≥ 2.6.13[pyinotify](https://github.com/seb-m/pyinotify)Both Python 2.x and 3.x are supportedInstallationpip install sisExamplessis -d src makesis -e hs cabal testsis -s ‘lynx -dumphttp://my-web-app/’Ignore patternsSisyphus will ignore changes in files matching any regex pattern found in the following files:/etc/sisignore~/.sisignore.sisignore
Package: siscad
Módulo para extrair informações do SiscadExemplofromsiscadimportSiscadsiscad=Siscad("passaporte","senha")print(siscad.rga)print(siscad.nome)print(siscad.carga_hor)print(siscad.cr)print(siscad.discs_feitas)print(siscad.ultima_matricula)forsemestreinsiscd.get_semestres():print(semestre.date)fordisciplinainsemestre.discs:print(disciplina.nome)print(*disc.notas,sep="\n")
Package: sis-calibration
Photon assisted tunneling:$$I_{p}(V_0,\alpha) = \sum_{n=-\infty}^{+\infty} J_n^2(\alpha) \cdot I_{dc}(V_0 + n\hbar \omega / e)$$Components of $Y_{mm\prime} = G_{mm\prime} + i B_{mm\prime}$G_{mm\prime} = \frac{e}{2 \hbar \omega_{m\prime}} \cdot
\;\sum_{n,n\prime=-\infty}^{\infty} J_n(\alpha) J_{n\prime}(\alpha) \delta_{m-m\prime, n\prime-n}
\left\{ \left[ I_{dc}(V_0+n\prime \hbar \omega /e + \hbar \omega_{m\prime}/e) -
I_{dc}(V_0 + n\prime \hbar \omega/e) \right] +
\left[ I_{dc}(V_0 + n\hbar \omega/e) -
I_{dc}(V_0 + n \hbar \omega/e - \hbar \omega_{m\prime}/e) \right]  \right\}B_{mm\prime} = \frac{e}{2 \hbar \omega_{m\prime}} \cdot
\sum_{n,n\prime=-\infty}^{\infty} J_n(\alpha) J_{n\prime}(\alpha) \delta_{m-m\prime, n\prime-n}
\left\{ \left[ I_{kk}(V_0+n\prime \hbar \omega /e + \hbar \omega_{m\prime}/e) -
I_{kk}(V_0 + n\prime \hbar \omega/e) \right] -
\left[ I_{kk}(V_0 + n\hbar \omega/e) -
I_{kk}(V_0 + n \hbar \omega/e - \hbar \omega_{m\prime}/e) \right]  \right\}$\omega_m = m \cdot \omega + \omega_0$$\omega$ - LO rate, $\omega_0$ - IF rate, $\omega_m$ - Signal rateAugmentedY'_{mm} =
\begin{bmatrix}
    Y_{11} + Y_{S} & Y_{10} & Y_{1-1}  \\
    Y_{01} & Y_{00} + Y_L & Y_{0-1}   \\
    Y_{-11} & Y_{-10} & Y_{-1-1}+Y_I  \\
\end{bmatrix}$Y_{IF} = 1/Z'_{00}$All there formulas from Tucker & Feldman theoryFor I-V curve analysis used RespFnFromIVData from QMixhttps://github.com/garrettj403/QMixReleases are published automatically when a tag is pushed to GitHub.shdeploy.sh-t{tag}.\deploy.bat {tag}
Package: siscomex-ncm
siscomex-ncmNCM (Nomenclatura Comum do Mercosul)NCMé a sigla paraNomenclatura Comum do Mercosul, toda e qualquer mercadoria que circula no Brasil deve ter este código. A NCM permite a identificação padronizada das mercadorias comercializadas, ou seja todo produto possui uma NCM.O código deve ser informado no preenchimento da nota fiscal e outros documentos de comércio exterior.A NCM é adotada por todos os países membros do Mercosul desde janeiro de 1995 e tem como base o método internacional de classificação de mercadoria, chamado como SH (Sistema Harmonizado de Designação e de Codificação de Mercadorias).O código é usado nas operações de exportação e importação de mercadorias desde 1995, já no mercado interno é obrigatório desde 2013.Como funciona?A Nomenclatura Comum do Mercosul obedece à seguinte estrutura de código: 0000.00.00Ou seja, é um código de oito dígitos que correspondem ao produto. Cada um dos numerais representa algo diferente, conforme abaixo:Os dois primeiros caracterizam o produto (capítulo);Os dois números seguintes abrangem mais sobre a característica do produto (posição);O quinto e sexto definem a subcategoria do mesmo (ou subposição);O sétimo o classifica (item); eO oitavo se refere ao subitem, que descreve especificamente do que se trata a mercadoria.Para exemplificar, veja a NCM4820.20.00, deve ser entendido da seguinte forma:Capítulo 48: Papel e cartão; obras de pasta de celulose, de papel ou de cartão.Posição 48.20: Livros de registro e de contabilidade, blocos de notas, de encomendas, de recibos, de apontamentos, de papel para cartas, agendas e artigos semelhantes, cadernos, pastas para documentos, classificadores, capas para encadernação (de folhas soltas ou outras), capas de processos e outros artigos escolares, de escritório ou de papelaria, incluindo os formulários em blocos tipo manifold, mesmo com folhas intercaladas de papel-carbono (papel químico), de papel ou cartão; álbuns para amostras ou para coleções e capas para livros, de papel ou cartão.Subposição: Neste exemplo não tem.Item: Neste exemplo não tem.Subitem 4820.20.00 – CadernosComo instalar a biblioteca pelo PyPI?pip install siscomex-ncmComo instalar a biblioteca pelo código-fonte?Faça fork deste projetoInstale opoetryInstale as dependências do projeto:poetry installComo executar os testes?Executar os testes:make testComo usar essa biblioteca?Importar a bibliotecafromncm.entitiesimportNcm,NcmListfromncm.clientimportFetchNcmDownload do arquivo JSONfetch_ncm=FetchNcm()fetch_ncm.download_json()Gravar do arquivo JSON localmentefetch_ncm=FetchNcm()fetch_ncm.download_json()fetch_ncm.save_json(json_data)json_data=fetch_ncm.load_json()Consulta código NCM específicofetch_ncm=FetchNcm()obj_dict=fetch_ncm.get_codigo_ncm('01031000')print(obj_dict.descricao_ncm)# result: '- Reprodutores de raça pura'Consulta toda a lista de NCMsfetch_ncm=FetchNcm()ncm_list=fetch_ncm.get_all()print(ncm_list.ncm_list)Consulta toda a lista de NCMs e retorno somente os códigos com 8 dígitosfetch_ncm=FetchNcm()fetch_ncm.only_ncm_8_digits=Truencm_list=fetch_ncm.get_all()print(ncm_list.ncm_list)LicençaMIT License
Package: sisense
Sisense APIPython interface for Sisense API.NoteIn development. Beta version available in:$pipinstall-ihttps://test.pypi.org/simple/sisenseInstall$pipinstallsisenseGetting startedfromsisenseimportSisensesisense=Sisense(host,token)datamodel=sisense.datamodelnew_datamodel=datamodel.clone('Datamodel copy')build=new_datamodel.start_build('replace_all')build.stop()For developersTestingTo run all test:$makeapi-testSupport filesIn order to test the API functions, you need to supply the following support files:tests/support_files/dashboard.dash: file created when exporting a dashboard on Sisensetests/support_files/datasecurity.json: a list of data security rulestests/support_files/hierarchy.json: a list of hierarchy objectstests/support_files/permission.json: a list of permission objectstests/support_files/config/api_v09.json: a JSON with{"host":"<host address>","token":"<API token>","elasticube":"<Elasticube's name used for testing>","user_email":"<User e-mail used for testing>"}tests/support_files/config/api_v1.json: a JSON with{"host":"<host address>","token":"<API token>","elasticube":"<Elasticube's name used for testing>","user_email":"<User's e-mail used for testing>","group_name":"<Group's name used for testing>","dashboard":"<Dashboard's name used for testing>","folder":"<Folder's name used for testing>"}tests/support_files/config/api_v2.json: a JSON with{"host":"<host address>","token":"<API token>","datamodel":"<Elasticube's name used for testing>","datamodel.oid":"<Elasticube's ID used for testing (must be the same datamodel)>",}
Package: sis-great-ai
⚠️ This is a fork of the original GreatAI library by@schmelczer, maintained by ScoutinScience. Make sure to installsis-great-aifrom PyPI to use this distribution.GreatAIEasily transform your prototype AI code into production-ready software.Applying AI is becoming increasingly more accessible, but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing unintended biases. GreatAI helps fix this by allowing you to easily transform your prototype AI code into production-ready software.Examplepipinstallsis-great-aiCreate a new file calleddemo.pyfromgreat_aiimportGreatAI@GreatAI.createdefgreeter(name:str)->str:returnf"Hello{name}!"Start it by executinggreat-ai demo.py, and find the dashboard athttp://localhost:6060.That's it. Your GreatAI service isnearlyready for production use. Many of theSE4ML best practicesare configured and implemented automatically (of course, these can be customised as well).Check out the full documentation here.Why is this GREAT?GreatAI fits between the prototype and deployment phases of your AI development lifecycle. This is highlighted in blue in the diagram. Here, several best practices can be automatically implemented, aiming to achieve the following attributes:General: use any Python library without restrictionRobust: have error-handling and well-tested utilities out-of-the-boxEnd-to-end: utilise end-to-end feedback as a built-in, first-class conceptAutomated: focus only on what actually requires your attentionTrustworthy: deploy models that you and society can confidently trustWhy GreatAI?There are other existing solutions aiming to facilitate this phase.Amazon SageMakerandSeldon Coreprovide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great.However,research indicatesthat professionals rarely use them. This may be due to their inherent setup and operational complexity.GreatAI is designed to be as simple to use as possible.Its straightforward, high-level API and sensible default configuration make it easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of theSE4ML best practices, and thus, can meaningfully improve your deployment without requiring prohibitively great effort.Learn moreCheck out the full documentation here.Findgreat-aionPyPIpipinstallsis-great-aiFindgreat-aionDockerHubdockerrun-p6060:6060scoutinscience/great-aiContributeContributions are welcome.Install for developmentpython3-mvenv--copies.envsource.env/bin/activate
pipinstall--upgradeflitpip
flitinstall--symlinkDevelopscripts/format-python.shgreat_aidocstestsFormat code.python3-mpytest--doctest-modules--asyncio-mode=strict.Run tests.mkdocsserveServe documentation.
Package: sisl
sislCopyright Nick R. Papior 2023sisl is anopen-source easy-to-use density functional theory API frameworkto post-analyse density functional theory codes output
as well as providing tight-binding calculation capabilities.
It couples to a wide range of density functional theory codes and has a high connection with LCAO codes such asSiesta. The tight-binding matrices can be used in non-equilibrium Green function calculations withTBtransas a backend. API for creating publication ready graphs and images.FeaturesSimple command-line interface: To extract or quickly plot calculation outputTight-binding API: Easily create (non-) orthogonal tight-binding matrices and do electronic structure analysisManipulation of sparse matrices: Extract, replace, append matrices and sub-matrices to one anotherPost analyse DFT simulations: Post-process LCAO Hamiltonians by doing (projected) density of states, inverse participation ratio and many morePost analyse NEGF simulations: Effectively create and post-analyse NEGF output fromTBtransReal-space grid analysis: Perform mathematical operations on DFT real-space grid outputs, spin-density differences and wavefunction plotsConversion of geometries and real-space grid: Easy conversion of geometry files and real-space grid file formats (cube, xsf, etc.)User contributed toolboxes: Users may contribute toolboxes for sharing methodologiesInteroperability with other codes:ASEandpymatgenand many more featuresTutorials and examplesThe easiest way to get started is to follow the tutorialshereand the workshop material forTranSiestahere.DocumentationPlease find documentation here:DocumentationAPI documentationInstallationCommunity supportThere are different places for getting information on using sisl, here is a short list
of places to search/ask for answers:Ask questions on theDiscord pageAsk questions on the Githubissue pageDocumentation, recommended reference pageWorkshopexamples showing different usesIf sisl was used to produce scientific contributions, please use thisDOIfor citation.
We recommend to specify the version of sisl in combination of this citation:@misc{zerothi_sisl,
  author = {Papior, Nick},
  title  = {sisl: v<fill-version>},
  year   = {2023},
  doi    = {10.5281/zenodo.597181},
  url    = {https://doi.org/10.5281/zenodo.597181}
}To get the BibTeX entry easily you may issue the following command:sdata --citewhich fills in the version number.ContributingKindly read ourContributing Guideto learn and understand about our development process, how to propose bug fixes and improvements, and how to build and test your changes to sisl.ContributorsLicensesisl © 2023, Released under the Mozilla Public License v2.0.
Package: sisl-gui
This is the graphical interface that you can use to interact withsisl'svisualization module.It consists of two parts:Back-end: The backend is implemented in python. The most important part is theSessionclass which stores
and manages the app state. TheSessioncan be connected to the frontend either using pyodide (it runs directly on the browser)
or through sockets (implemented withflaskandflask-socketio).Front-end: A javascript/typescript UI, which is developed usingReact. The build is
shipped with the python package and also available athttps://pfebrer.github.io/sisl-gui/.User guideInstallationIt is extremely simple to installsisl-guiwithpip:pip install sisl-guiUsageFrom the browserThis is the simplest way, since it doesn't have any requirements.You don't even need to install itas mentioned
in the previous section, just go tohttps://pfebrer.github.io/sisl-gui/.From a terminalYou can use thesisl-guicommand.From pythonimportsisl_guiapp=sisl_gui.launch()
Package: sismoscl
sismosclSismos ocurridos en Chile, casi en tiempo realInstalaciónCon python >= 3.7pipinstall-UsismosclConfiguraciónImporta la librería, luego su funciónfromsismosclimportsismosUsoDevuelve una lista con los últimos sismos registrados por la Universidad de Chilesismos()Devuelve una lista con los últimos sismos mayores a magnitud 2sismos(2)
Package: sisosign
sisosignFor those times when you are pair programming and the story is running longer than expected. Someone needs to go but you can't decide who should stay on the story.Why not have a computer make these tough choices for you?sisosignmakes it easy.Installationpip install sisosignYou might needsudodepending on your setup.Example usagesisosign firstname1 firstname2This will print a message informing you of who should stay. Et voila! Problem solved.ConclusionThat's it. It doesn't do anything else.
Package: sispo
# Space Imaging Simulator for Proximity Operations (SISPO)[![sispo](https://img.shields.io/badge/powered%20by-sispo-orange?style=flat)](https://github.com/YgabrielsY/sispo)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3661054.svg)](https://doi.org/10.5281/zenodo.3661054)Currently, a problem exists with themathutilsimport in thesim.py. An import ofbpymust be done before running sispo.## Documentation[![Documentation Status](https://readthedocs.org/projects/sispo/badge/?version=latest)](https://sispo.readthedocs.io/en/latest/?badge=latest)Currently, the most extensive documentation on the project can be found in thedocfolder. It contains the master thesis that was the starting point of this repository. The thesis is written in Latex and might need to be compiled.Another kind of documentation is started using Read the Docs. It can be found by following the badge.If you are using this package in a code project, please acknowledge by adding our badge to your readme file by copying:[![sispo](https://img.shields.io/badge/powered%20by-sispo-orange?style=flat)](https://github.com/YgabrielsY/sispo)There is another repository linked to sispo which uses Docker to simplify the installation. Can be found here [sispo_docker](https://github.com/SISPO-developers/sispo_docker)## Acknowledgements
Acknowledgements to packages used[![astropy](http://img.shields.io/badge/powered%20by-AstroPy-orange.svg?style=flat)](http://www.astropy.org/)
Package: sistdmyy
#从2016年开始我在深圳市标准技术研究院的工作内容打包如下1 __init__模块：数据库访问,excel表输出2 dz模块地址挖掘分析，地理数据标准化街道逆向迭代3 福田模块类福田模式的数据自动化-------------------------------------------------------documentation------------------------------------------------------------------此部分和地址分析相关dz模块的算法，主要依靠正则、jieba分词，进行规则的堆砌。__init__.py里有 类对象 mine#mine是用来生成dz_dz表的'''这个类主要接口是：dz_dasa返回地理名词dz_dasa("深圳市盐田区沙深路粤和街13号稳胜大厦A栋五楼左侧503室",'大厦')=稳胜大厦pats()生成一个类变量P,p是一个字典，定义每种建筑类型的提取规则P['大厦']=[关于该名词的正则规则]minedf(df['jgdz'])return df形成一个 type-word-tag-tf的表数据库中生成 dz_dz表---------------------------------------------------------------------------------------------------------------------------------------dz_std.py 有分割对象 std#std用于将一个地址分割'''需要dz_dz  dz_qh dz_jd 三张表spl(a)分解深圳市福田区莲花街道北环大道7045号哈尔滨大厦1-07 成一个dick但是要注意一个词典的问题loadword()是切换成 dz_dz的词这不是做好的设计，此处将来可能要改成,self.words self.word2s分开，切换动作单独做成一个函数tb()集成tb的四个后续处理std类是运用得到的语料将地址标准化z=std(sql)sql="select ztsfdm,jgdz from basedb.dbo.t_zzjgjz where clrq>='2017-05-01'"初始化sql,决定需要处理的df  ztsfdm-jgdz最终结果存在示例的变量df里,z.df依赖：需要dz_dz  dz_qh dz_jd 三张表生成：dfjgdz    ztsfdm  区划  村社镇 楼群  街道  路道街 路道街扩展   村社镇扩展深圳市宝安区西乡街道固戍二路泳辉商务大厦510 440300000012017030209766    宝安区     泳辉商务大厦  西乡街道    固戍二路深圳市龙华新区观澜街道章阁社区环观南路94号德盛昌大厦1501 440300000012017030212091    龙华新区    章阁社区    德盛昌大厦   观澜街道    环观南路    环观南路94号---------------------------------------------------------------------------------------------------------------------------------------dz_jd.py中有对象jdtrain,jd_predictjdtrain用来生成 dz_std_jdtrain 表，街道的训练语料p_corpus()生成dz_std_jdcorpus表ztsfdm  jgdz    word    rn440300000012017030209766    深圳市宝安区西乡街道固戍二路泳辉商务大厦510 西乡街道    1440300000012017030212091    深圳市龙华新区观澜街道章阁社区环观南路94号德盛昌大厦1501 观澜街道    1440300000012017030213027    深圳市龙岗区龙岗街道银威路吓岗一区8巷3号701    龙岗街道    1p_stdtrain()用std.mindf() 将dz_Std_jdcorpus生成dz_std_jdtrain表jgdz    ztsfdm  区划  村社镇 楼群  街道  路道街 路道街扩展   村社镇扩展深圳市宝安区西乡街道固戍二路泳辉商务大厦510 440300000012017030209766    宝安区     泳辉商务大厦  西乡街道    固戍二路深圳市龙华新区观澜街道章阁社区环观南路94号德盛昌大厦1501 440300000012017030212091    龙华新区    章阁社区    德盛昌大厦   观澜街道    环观南路    环观南路94号p_update()替换dz_std_jdtrain中的街道为 dz_std_jdcorpus中的街道涉及表dz_std_jdcorpus /dz_std_jdtrainjd_predict运用dz_std_jdtrain，来你逆向迭代那些地址中不包含标准街道名称的。p_df(sql)sql决定，需要转换的对象select  ztsfdm,jgdz from basedb.dbo.t_zzjgjz where ztsfdm is not null and jgdz is not null对于对象，能用地址名中包含标准街道名称的会直接给出，不含的会通过train数据预测return df:columns=ztsfdm-jgdz-wordp_write(name=None,sql=None)包含转换和写入写到name=dz_sist_jdp_update(udate)udate='2017-05-01'针对每次全部重写太慢，这个函数只更新udate以后成立和地址变更的企业在更新地址变更部分，会用到dz_sist_jdAlterTmp表这张表记录udate后做了地址变更的对象，用以重新更新涉及表：dz_sist_jd  dz_sist_jdAlterTmp-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------数据库表总结-----------------------------------------------------------------------------dz_dz  dz_qh dz_jddz_std_jdcorpus /dz_std_jdtraindz_sist_jd  dz_sist_jdAlterTmp---------------------------------------------------------------------------------------------------------------------------------------
Package: sister
sisterSISTER (SImpleSenTenceEmbeddeR)InstallationpipinstallsisterBasic Usageimportsistersentence_embedding=sister.MeanEmbedding(lang="en")sentence="I am a dog."vector=sentence_embedding(sentence)Supported languages.EnglishJapaneseFrenchIn order to support a new language, please implementTokenizer(inheritingsister.tokenizers.Tokenizer) and add fastText
pre-trained url toword_embedders.get_fasttext()(List of model urls).Bert models are supported for en, fr, ja (2020-06-29).Actually Albert for English, CamemBERT for French and BERT for Japanese.To use BERT, you need to install sister bypip install 'sister[bert]'.importsisterbert_embedding=sister.BertEmbedding(lang="en")sentence="I am a dog."vector=bert_embedding(sentence)You can also give multiple sentences to it (more efficient).importsisterbert_embedding=sister.BertEmbedding(lang="en")sentences=["I am a dog.","I want be a cat."]vectors=bert_embedding(sentences)