Saravanan666/pypi-org huggingface dataset:
Package: openbandparams
The main goal of this project is to provide easy access to semiconductor
band parameters for calculations and simulations. Basic functionality
requires only the standard python distribution.Example scripts are provided for basic usage and for generating common
plots such as bandgap vs. lattice constant and bandgap vs. alloy
composition.Materials included in this version:III-V Zinc BlendesBinariesAlN, GaN, InN,
AlP, GaP, InP,
AlAs, GaAs, InAs,
AlSb, GaSb, InSbTernariesAlGaN, AlInN, GaInN,
AlGaP, AlInP, GaInP,
AlGaAs, AlInAs, GaInAs,
AlGaSb, AlInSb, GaInSb,
AlNP, GaNP, InNP,
AlNAs, GaNAs, InNAs,
AlPAs, GaPAs, InPAs,
AlPSb, GaPSb, InPSb,
AlAsSb, GaAsSb, InAsSbQuaternariesAlNPAs, AlPAsSb,
GaNPAs, GaPAsSb,
InNPAs, InPAsSb,
AlGaInN, AlGaInP, AlGaInAs, AlGaInSb,
AlGaNP, AlInNP, GaInNP,
AlGaNAs, AlInNAs, GaInNAs,
AlGaPAs, AlInPAs, GaInPAs,
AlGaPSb, AlInPSb, GaInPSb,
AlGaAsSb, AlInAsSb, GaInAsSbParameters included in this version:lattice constantsthermal expansion coefficientsbandgap energies (direct and indirect)Varshni parameterssplit-off energieseffective massesLuttinger parametersKane parameters (Ep and F)Valance band offsetsband deformation potentialselastic constantsalloy bowing parameterseffects of biaxial strainThesource codeanddocumentationare graciously hosted by GitHub.
Package: openbankingapi
OpenBanking API wrapperA simple wrapper for the the UK open banking APIs.https://www.openbanking.org.ukCurrently implemented a simple wrapper around retrieving a list of banks and business related data.Currently only tested on Python 3.4Installpip install openbankingapiExamplefrom openbankingapi import OpenBankingApi

api = OpenBankingApi(timeout=5)

banks = api.banks() # retrieves the list of participating banks

sme_loans = api.unsecured_sme_loans(banks[0])

current_accounts = api.business_current_accounts(banks[0])

credit_cards = api.commercial_credit_cards(banks[0])
Package: openbankingbr
OpenBankingBRUm pacote Python para carregar, analisar e exportar osdados públicosexpostos pelos participantes doOpenBanking Brasil.This package is intended to extract public data from the Open Bank Brasil. Since most users of this library will be Brazilians, everything is in Portuguese.Dados suportados:ParticipantesAgênciasvia/open-banking/channels/v1/branchesfromopenbankingbrimportOpenBankingBRob=OpenBankingBR(cacheDir='./cache')forparticipanteinob.participantes:foragenciainparticipante.agencias:print(f'Participante:{participante.nome}> agencia código:{agencia.codigo}')Produtosvia/open-banking/products-services/v1/*fromopenbankingbrimportOpenBankingBRob=OpenBankingBR(cacheDir='./cache')forparticipanteinob.participantes:print(f'Participante:{participante.nome}')forprodutoinparticipante.produtos:print(f'Produto:{produto.tipo}-{produto.nome}')# Serviços disponíveis no produtoforservicoinproduto.servicos:print(f'    Serviço >{servico.nome}')# Pacotes de serviço disponíveis no produto.forpacoteinproduto.pacotes:print(f'    Pacote >{pacote.nome}')Mecanismo de cacheComo esta biblioteca realiza centenas de chamadas em API's das financeiras, existe um mecanimo padrão de salvar os dados retornados pelos end-points das apis em uma pasta de cache com o objetivo de não enviar um request bem sucedido mais de uma vez no mesmo dia para a endpoint/financeira.Basicamente é um mecanismo de bom senso para não abusar das apis disponibilizadas pelas financeiras, porém você pode desabilitar este comportamento comcacheDir = None(mas não recomendo).Processamento batchO objetivo inicial ao criar este pacote foi exportar os dados que são listados pelos participantes periodicamente para realizar análises dos produtos de crédito das instituições financeiras.Uma forma simples de exportar todos produtos listados:fromopenbankingbr.batchimportBatchOpenBankingbatch=BatchOpenBanking(dataDir='./data',cacheDir='./cache',ignoraErros=True,)batch.todos_dados()## ou... somente os aquivos que interessar.## batch.agencias_csv()# batch.produtos_csv()# batch.servicos_csv()# batch.pacotes_csv()Notas importantesQualidade dos dadosEm teoria as APIs são homologadas pelo Bacen, porém na prática consultando as APIs é facilmente observado queváriasinstituições financeiras não seguem o padrão da documentação, não sei como passaram na homologação, mas o fato é que o dado problemático existe. Nesta biblioteca tentei contornar e validar o máximo dos dados, ignorando os valores que são absurdamente fora dopadrãoda documentação.SegurançaA validação de certificados SSL teve que ser desabilitada, visto que as financeiras Brasileiras não se dão ao trabalho de ter um certificado SSL válido para as APIs expostas para a internet.
Package: openbases
OpenBases PythonThis is a small module with helper functions and scripts for using Open Bases tools.
Please contribute atopenbases/openbases-pythonon
Github. Documentation ishereJust... no!Better!Quick StartTo installgitclonehttps://www.github.com/openbases/openbases-pythoncdopenbases-python
pythonsetup.pyinstallpip install openbasesThere are different console entry points depending on the kind of functions that you need.Open Bases IconsGet an icon fromopenbases-icons$ob-icons
https://openbases.github.io/openbases-icons/flaticon/sea-life-collection/squid.svg$ob-icons--help

OpenBasesIcons[v0.0.1]usage:ob-icons[--version][--regexpREGEXP][--help][--urlURL][--nN][--sepSEP]OpenBasesPythonIcons

optionalarguments:--versionshowopenbasespythonversion--regexpREGEXPregularexpressionfilterforiconname--helpshowopenbasesiconshelp--urlURLcompleteurlforjsonlistoficons--nN,--NNnumberoficonstoreturn--sepSEPseparatortoprinticonstoscreen(defaultnewline)icons usageOpen Base BadgesGenerate badges and strings of badges with theob-badgeentrypoint$ob-badgeOpenBasesBadgesPython[v0.0.3]usage:ob-badge[-h][--version]generalusage...

OpenBasesPythonBadges

optionalarguments:-h,--helpshowthishelpmessageandexit--versionshowopenbasespythonversion

actions:actionsforopenbasesgeneralusagedescriptionviewviewoptionsforstyle,labels,etc.createextractvaluesfromapaper.md$ ob-badge create experiment labjs
![https://img.shields.io/badge/experiment-labjs-%23eaab1b.svg?style=flat&link=https%3A%2F%2Fopenbases.github.io](https://img.shields.io/badge/experiment-labjs-%23eaab1b.svg?style=flat&link=https%3A%2F%2Fopenbases.github.io)badges usageOpen Bases Paperob-paperis the console entry point for interacting with a paper, typically a paper.md.$ob-paperhelpOpenBasesPaperPython[v0.0.0]usage:ob-paper[--debug][--quiet][--version]generalusage...

OpenBasesPythonPaper

optionalarguments:--debug,-duseverboseloggingtodebug.--quiet,-qsuppressallnormaloutput--versionshowopenbasespythonversion

actions:actionsforopenbasesgeneralusagedescriptiongetextractvaluesfromapaper.mdshellstartaninteractiveshellwithopenbasespaperFor complete usage, please see:usage documentationdocumentationimplementation inopenbases-pdfandbuilder-pdfHave fun!
Package: openbayestool
openbayestool该工具用于OpenBayes下参数、指标的记录。快速开始该类库将会把模型执行过程中的parameters和metrics提交到openbayes-server方便记录每次模型的结果。fromopenbayestoolimportlog_param,log_metric,clear_metric,clear_param# 记录参数 `learning_rate=0.01`log_param('learning_rate',0.01)# 同一参数将会记录最后一个请求的结果 `foo=3`log_param('foo',1)log_param('foo',2)log_param('foo',3)# 记录模型的运行结果 `precision=0.77`log_metric('precision',0.77)# 同一个结果 precision 多次记录会追加结果，即结果为 [0.79, 0.82, 0.86]log_metric('precision',0.79)log_metric('precision',0.82)log_metric('precision',0.86)# 清理一个自定义的 metricclear_metric('precision')# 清理一个自定义的 paramclear_param('foo')安装注意在OpenBayes内无需安装，默认已经引入了该依赖。pipinstall-Uopenbayestool使用注意在 openbayes 所提交的任务会自动设置API 访问 token和要记录的容器的 url无需用户知晓。设置要记录的容器的 url可以通过环境变量配置：JOB_UPDATE_URL=<job-url>，也可以在程序中采用api配置：fromopenbayestoolimportset_callback_url,get_callback_urlset_callback_url('<job-url>')# set the job-urlget_callback_url()# return the job-url设置访问 API 的 token可以通过环境变量JOB_ACCESS_TOKEN=<job-token>配置，也可以在程序中采用api配置：fromopenbayestoolimportset_access_token,get_access_tokenset_access_token('<job-token>')# set the job-tokenget_access_token()# return the job-token通过 api 操纵parameters和metricsfromopenbayestoolimportlog_param,log_metric,clear_metric# 记录参数 `learning_rate=0.01`log_param('learning_rate',0.01)# 同一参数将会记录最后一个请求的结果 `foo=3`log_param('foo',1)log_param('foo',2)log_param('foo',3)# 记录模型的运行结果 `precision=0.77`log_metric('precision',0.77)# 同一个结果 precision 多次记录会追加结果，即结果为 [0.79, 0.82, 0.86]log_metric('precision',0.79)log_metric('precision',0.82)log_metric('precision',0.86)# 清理一个自定义的 metricclear_metric('precision')查看记录结果在 openbayes 的容器页面会展现以上的记录结果并作为自动建模确认下一步参数的依据。
Package: openbb-agents
OpenBB-agentsActive work-in-progress. Consider pre-alpha for now.This is a project that leverages LLMs and OpenBB Platform to create financial
analyst agents that can autonomously perform financial research, and answer
questions using up-to-date data. This is possible as a result of agents
utilizing function calling to interact with the OpenBB platform.InstallationCurrently, we only support Python 3.11. We will be adding support for more version of Python relatively soon.openbb-agentsis available as a PyPI package:pipinstallopenbb-agents--upgradeUsage>>>fromopenbb_agents.agentimportopenbb_agent>>>result=openbb_agent("What is the current market cap of TSLA?")# Will print some logs to show you progress>>>print(result)-ThecurrentmarketcapofTSLA(Tesla,Inc.)isapproximately$695,833,798,800.00.-Thisfigureisbasedonthemostrecentdataavailable,whichisfromJanuary15,2024.-Themarketcapiscalculatedbymultiplyingthecurrentstockprice($218.89)bythenumberofoutstandingshares(3,178,920,000).If you've cloned the repository, you can use therun.pyscript and pass in your query:pythonrun.py"What is the current market cap of TSLA?"Queries can be complex:pythonrun.py"Perform a fundamentals financial analysis of AMZN using the most recently available data. What do you find that's interesting?"Queries can also have temporal dependencies (i.e the answers of previous subquestions are required to answer a later subquestion):pythonrun.py"Who are TSLA's peers? What is their respective market cap? Return the results in _descending_ order of market cap."There is more functionality coming very soon!DevelopmentCreate a new virtual environment, withpoetrypoetry installLinting and FormattingWe're currently experimenting withruffas a drop-in replacement forblack,isortandpylint.You can run linting checks as follows:ruffcheckOr fix linting errors:ruffcheck--fixOr format the code:ruffformatWe've also included these in thepre-commit, if you'd prefer to have these checks run automatically before commiting code.
You can install thepre-commithooks as follows:pre-commitinstallTestingWe are in the process of adding tests.We usepytestas our test-runner:pytesttests/
Package: openbb-chat
OpenBB ChatDescriptionOpenBB Chat provides chat capabilities toOpenBBby leveraging the generative potential of LLMs. The chat is implemented followingInstructGPT. This repository contains the implementations of the NLP models and the training/inference infraestructure.InstallationPoetry# clone projectgitclonehttps://github.com/Dedalo314/openbb-chatcdopenbb-chat# [OPTIONAL] create conda environmentcondacreate-nmyenvpython=3.10
condaactivatemyenv# install poetry (change paths as needed)POETRY_VERSION=1.5.1POETRY_HOME=/opt/poetryPOETRY_VENV=/opt/poetry-venvPOETRY_CACHE_DIR=/opt/.cache
python3-mvenv$POETRY_VENV\&&$POETRY_VENV/bin/pipinstall-Upipsetuptools\&&$POETRY_VENV/bin/pipinstallpoetry==${POETRY_VERSION}# add poetry to PATHPATH="${PATH}:${POETRY_VENV}/bin"poetryinstallHow to runTrain model with default configuration# train demo on CPUpoetryrunpythonopenbb_chat/train.pytrainer=cpu# train demo on GPUpoetryrunpythonopenbb_chat/train.pytrainer=gpuTrain model with chosen experiment configuration fromconfigs/experiment/poetryrunpythonopenbb_chat/train.pyexperiment=experiment_name.yamlYou can override any parameter from command line like thispoetryrunpythonopenbb_chat/train.pytrainer.max_epochs=20data.batch_size=64Released modelsThe modelGriffin-3B-GPTQhas been created as part of this project by quantizingGriffin-3B. In the future, more models will be trained and released as needed.Sample usage with pre-trained modelsIn the repositoryhttps://github.com/GPTStonks/apiopenbb-chatis used to performretrieval-augmented generation(RAG) with OpenBB's official documentation and pre-trained models. In particular, theclassifiersmodules are used to find the appropriate function in OpenBB and thellmsmodules are used to complete the function call.LicenseThe Dockerfile is based on the imagenvidia/cuda, which states that the following notice must be included:This software contains source code provided by NVIDIA Corporation.
Package: openbb-core
OpenBB Platform - CoreOverviewThe Core extension serves as the foundational component of the OpenBB Platform. It encapsulates essential functionalities and serves as an infrastructural base for other extensions. This extension is vital for maintaining the integrity and standardization of the platform.Key FeaturesStandardized Data Model(DataClass): A flexible and dynamic Pydantic model capable of handling various data structures.Standardized Query Params(QueryParamsClass): A Pydantic model for handling querying to different providers.Dynamic Field Support: Enables handling of undefined fields, providing versatility in data processing.Robust Data Validation: Utilizes Pydantic's validation features to ensure data integrity.API Routing Mechanism(RouterClass): Simplifies the process of defining API routes and endpoints - out of the box Python and Web endpoints.Getting StartedPrerequisitesPython 3.8 or higher.Familiarity with FastAPI and Pydantic.InstallationInstalling through pip:pipinstallopenbb-coreNote that, the openbb-core is an infrastructural component of the OpenBB Platform. It is not intended to be used as a standalone package.UsageThe Core extension is used as the basis for building and integrating new data sources, providers, and extensions into the OpenBB Platform. It provides the necessary classes and structures for standardizing and handling data.ContributingWe welcome contributions! If you're looking to contribute, please:Follow the existing coding standards and conventions.Write clear, documented code.Ensure your code does not negatively impact performance.Test your contributions thoroughly.Please refer to ourContributing Guidelines.CollaborationEngage with the development team and the community. Be open to feedback and collaborative discussions.SupportFor support, questions, or more information, please visitOpenBB Platform Documentation.LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details.
Package: openbb-devtools
The OpenBB DevTools ExtensionThis extension aggregates the dependencies that facilitate a nice development experience
for OpenBB. It does not contain any code itself, but rather pulls in the following dependencies:Linters (ruff, pylint, mypy)Code formatters (black)Code quality tools (bandit)Pre-commit hooks (pre-commit)CI/CD configuration (tox, pytest, pytest-cov)Jupyter kernel (ipykernel)... add your productivity booster here ...InstallationThe extension is included into the dev_install.py script.Standalone installation:pipinstallopenbb-devtools
Package: openbb-eia
OpenBB Platform v4 Cookiecutter TemplateIntroductionThis is the generated cookiecutter template for the OpenBB Platform v4.
It is used to help you create a new extension that can be integrated into the existing structure of the OpenBB Platform.With it you can:Create a new extensionBuild custom commandsInteract with the standardization frameworkBuild custom services and applications on top of the frameworkGetting StartedWe recommend you check out the files in the following order:eia/README.mdeia/models/example.pyeia/provider.pyeia/__init__.pyeia/router.py
Package: openbb-equity
OpenBB Equity ExtensionThis extension provides equity market data tools for the OpenBB Platform.Features of the Equity extension include:Access to various equity market data sourcesSub-modules such as:calendarfor equity-specific eventscomparefor peer analysisdarkpoolfor dark pool shorts datadiscoveryfor equity discoveryestimatesfor analyst estimatesfundamentalfor fundamental analysisoptionsfor optionsownershipfor internal and external ownershippricefor historical pricing datashortsfor shorts dataInstallationTo install the extension, run the following command in this folder:pipinstallopenbb-equityFor development please checkContribution Guidelines.Documentation availablehere.
Package: openbb-fd
Finance Database provider for OpenBBFinance Database by Jeroen Bouma offers an extensive collection of over 300,000 symbols across various financial instruments, including Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies, and Money Markets. It's designed to provide a comprehensive overview of sectors, industries, and types of investments.IntroductionThis extension integrates the Finance Database datasets into OpenBB. It enhances the capabilities of OpenBB’s equity, crypto, and ETF search functionalities. This integration adds capabilities to search through a broad spectrum of financial instruments.InstallationTo install this extension, you can use pip:pipinstallopenbb-fdAlternatively, for the latest version directly from the repository:pipinstallgit+https://github.com/piiq/openbb-fd.gitUsageHere are some command examples to demonstrate the usage of this integration:To search for equities in a defense industry:>>>sample=obb.equity.search(query="Defense",provider="fd")>>>print(f"Total companies in the sample: {len(sample.to_df()['name'].unique())}")Totalcompaniesinthesample:880To find ETFs matching certain criteria:>>>sample=obb.etf.search(query="Wheat",provider="fd")>>>print(f"Total etfs in the sample:{len(sample.to_df()['name'].unique())}")Totaletfsinthesample:10For cryptocurrency analysis:>>>sample=obb.crypto.search(query="node",provider="fd")>>>print(f"Total cryptos in the sample:{len(sample.to_df()['name'].unique())}")Totalcryptosinthesample:10LicenseThis project is licensed under the MIT License.
Package: openbb-orats
openbb-orats PackageIntroductionThe openbb-orats package is a specialized extension designed for the OpenBB Platform, focusing on integrating options data and analytics from ORATS (Options Research & Technology Services). This package serves as a bridge, allowing users to access and manipulate options data seamlessly within the OpenBB Platform environment.Key features include:Retrieving options data including pricing, Greeks, and implied volatilityPerforming advanced options analyticsAccessing historical options data and trendsCustomizable queries for specific data retrievalGetting StartedTo effectively utilize the openbb-orats package, familiarize yourself with its core components and functionalities:openbb_orats/router.py- Defines the routes for data retrieval and analytics commands.openbb_orats/provider.py- Manages data fetching from ORATS API.openbb_orats/models/- Contains models for structuring the options data.openbb_orats/__init__.py- Initializes the package, making it ready for use within the OpenBB Platform.
Package: openbb-quantitative
OpenBB QA ExtensionThis extension provides Quantitative Analysis (QA) tools for the OpenBB Platform.Features of the QA extension include various statistical tools and models.This extension works nicely with a companionopenbb-chartingextensionInstallationTo install the extension, run the following command in this folder:pipinstallopenbb-qaFor development please checkContribution Guidelines.Documentation availablehere.
Package: openbb-terminal-nightly
OpenBB SDK v3OpenBB is committed to build the future of investment research by focusing on an open source infrastructure accessible to everyone, everywhere.Check our website atopenbb.coOverviewOpenBB SDK provides a convenient way to access raw financial data from multiple data providers. This allows users to build custom financial dashboards and reports in minutes.More information on this product can be foundhereInstallationThe command below provides access to the core functionalities behind theOpenBB Terminal.pipinstallopenbb==3.2.4If you wish to utilize ourPortfolio OptimizationorMachine Learning / Artificial Intelligencetoolkits, please see instructionshere.UsageAccess our fully fledged financial SDK with a single line of python code.fromopenbb_terminal.sdkimportopenbbEverything you need to use the OpenBB SDK can be found on ourOfficial Documentation.Main uses cases can be seen below.RAW FINANCIAL DATA AT YOUR FINGERTIPSAccess raw financial data from any data source that you are interested. The open source nature of this SDK makes it so that this is an ever-growing project and that everyone can contribute to.GENERATE INSIGHTS 10X FASTERFor most of the functionalities, adding_chartto the function will allow you to visualize the output directly from a Jupyter Notebook. Not only speeding up the time it takes to create a plot for the data you are interested in, but making building custom reports much faster.
Package: openbci
OpenBCIHight level Python module for OpenBCI hardware.::: warningThis is NOT an official package fromOpenBCI team.This module is still unstable and not recommended for use in production.
:::What is?A Python module for interaction withOpenBCI boards.
Currently, we have support for Cyton (and Daisy) and their WiFi module.
All source code can be accessed from ourbitbucket repository.What do we want?We want a stable, high level, easy to use and extensible Python module for work
with OpenBCI, for students and researchers. We are developing a set of tools for
preprocessing, real-time data handling and streaming.Who are we?We are a research group focused on digital processing of signals and machine
learning from the National University of Colombia at Manizales (GCPDS).How works?An acquisition object can be instantiated from differents backend imlplementation:
Serial, WiFi and WebSockets.SynchronousFor more details refers toCyton data acquisition.fromopenbci.acquisitionimportCytonRFDuinoasDevice# from openbci.acquisition import CytonWiFi as Deviceopenbci=Device()openbci.collect(5)eeg_data=openbcib.eeg_buffer()AynchronousFor more details refers toWiFi data acquistion.fromopenbci.acquisitionimportCytonRFDuinoasDevice# from openbci.acquisition import CytonWiFi as Deviceopenbci=Device()openbci.start_collect()# some python stuffopenbci.stop_collect()eeg_data=openbcib.eeg_buffer()WebSocketsFor more details refers toWebSockets data acquistion.fromopenbci.stream.wsimportCytonWS_decorator@CytonWS_decorator()defeeg_with_ap(ws,eeg_data):""""""# some python stuffAPIContact:Main developer: Yeison Cardonayencardonaal@unal.edu.co,@yeisondev.
Package: openbd
openbdopenbdis a package for openBD(https://openbd.jp/).Installationpip install openbdExamplefromopenbdimportbook_infoprint(book_info('4764905809'))BookInfo(isbn='9784764905801', title='データ分析ライブラリーを用いた最適化モデルの作り方',
subtitle=None, series='Pythonによる 問題解決シリーズ', authors='斉藤 努／著、久保 幹雄／監修',
publish='近代科学社', date='2018/12/13', price='3200円', page='224', size='B5変形',
content='各種ライブラリを組み合わせることで、シンプルで分かりやすい最適化モデルの作成方法を学ぶ',
image='https://cover.openbd.jp/9784764905801.jpg', data=None)