Saravanan666/pypi-org huggingface dataset:
Package: blipleo
Language Experience Overview (LEO) Report GeneratorIn Singapore, more than 90% of adults are literate in at least 2 languages (Singapore Census, 2010).
Singaporean families often involve grandparents in the care of young children, and one in five families employ a live-in foreign worker for domestic help (Singapore Ministry of Manpower, 2020).
Infants in Singapore typically hear two or more languages from each parent, two-to-three languages from their grandparents (Woon, 2018), and possibly more from a domestic helper.
Given this complexity, existing models of balanced/unbalanced or dominant/non-dominant bilingualism are insufficient for describing the rich tapestry of multilingual experiences.
In order to capture this variety, we created a flexible multilingual tool, the Language Experience Overview (LEO).
This multivariate multilingual tool combines estimates of care time and language-use ratios with language profiles of each caregiver.
Importantly, the LEO Report visualises the results in parent-friendly feedback.blipleois a Python package to generate Language Experience Overview (LEO) report.Citation:
Woon, Fei Ting; Le, Tuan Anh; binte Amran, Shaza; Ang, Wen Xin; Styles, Suzy J, 2021, "Language Experiences Overview (LEO)",https://doi.org/10.21979/N9/XQUFEW, DR-NTU (Data), V1Installationblipleopackage is available on the Python official package index PyPI:https://pypi.org/project/blipleo/It can be installed on Python 3.6 or later using pippipinstallblipleoNotes:blipleorequiresInkscapeto generate PDF files, which can be downloaded freely athttps://inkscape.org/release/UsageTo generate a LEO report, prepare a LEO json data file (For sample see:data/baby_test.json)
and use the following commands:importblipleoleo=blipleo.read_json('./data/baby_test.json')blipleo.generate_leo(leo,'./data')The generated LEO report looks like this:https://github.com/BLIPNTU/LEO/blob/main/data/baby_test.pdfLicenseThe blipleo package is licensed under GPL version 3.0The LEO template graphic vector files under templates folder are licensed under CC-BY-NC 4.0Developersblipleois a free software, source code is available on Github:https://github.com/BLIPNTU/LEOLEO research archive:https://doi.org/10.21979/N9/XQUFEWMaintainer:Le Tuan AnhContactFor more information, please contact Fei Ting Woon atfeitingwoon@ntu.edu.sg
Package: blipnlptest
BlipNlpTestEssa é uma classe que permite o teste de mensagens em provedores integrados na plataforma, com o retorno do conteúdo cadastrado no Assistente de Conteudo.InstalaçãoPara instalar o pacote, basta executar o comando abaixo:pip install blipnlptestUsoApós a instalação do pacote, você terá acesso a classe que permitirá a execução do teste.Os parâmetros necessários são:data (opcional) : Dataframe de entradaresource (opcional) : Nome do recurso com o texto de teststhreshold (opcional) : float referente ao threshold do provedor com valores entre 0.1 (10%) a 1.0 (100%). (default: 0.5)key: chave do botcontract_id: id do contrato.Exemplo do código:Caso a análise seja de dados já analisados pelo provedor (envie um dataframe que tenha no mínimo as colunas Text, Intentions, Entities e Score), use:import blipnlptest as bnt

cc = bnt.contentchecker(key, data=df, contract_id="cliente_x")
cc.identityanalysis()Se a análise for feita com dados que não foram analisados (envie um dataframe que a coluna de texto tenha o nome Text), use:import blipnlptest as bnt

cc = bnt.contentchecker(key, data=df)
cc.sentences()OBS: A divisão foi feita para que os dados já rotulados não realizem outra análise no provedor.Caso queira analisar com dados que não estejam na base de dados, utilize o recurso do bot. O recurso deverá ser do tipo texto, e as mensagens separadas por vírgula. Use:import blipnlptest as bnt

cc = bnt.contentchecker(key, resource='nome_do_recurso')
cc.byresource()Em todos os casos, você pode colocar o valor do threshold personalizado, da seguinte forma:cc = bnt.contentchecker(key, data=df,threshold=0.6)
cc.sentences()Com os parâmetros previamente atribuídos, rodando o código acima você terá como saída a exibição do resultado com:A mensagem de entrada;A intenção reconhecida;As entidades reconhecidas;O score;A resposta entregue pelo Assistente de Conteudo;Se foi entregue (y/n);Ponto de atenção.Os pontos de atenção são sugestões de pontos para observar, eles tem os status abaixo:model = Se o score for baixo e não retornar conteúdo, necessário avaliar o aumento da confiança no modelo;refine = Se o score for baixo e existir uma resposta no Assistente de Conteudo, é necessário refinar e entender os próximos passos (aumentar a confiança ou ajustar os exemplos de alguma intenção);valid = Se o score for alto e retornou uma resposta, avaliar se a resposta está válida.content | entity in the combination = Se o score estiver alto, foi identificada entidade e não retornou uma resposta, é válido checar a falta da entidade na combinação;content | entity in the model = Se o score estiver alto, e não foi identificada alguma entidade, é válido checar a falta dela no modelo (que se criada, consequentemente irá impactar o conteúdo.LicençaEsse projeto está sob a licença MIT. Veja o arquivo LICENSE para mais detalhes.
Package: bliptv.reader
The goal of this project is to give developers an easy way to access
data stored on blip.tv, things like episodes, their enclosures and so on.
It is a wrapper around the blip.tv API and encapsulates the formatting
and parsing job.Contentsbliptv.reader Package ChangesDetailed DocumentationInstallationUsageEnclosuresDownloadbliptv.reader Package Changesbliptv.reader version 1.0 (2008/07/27)initial releaseDetailed DocumentationInstallationTo install it, the best way is to use easy_install:$ easy_install bliptv.readerThis will download the package and it’s dependencies from PyPI.
If you don’t have easy_install installed, you can download it here:http://peak.telecommunity.com/DevCenter/EasyInstallUsageFirst you need to import it:>>> from bliptv.reader import ShowNow you can select a show and initialize it. We take COM.lounge TV as an example
which can be found athttp://comlounge.blip.tv. The showname thus is “comlounge”:>>> show = Show('comlounge')We can now select the first page of episodes from it:>>> page1 = show.episodes.pages[1]We can also retrieve the next page like that:>>> page2 = page1.nextAnd go back to page1 with prev:>>> page1 = page2.prevUnfortunately blip.tv does not return the overall amount of episodes so that
we can only check next and prev if the return None like this:>>> page1.prev is None
TrueAlso note, that the list of episodes is backwards, starting with the most
recent episode.Now we can check the episodes in that page (we can only test existance here
because we don’t know the exact names of the episodes as these change all the time):>>> episode = page1[0]
>>> len(episode.title)>0
True

>>> episode.url.startswith("http://blip.tv/file")
TrueMoreover you can access:ratingThe rating of the show, e.g. TV-UNpureDescriptionA plain text version of the descriptiondescriptionThe HTML version of the descriptionkeywordsThe tags used as a listEnclosuresOne important part of course are the media files of an episode.
These are stored as a dictionary inside the enclosures attribute of an episode:>>> enclosures = episode.enclosuresThe dict is a filetype => Enclosure structure:>>> enclosure = enclosures['video/x-flv']
>>> enclosure.filetype
'video/x-flv'

>>> enclosure.url.startswith('http://blip.tv/file/get/')
True

>>> enclosure.filesize>0
True

>>> enclosure.height>0
True

>>> enclosure.width>0
TrueDownload
Package: bliss-audio
Python bindings forbliss-rs.Audio library used as a building block to make playlists from songs.Installationbliss-audio is available for Python 3.5+ via pip:$ pip install bliss-audioUsagefrombliss_audioimportSongimportnumpyasnpsong1=Song("/path/to/song1")print(f'Song "{song1.title}" has the following analysis:')forkey,valueinsorted(song1.analysis_dict.items()):print(f"{key}:{value}")song2=Song("/path/to/song2")distance=np.linalg.norm(np.array(song1.analysis)-np.array(song2.analysis))print(f'\nDistance between song1 and song2 is{distance}')Then you most likely want to analyze a bunch of songs like that, store the
result somewhere, and generate playlists on the fly by taking a song and
finding the next one by computing the one with the smallest distance to it.
Package: blissclient
blissclientA python client forblissterm, the high-level client is fully typed ready for auto-completion in any modern IDE.Getting StartedSet theBLISSTERM_URLexportBLISSTERM_URL=http://localhost:5000Then:fromblissclientimportBlissClientclient=BlissClient()omega=client.hardware.get("omega")print(omega.position)omega.move(100)omega.wait()Execute calls in the session:fromblissclientimportBlissClientclient=BlissClient()test_session=client.session("test_session")# Blocking until the call terminatesreturn_value=test_session.call("ascan","$omega",0,10,10,0.1,"$diode")Strings prefixed with$are translated to the relevant beacon objectsOr execute asynchronously:importtimefromblissclientimportBlissClientclient=BlissClient()test_session=client.session("test_session")call_id=test_session.call("ascan","$omega",0,10,10,0.1,"$diode",call_async=True)whileTrue:response=test_session.state(call_id=call_id)ifresponse.state=="terminated":breakelse:time.sleep(1)print(response.return_value)# The redis scan key, can be used with `blissdata``response.return_value["key"]See the test suite for more examples.
Package: bliss-deblender
Bayesian Light Source Separator (BLISS)IntroductionBLISS is a Bayesian method for deblending and cataloging light sources. BLISS providesAccurate estimationof parameters in blended field.Calibrated uncertaintiesthrough fitting an approximate Bayesian posterior.Scalabilityof Bayesian inference to entire astronomical surveys.BLISS uses state-of-the-art variational inference techniques includingAmortized inference, in which a neural network maps telescope images to an approximate Bayesian posterior on parameters of interest.Variational auto-encoders(VAEs) to fit a flexible model for galaxy morphology and deblend galaxies.Wake-sleep algorithmto jointly fit the approximate posterior and model parameters such as the PSF and the galaxy VAE.InstallationBLISS is pip installable with the following command:pipinstallbliss-deblenderand the required dependencies are listed in the[tool.poetry.dependencies]block of thepyproject.tomlfile.Installation (Developers)To use and installblissyou first need to installpoetry.Then, install thefftwlibrary (which is used bygalsim). With Ubuntu you can install it by runningsudoapt-getinstalllibfftw3-devInstall git-lfs if you haven't already installed it for another project:git-lfsinstallNow download the bliss repo and fetch some pre-trained models and test data from git-lfs:gitclonegit@github.com:prob-ml/bliss.gitTo create a poetry environment with theblissdependencies satisified, runcdbliss
poetryinstall
poetryshellVerify that bliss is installed correctly by running the tests both on your CPU (default) and on your GPU:pytest
pytest--gpuFinally, if you are planning to contribute code to this repository, consider installing our pre-commit hooks so that your code commits will be checked locally for compliance with our coding conventions:pre-commitinstallLatest updatesGalaxiesBLISS now includes a galaxy model based on a VAE that was trained on Galsim galaxies.BLISS now includes an algorithm for detecting, measuring, and deblending galaxies.StarsBLISS already includes the StarNet functionality from its predecessor repo:DeblendingStarFields.ReferencesMallory Wang, Ismael Mendoza, Cheng Wang, Camille Avestruz, and Jeffrey Regier.Statistical Inference for Coadded Astronomical Images.Machine Learning and the Physical Sciences workshop, NeurIPS 2022.arXiv:2211.09300Derek Hansen, Ismael Mendoza, Runjing Liu, Ziteng Pang, Zhe Zhao, Camille Avestruz, and Jeffrey Regier.Scalable Bayesian Inference for Detection and Deblending in Astronomical Images. ICML Workshop on Machine Learning for Astrophysics, 2022.arXiv:2207.05642Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, and The LSST Dark Energy Science Collaboration.Variational Inference for Deblending Crowded Starfields, 2021.arXiv:2102.02409
Package: blissful-basics
What is this?The tools I find myself copying and pasting between every python project.How do I use this?pip install blissful_basicsfromblissful_basicsimportprint,flatten,to_pure,stringify,stats,product,countdown,large_pickle_save,large_pickle_load,FS,Object## print helpers#if1:# settingsprint.indent.string="    "# recursively indents function calls@print.indent.functiondefmy_func(counter=5):ifcounter<=0:returnprint(f"function call{counter}start")my_func(counter-1)print(f"function call{counter}done")my_func()#    function call 5 start#        function call 4 start#            function call 3 start#                function call 2 start#                    function call 1 start#                    function call 1 done#                function call 2 done#            function call 3 done#        function call 4 done#    function call 5 done# simple indentwithprint.indent:print("howdy1")withprint.indent:print("howdy2")print("howdy3")#    howdy1#        howdy2#    howdy3# also indents stuff from the functionwithprint.indent.block("stuff"):print("hi")my_func()# stuff#     hi#        function call 5 start#            function call 4 start#                function call 3 start#                    function call 2 start#                        function call 1 start#                        function call 1 done#                    function call 2 done#                function call 3 done#            function call 4 done#        function call 5 done## to_pure()#if1:importnumpyimporttorchto_pure(numpy.array([1,2,3,4,5]))# [1,2,3,4,5]to_pure(torch.tensor([1,2,3,4,5]))# [1,2,3,4,5] # even if its on a GPU device## stats#if1:stats([1,2,3,4,5])# Object(#     max = 5,#     min = 1,#     range = 4,#     count = 5,#     sum = 15,#     average = 3.0,#     stdev = 1.5811388300841898,#     median = 3,#     q1 = 1.5,#     q3 = 4.5,#     normalized = (0.0, 0.25, 0.5, 0.75, 1.0),# )## plain object#if1:a=Object(thing=10)a.thing# 10a.thing=99a.thing# 99
Package: blissoda
blissodablissodaprovides utilities for online data analysis and automation inBLISS.blissodais mostly used by the beamline macro's for BLISS. In this case it needs to be installed in the BLISS environment.The actual data processing should be distributed byewoksjob. This project
should not contain any data processing code or have any scientific libraries as dependencies.Installpipinstallblissoda[id22,bm23,id31,streamline,server,test,...]submit: when workflows need to be submitted from this environment (remote execution)server: listen to Redis and execute workflows described inscan infoid22: XRD data reduction workflowsbm23: EXAFS plottingid31: XRD data reduction workflowsstreamline: XRD data reduction workflowstest: for testingMany modules ofblissodadepend onblissbut this is not added to the project requirements.Testpytest--pyargsblissoda.testsDocumentationhttps://blissoda.readthedocs.io/
Package: bliss-toolkit
Bayesian Light Source Separator (BLISS)IntroductionBLISS is a Bayesian method for deblending and cataloging light sources. BLISS providesAccurate estimationof parameters in blended field.Calibrated uncertaintiesthrough fitting an approximate Bayesian posterior.Scalabilityof Bayesian inference to entire astronomical surveys.BLISS uses state-of-the-art variational inference techniques includingAmortized inference, in which a neural network maps telescope images to an approximate Bayesian posterior on parameters of interest.Variational auto-encoders(VAEs) to fit a flexible model for galaxy morphology and deblend galaxies.Wake-sleep algorithmto jointly fit the approximate posterior and model parameters such as the PSF and the galaxy VAE.InstallationBLISS is pip installable with the following command:pipinstallbliss-toolkitand the required dependencies are listed in the[tool.poetry.dependencies]block of thepyproject.tomlfile.Installation (Developers)To use and installblissyou first need to installpoetry.Then, install thefftwlibrary (which is used bygalsim). With Ubuntu you can install it by runningsudoapt-getinstalllibfftw3-devInstall git-lfs if you haven't already installed it for another project:git-lfsinstallNow download the bliss repo and fetch some pre-trained models and test data from git-lfs:gitclonegit@github.com:prob-ml/bliss.gitTo create a poetry environment with theblissdependencies satisified, runcdbliss
poetryinstall
poetryshellVerify that bliss is installed correctly by running the tests both on your CPU (default) and on your GPU:pytest
pytest--gpuFinally, if you are planning to contribute code to this repository, consider installing our pre-commit hooks so that your code commits will be checked locally for compliance with our coding conventions:pre-commitinstallLatest updatesGalaxiesBLISS now includes a galaxy model based on a VAE that was trained on Galsim galaxies.BLISS now includes an algorithm for detecting, measuring, and deblending galaxies.StarsBLISS already includes the StarNet functionality from its predecessor repo:DeblendingStarFields.ReferencesMallory Wang, Ismael Mendoza, Cheng Wang, Camille Avestruz, and Jeffrey Regier.Statistical Inference for Coadded Astronomical Images.Machine Learning and the Physical Sciences workshop, NeurIPS 2022.arXiv:2211.09300Derek Hansen, Ismael Mendoza, Runjing Liu, Ziteng Pang, Zhe Zhao, Camille Avestruz, and Jeffrey Regier.Scalable Bayesian Inference for Detection and Deblending in Astronomical Images. ICML Workshop on Machine Learning for Astrophysics, 2022.arXiv:2207.05642Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, and The LSST Dark Energy Science Collaboration.Variational Inference for Deblending Crowded Starfields, 2021.arXiv:2102.02409
Package: blisswriter
Bliss WriterTheblisswriterproject provides NeXus-compliant HDF5 file writing based on
data published byblissdata. It can be used in three wayswriter service for a singleblisssession as aprocesscommand line interface:NexusSessionWriterconsumer of Redis data published byblissdatawriter service for a singleblisssession as aTango devicecommand line interface:NexusWriterServiceconsumer of Redis data published byblissdatawriter python APIpython API:from blisswriter.writer.main import ScanWriterWithStatesame API used by the writer servicesblisswriteris developed and maintained by theSoftware groupof theEuropean Synchrotron.
Package: blister-cpp
blisterConventions-over-configuration C++/modules project manager.This is a simple approach to deal with C++ modules. By using a bunch of
conventions, this Python module scans for all files and generates abuild.ninjato compile the code.Currently, it is personal endeavor to use C++ modules with some dependency
management, which (as of 2019) is not provided by existing building systems.
Hacks and non-sense may be included.Requires a folder structure like this:rootincludesh-files and subfolderssourcescommoncppm-files and subfoldersosx-metalcppm-filesmm-files...testsapp-folder-1cppm-filescpp-filesapp-folder-2...toolscpp-filesunit-testscpp-filesbli.yamlincludesis added as part of the include path.sources/commoncontains CPP modules (usingcppmas extension). They will
be scanned, with allcppmfiles added to the build. The module name is
independent of the file name, being extracted from theexport modulestatement.sources/osx-metalis a hack to build Apple-compatible folders. It also
compilesmetalfiles.testscreates one standalone application per folder. Every linked application
will contains all objects fromsourcesfolders.unit-testsare compiled independently and liked withsources/commonobjects
and run as part of thetesttarget.Dependencies are calculated from bothcppmandcppfiles, by parsing theimportstatements.bli.yamlidentifies the root folder and contains configuration to override
global and per-file flags.
Package: blitskrieg
BLITSkrieg: Bitcoin Lightning Integration Test ServiceBLITSkrieg is a gRPC server in Python that aims to provide a set of utilities
to test Bitcoin and Lightning Network services.It usesboltlightin order to hide
the differences between the different LN node implementations.Currently the available services are:bitcoind[1]boltlight[2]c-lightningeclairelectrs[3]electrumlndNotesdocker image always necessarydocker image necessary to call LN-related commandsdocker image necessary to runelectrumSystem dependenciesLinux or macOS (Windows may work, but is not supported)Python 3.9+poetrydockerBuildIn order to build the docker image for BLITSkrieg, run:# production mode$./unix_helperbuild# development mode$./unix_helperbuild-devProduction mode does not include the package dev dependencies.Pre-built (production mode) images for the latest versions can be downloaded
from GitLab'scontainer
registry.RunIn order to launch the docker image of BLITSkrieg, run:# production mode$./unix_helperrun# development mode$./unix_helperrun-devCLIBLITSkrieg features a Command Line Interface, namedbli. It can be installed
and called from the project directory.In order to install the project, run:$poetryinstallIn order to runbli, call it via poetry:$poetryrunbliblisupports shell completion for bash and zsh (the shell needs to have
completion support enabled). It can be enabled sourcing the appropriate file
for the running shell, from the project directory:$../blitskrieg/share/complete-bli.bash
$../blitskrieg/share/complete-bli.zshUseIn order to run a command on BLITSkrieg, you can directly call it by making
a gRPC call to it, or you can use its CLI.To get a list of available CLI commands, run:$poetryrunbli--helpIn order to perform any action in BLITSkrieg, you first need to create a LN
stack. To do so, call:$poetryrunblicreatestack[--<implementation>=<number_of_nodes>]If you do not specify any LN node, this will run a singlebitcoindinstance
and only Bitcoin-related commands will be available.Visit ourdocker repositoryin
order to build the required services.
Otherwise, if the needed docker images are not available, they will be pulled
from hashbeam's GitLab container registry.
Package: blitzactions
BlitzActionsis a framework to translate test prompts to function executionsInstallationpipinstallblitzactionsUsagefromosimportenvironfromdotenvimportload_dotenv,find_dotenvload_dotenv(find_dotenv())fromblitzactions.managerimportActionsManagerdata=[{"name":"Joshua","sales":100000,"profit":10000,"payment":1000,},{"name":"Rudra","sales":300000,"profit":30000,"payment":3000,},{"name":"Vishal","sales":200000,"profit":20000,"payment":2000,}]manager=ActionsManager(environ.get('OPENAI_API_KEY'))@manager.registerdefgenerate_employee_report(employee_name:str)->str:"""Generates report of employee with name passed in employee_names"""returnf'Employee name:{employee_name}\nSales:{data[0]["sales"]}\nProfit:{data[0]["profit"]}\nPayment:{data[0]["payment"]}'@manager.registerdefshow_employee_data()->str:"""Generates a report of all employees with all details in the table"""return'\n'.join([f'{d["name"]}|{d["sales"]}|{d["payment"]}'fordindata])@manager.registerdefexecute_payments_to_employees()->int:"""Processes payroll payments to all employees for this month"""foremployeeindata:print(f'Processing{employee["payment"]}USD payment for{employee["name"]}')result=manager.execute(input("Enter your prooompt: "))print(result)ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. /s