Saravanan666/pypi-org huggingface dataset:
Package: qa-analytics-insights
OverviewThis repository hosts the source code for theQA Analytics Insightsproject,
a robust command-line interface (CLI) tool designed to generate data-driven
insights from QA (Quality Assurance) test results. Test results are typically
generated in XML format by test automation frameworks such aspytestandunittest.FeaturesAnalyze test results in XML format.Generate visualizations and metrics to better understand test performance.Create actionable insights to improve QA processes.Command line toolThe command line tool can be used to generate insights from the tests results
in xml format.The tool can be used as follows:$ qa-analytics-insights --help
Usage: qa-analytics-insights -f <file> [-o <output_dir>] [-vv] [-h] [-v]The tool accepts the following arguments:-for–file: Path to the file containing the tests results in xml format.-oor–output: Path to the directory where the insights will be generated.-vvor–verbose: Enable verbose mode.-vor–version: Show version and exit.-hor–help: Show help message and exit.LibraryThe library can be used to generate insights from the tests result in xml
format.The library can be used as follows:from qa_analytics_insights.result_analyzer import ResultAnalyzer
analyzer = ResultAnalyzer(
    test_results_file_path='path/to/test/results/file.xml',
    output_dir='path/to/output/dir',
)InstallationTo install the package frompypi, run the following command:$ pip install qa-analytics-insightsDevelopmentTo install the package in development mode, run the following command:# create a virtual environment
$ virtualenv -p python3 venv

# activate the virtual environment
$ source venv/bin/activate

# Run the package in development mode
$ hatch run develop:allFor linting, run the following command:$ hatch run linter:linterfor building the package, run the following command:$ hatch buildfor generating the documentation, run the following command:$ hatch run docs:allfor running the tests with coverage, run the following command:$ hatch run default:all
Package: qa-annotator
QA-Annotator: Annotation Is All You NeedAbout The ProjectMaking the mammoth task of data annotation for an NLP-based Question-Answering system hassle-free. Just jump right in and start annotating!Getting StartedInstallationInstall with pip (python 3 required)$pipinstallQA-AnnotatorStart the server$annotatorContributingContributions, suggestions are welcomed! :)If you like the project, Don't forget to give a star! Thanks!LicenseDistributed under the MIT License. SeeLICENSE.txtfor more information.ContactPrateek Yadav -@impyadav_LinksProject Link:https://pypi.org/project/QA-Annotator/
Package: qabal
Qabal is a simple and fast open source content-based message broker. Use
Qabal to organize all your multi-stage analytical workloads.Qabal has no install dependencies outside of the default Python runtime.
Use it on your Raspberry Pi, laptop, or 100 node Dask cluster. Routing
decisions in Qabal take constant time, so you can use it for complex
multistage analytics with thousands of steps. It’s lightweight, simple
to understand, easy to integrate with distributed task libraries (eg.
Dask) and fast to execute. It has configurable full pipeline data
provenance. In extended provenance mode, Qabal records when every data
item is added or updated by what analytic and at what time.Qabal analytics need not be aware that they are part of a message broker
pipeline. They don’t need to depend or import any Qabal library. The
data structure used in routing extends the standard Python dictionary.
Qabal comes with ready to use reflection-based content injection, giving
users flexibility on the API of their analytics.Installationpip install qabalTry It For YourselfQabal has a simple API that revolves around attaching functions to a
Session object.You define routes in Qabal using a query DSL that resembles Python
conditional statements. These get “compiled” into more traditional
message broker routes.fromqabalimportSession,Itemdeffoo(item):item['foo']='foo'returnitemdefbar(item):item['bar']='bar'returnitemdefbaz(item):item['baz']='baz'returnitemdefbar_baz(item):item['bar']='bar!'item['baz']='baz!'returnitemdefparameters_are_okay_too(foo,bar):return{'baz':foo+bar}# If your trigger query doesn't use the Qabal query language, an analytic need not even depend on Qabal.classAnalyticWithMetadata:__trigger__=Item['baz']=='baz!'__inject__=True__creates__='qux'def__call__(self,baz):returnbaz+'?'example=AnalyticWithMetadata()# Despite all the Qabal metadata, this analytic still maintains a normal API.print(example('hello'))# Outputs: 'hello?'sess=Session(provenance='extended')# Qabal records a ton of information in this mode.sess.add(foo,Item['type']=='foo')sess.add(bar,Item['foo']=='foo')res=sess.feed({'type':'foo'})print(res)print(res.provenance)# The session is mutable at any time.handle=sess.add(bar_baz,Item['bar']=='bar')res=sess.feed({'type':'foo'})sess.remove(handle)print(res)sess.add(bar_baz,Item['bar']=='bar')# Analytics with metadata don't need a routesess.add(example)res=sess.feed({'type':'foo'})print(res)
Package: qablet-contracts
qablet_contractsQablet Contracts documentation is atqablet.github.io/qablet-contracts.A Qablet timetable defines a financial product using a sequence of payments, choices and conditions. A valuation model implemented with a Qablet parser can value any contract, as long as the contract can be described using a Qablet Timetable.This repositary contains code to create qablet timetables.
It does not contain models that price qablet timetables. Such models will be available in other independent projects.Install it from PyPIpipinstallqablet_contracts
Package: qaboard
Experiment tracking framework with advanced viewers.Helps algo/ml/perf engineers share results, collaborate, and build better products.FeaturesOrganize, View and Compare Results,Tuning/OptimizationWeb-based,with sharable URLs.Visualizations:support for quantitative metrics, and many file formats: advanced image viewer, support for videos, plotly graphs, flame graphs, text, pointclouds, HTML...Integrations:direct access from Git and CI tools, easily exportable results, API, links to the code, trigger gitlabCI/jenkins/webhooks...Agnosticto your language/framework: run your existing code, write files, view them.For screenshots checkour website.BenefitsQA-Board across many projects enables us to:Scale R&D:enable engineers to achieve more and be more productive.Faster Time-to-Market:collaboration across teams, workflow integration..Quality:uncover issues earlier, KPIs, tuning, reporting...Getting StartedRead the docs!You will learn how to:Start a QA-Board serverWrap your code with QA-BoardView output files and KPIs...and setup parameter tuning, integrations with 3rd party tools, etc.If you want to learn about the code's organization, or how to contribute, readCONTRIBUTING.mdFeedback? Questions? Need Help? Found a bug?Don't hesitate to get in touch! Contactarthur.flam@samsung.com, we'll be delighted to hear your insights.If you've got questions about setup, deploying, want to develop new features, or just want to chat with the developers, please feel free tostart a thread in our Spectrum community!Found a bug with QA-Board? Go ahead andsubmit an issue. And, of course, feel free to submit pull requests with bug fixes or changes to themasterbranch.ContributorsQA-Board was started atSamsung SIRCbyArthur Flam.Thanks to the following people for their contributions, testing, feedback or bug reports: Amir Fruchtman, Avi Schori, Yochay Doutsh, Itamar Persi, Amichay Amitay, Lena Grechikhin, Shai Shamir, Matan Danino, Roy Shaul, Gal Hai, Rivka Emanuel, Ela Shahar, Nadav Ofer, David Nukrai. Thanks also to Sebastien Derhy, Elad Rozin, Nathan Levy, Shahaf Duenyas, Yotam Ater, Asaf Jazcilevich and Yoel Yaffe for supporting the project.You don't see your name? Get in touch to be added to the list!CreditsThe logo is a the Poodletwemoji🐩, recolored in Samsung Blue 🔵.Copyright 2019 Twitter, Inc and other contributors. Code licensed under theMIT License. Graphics licensed underCC-BY 4.0
Package: qacode
BranchLinux DeployWindows DeployCircleCI - DockerCodeClimatemasterPython tested versions3.73.63.5<=3.4SupportedSupportedSupportedNot SupportedHow to install ?Install from PIP :pip install qacodeInstall from setup.py file :python setup.py installDocumentationHow to use library, searching forUsage Guideor auto updatedQAcode’s Documentation.https://netzulo.github.io/How to exec tests ?Tests from setup.py file :python setup.py testInstall from PIP file :pip install toxTests from tox :tox-l&& tox-eTOX_ENV_NAME(see tox.ini file to get environment names)TOX Env nameEnv descriptionpy35,py36,py37Python supported versionsflake8Exec linter in qalab/ tests/coverageGenerate XML and HTML reportsdocsGenerate doc HTML in /docsConfiguration File{
  "bot": {
    "log_output_file": "logs/",
    "log_name": "qacode",
    "log_level": "DEBUG",
    "mode": "remote",
    "browser": "chrome",
    "options": { "headless": false },
    "url_hub": "http://localhost:11000/wd/hub",
    "drivers_path": "../qadrivers",
    "drivers_names": [
      "chromedriver_32.exe",
      "chromedriver_64.exe",
      "chromedriver_32",
      "chromedriver_64",
      "firefoxdriver_32.exe",
      "firefoxdriver_64.exe",
      "firefoxdriver_64.exe",
      "firefoxdriver_32",
      "phantomjsdriver_32.exe",
      "phantomjsdriver_64.exe",
      "phantomjsdriver_32",
      "phantomjsdriver_64",
      "iexplorerdriver_32.exe",
      "iexplorerdriver_64.exe",
      "edgedriver_32.exe",
      "edgedriver_64.exe"
    ]
  }
}Getting StartedJust starting example of usage before readUsage Guide(or refer to `QAcode’s Documentation`_).fromqacode.core.bots.bot_baseimportBotBasefromqacode.core.webs.controls.control_baseimportControlBasefromqacode.utilsimportsettingsSETTINGS=settings(file_path="/home/user/config/dir/",file_name="settings.json")try:bot=BotBase(**SETTINGS)bot.navigation.get_url("http://the-internet.herokuapp.com/login")ctl_config={"selector":"input[name='username']"}ctl=ControlBase(bot,**ctl_config)# ENDimportpdb;pdb.set_trace()# TODO, remove DEBUG laneprint(ctl)exceptExceptionaserr:print("ERROR:{}".format(err))finally:bot.close()
Package: qad-api
The QAD-API Python libraryThe QAD-API Python library is a library for accessing the (REST) API ofQAD Cloud.NoteAs this library serves simply as a Python front-end to the QAD Cloud,
you will first need an account on this platform, before using the library.InstallationWe do not yet provide a PyPi package. The recommended method for installing
QAD-API is to clone the following repository, and then "pip install" it
from the appropriate local folder:gitclonehttps://github.com/HQSquantumsimulations/qad-api.git
pipinstall-eqad-apiUsageThe QAD-API is utilized by importing the classQAD_APIfrom the root packageqad_api. In order access the API, one must create an instance of this class.To learn more about the API functionality, please refer to thedocumentation of QAD_API.ExampleTo get started with the QAD_API, we provide a quick and simple example here.
You also find this example in the folderexamples/lattice.We will create an instance ofQAD_API, which will authenticate the user
with the back-end. The first time this is done, the user will be asked
to open a link in a browser and use their credentials to authenticate
with the back-end (OAuth2).After this, we use the instance ofQAD_APIto access the API functionality.
We create a unit-cell and a system for the lattice-based problem solver "SCCE,"
and also create a job for that solver by passing the recently created handlers,
then wait for the job to finish. This will take some time, after which we
download the results file to the local file system.fromqad_apiimportQAD_API# Creating an QAD_API instance will authenticate the user with the backendqad=QAD_API()# Create a unit-cellunit_cell=qad.lattice.unit_cells.create('1D XXZ',{"unitcell":{"atoms":[#                     eps    U['0','A',[0,0,0],0.0001,0.0],['1','B',[0.5,0,0],-0.0001,0.0]],"bonds":[#                 t    U['0','0',[1,0,0],-1.0,0.0]],"lattice_vectors":[[1,0,0]]}})print(f"Unit cell created:{unit_cell.id}")# Create a systemsystem=qad.lattice.systems.create('1D XXZ',{"system":{"cluster_size":[14,1,1],# measured in unit cells"system_size":[2,1,1],# measured in clusters"cluster_offset":[0,0,0],# measured in clusters"system_boundary_condition":"periodic"}})print(f"System created:{system.id}")# Create a job (will start to run it automatically)job=qad.lattice.scce.jobs.create('1D XXZ',unit_cell,system)print(f"Job created:{job.id}")# Wait for the job to be done (when using co-routines: await job.wait())job.wait_blocking()# Download the result to a local filejob.download_result(f"./{job.id}.h5")print("Downloaded result")
Package: qadence-libs
Qadence-LibsQadence-Libsis a Python package that provides extra functionality for Qadence.Installation guidePyPIand can be installed usingpipas follows:pipinstallqadence_libsContributingBefore making a contribution, please review ourcode of conduct.Submitting Issues:To submit bug reports or feature requests, please use ourissue tracker.Developing in qadence:To learn more about how to develop withinqadence, please refer tocontributing guidelines.Setting up qadence in development modeWe recommend to use thehatchenvironment manager to installqadence_libsfrom source:python-mpipinstallhatch# get into a shell with all the dependenciespython-mhatchshell# run a command within the virtual environment with all the dependenciespython-mhatchrunpythonmy_script.pyWARNINGhatchwill not combine nicely with other environment managers such as Conda. If you still want to use Conda,
install it from source usingpip:# within the Conda environmentpython-mpipinstall-e.CitationIf you use Qadence-Libs for a publication, we kindly ask you to cite our work using the following BibTex entry:@misc{qadence-libs2024pasqal,
  url ={https://github.com/pasqal-io/qadence-libs},
  title ={Qadence Libs:{A}n{E}xperiment runner for Qadence.},
  year ={2023}}LicenseQadence-Libs is a free and open source software package, released under the Apache License, Version 2.0.
Package: qadence-protocols
Qadence-ProtocolsQadence-Protocolsis a Python package that provides extra functionality for Qadence.Installation guidePyPIand can be installed usingpipas follows:pipinstallqadence_protocolsContributingBefore making a contribution, please review ourcode of conduct.Submitting Issues:To submit bug reports or feature requests, please use ourissue tracker.Developing in qadence:To learn more about how to develop withinqadence, please refer tocontributing guidelines.Setting up qadence in development modeWe recommend to use thehatchenvironment manager to installqadence_protocolsfrom source:python-mpipinstallhatch# get into a shell with all the dependenciespython-mhatchshell# run a command within the virtual environment with all the dependenciespython-mhatchrunpythonmy_script.pyWARNINGhatchwill not combine nicely with other environment managers such as Conda. If you still want to use Conda,
install it from source usingpip:# within the Conda environmentpython-mpipinstall-e.CitationIf you use Qadence-Protocols for a publication, we kindly ask you to cite our work using the following BibTex entry:@misc{qadence-protocols2024pasqal,
  url ={https://github.com/pasqal-io/qadence-protocols},
  title ={Qadence Protocols:{A}n{E}xperiment runner for Qadence.},
  year ={2023}}LicenseQadence-Protocols is a free and open source software package, released under the Apache License, Version 2.0.
Package: qa-engine
QA engineOMBot项目的QA引擎，提供项目问答功能使用方法1. 触发闲聊模式fromqa_engineimportQAEnginefromombot_utils.schemasimportChatRecordsif__name__=='__main__':qa=QAEngine()records={"bot_id":"2","session_id":"string","callback":"string","messages":[{"role":"user","message_type":"image","src_type":"url","content":"https://minio/hzlh/omintel/cess/c25f30559b974c728e5dbe1d4177aa5a.jpg","objects":[]},{"role":"user","message_type":"text","src_type":"text","content":"你好，请问你是谁？","objects":[]}]}records=ChatRecords(**records)qa.run(bot_id="2",chat_records=records)1. 触发专业模式fromqa_engineimportQAEnginefromombot_utils.schemasimportChatRecordsif__name__=='__main__':qa=QAEngine()records={"bot_id":"2","session_id":"string","callback":"string","messages":[{"role":"user","message_type":"image","src_type":"url","content":"https://minio/hzlh/omintel/cess/c25f30559b974c728e5dbe1d4177aa5a.jpg","objects":[]},{"role":"user","message_type":"text","src_type":"text","content":"视频中有人摔倒吗？","objects":[]}]}records=ChatRecords(**records)qa.run(bot_id="2",chat_records=records)
Package: qafacteval
QAFactEval: Improved QA-Based Factual Consistency Evaluation for SummarizationThis is the official code repository for the NAACL 2022 paperQAFactEval: Improved QA-Based Factual Consistency Evaluation for SummarizationbyAlexander R. Fabbri,Chien-Sheng Wu,Wenhao Liu, andCaiming Xiong.In our paper, we conduct an extensive comparison of the components of QA-based metrics for factual consistency evaluation in summarization. Our optimized metric builds onQAEvalwith question consistency filtering and an improved answer overlap metric, leading to a 14% average improvement over previous QA-based metrics on theSummaCfactual consistency benchmark.Table of ContentsUpdatesUsing QAFactEvalCitationLicenseUpdates5/2/2022- Initial commit! :)Using QAFactEvalYou can install qafacteval via pip:pipinstallqafactevalYou can also install from source:gitclonehttps://github.com/salesforce/QAFactEvalcdQAFactEval
pipinstall-e.For use in scriptsDownload the required pretrained models usingdownload_models.sh.Seerun.pyfor an example of using the QAFactEval metric:fromqafactevalimportQAFactEvalkwargs={"cuda_device":0,"use_lerc_quip":True,\"verbose":True,"generation_batch_size":32,\"answering_batch_size":32,"lerc_batch_size":8}model_folder=""# path to models downloaded with download_models.shmetric=QAFactEval(lerc_quip_path=f"{model_folder}/quip-512-mocha",generation_model_path=f"{model_folder}/generation/model.tar.gz",answering_model_dir=f"{model_folder}/answering",lerc_model_path=f"{model_folder}/lerc/model.tar.gz",lerc_pretrained_model_path=f"{model_folder}/lerc/pretraining.tar.gz",**kwargs)results=metric.score_batch(["This is a source document"],[["This is a summary."]],return_qa_pairs=True)score=results[0][0]['qa-eval']['lerc_quip']CitationWhen referencing this repository, please citethis paper:@misc{fabbri-etal-2022-qafacteval,title={QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization},author={Alexander R. Fabbri and Chien-Sheng Wu and Wenhao Liu and Caiming Xiong},year={2022},eprint={2112.08542},archivePrefix={arXiv},primaryClass={cs.CL},url={https://arxiv.org/abs/2112.08542},}LicenseThis repository is released under theBSD-3 License.
Package: qaffeine
qaffeineLittle tool that prevents your computer from entering inactivity modes. Can run in a terminal or in the notification area. Written in Python 3 and Qt 5.Compatible with Linux, OS/X and Windows.RequirementsPython 3PySide2pyautoguiInstallationUsing PIP#pip3 install qaffeineThis will pull the dependencies automatically.Using the setup.py supplied in the source tree#python3 setup.py installUsageCommand lineSyntax:$ qaffeine-cli -h
usage: Prevent computer inactivity by simulating key presses
       [-h] [-n] [-d DELAY] [-k KEY] [-v]

optional arguments:
  -h, --help            show this help message and exit
  -n, --nogui           Don't start a GUI, only a operate in text mode
  -d DELAY, --delay DELAY
                        Delay between key presses in seconds [default: 5] -
                        only valid with --nogui
  -k KEY, --key KEY     Key to press [default: altright]; see keys.txt for a
                        list of valid values - only valid with --nogui
  -v, --version         Show version number and exitGUIRunning qaffeine without any argument starts the graphical interface. Qaffeine then runs in the notification area.
Package: qafs
Quality Aware Feature StoreSimple and scalable feature store with data quality checks.feature store aim to solve the data management problems when building Machine Learning applications. However the data quality is a component which data teams need integrate and handle as separated component. This project join both concepts keeping the data quality closely coupled with data transformations making necessary a minimal data verification check and possibiliting the data/transformations check evolve during the projects.For thatqafshave a strong dependecy withpanderato build the data validations.FeaturesPandas-like APIFeatures information stored in database along with metadata.Dask to process large datasets in a cluster enviroment.Data is stored as timeseries inParquet format, store in filesystem or object storage services.Store transformations as feature.Get StartedInstalling the python package through pip:$pipinstallqafsBellow is an example of usageqafswhere we'll create a feature store and registernumbersfeature and ansquaredfeature transformation. First we need import the packages and create the feature store, for this example we are using sqlite database and persisting the features in the filesystem:importqafsimportpandasaspdimportpanderaaspafrompanderaimportCheck,Column,DataFrameSchemafrompanderaimportiofs=qafs.FeatureStore(connection_string='sqlite:///test.sqlite',url='/tmp/featurestore/example')Features could be stored in namespaces, it help organize the data. When creatingnumberswe specify the'example/numbers'feature to point the featurenumbersat that namespaceexamplehowever we can use the argumentsname='numbers', namespace='example'as well. The we specify the data validation usingpanderatelling that feature isIntegerand the values should begreater than 0:fs.create_namespace('example',description='Example datasets')fs.create_feature('example/numbers',description='Timeseries of numbers',check=Column(pa.Int,Check.greater_than(0)))dts=pd.date_range('2020-01-01','2021-02-09')df=pd.DataFrame({'time':dts,'numbers':list(range(1,len(dts)+1))})fs.save_dataframe(df,name='numbers',namespace='example')To register oursquaredtransformation feature we're using the annotationfs.transformand fetching the data from thenumbersfeature applying the same data validation fromnumbers:@fs.transform('example/squared',from_features=['example/numbers'],check=Column(pa.Int,Check.greater_than(0)))defsquared(df):returndf**2When fetch our features we should see:df_query=fs.load_dataframe(['example/numbers','example/squared'],from_date='2021-01-01',to_date='2021-01-31')print(df_query.tail(1))##----#             example/numbers  example/squared# time# 2021-01-31              397           157609##----ContributingPlease follow theContributingguide.LicenseGPL-3.0 LicenseThis project started using the as basebytehub feature storeand is under the same license.
Package: qafunnypet
qafunnypetInstall qafunnypet from PyPi.pipinstallqafunnypetExamplefromqafunnypetimportQafunnypetclassDog(Qafunnypet):def__init__(self,name,age,color=None):super().__init__(name,age)self.color=colordefpresent(self):ifself.colorisNone:passelse:print(f"I am{self.name}and I am{self.age}years old and I am{self.color}")defspeak(self):print("Bark")>>>doggy=Dog("Tim",15,"Bronw")>>>doggy.present()>>>doggy.speak()>>>some=Qafunnypet("Cuasi",10)>>>some.present()>>>some.speak()