Saravanan666/pypi-org huggingface dataset:
Package: qa-genie
QA GenieEnglish |हिंदीQA Genie is a Python package designed for generating questions and answers from unstructured data.This package is built using the unofficial API of HuggingChat:hugchat. It leverages HuggingChat's capabilities for question and answer generation.NoteThis package is in its alpha release and more functionality will be added soon!Update 1.0.0a3:This update enables the user to adjust iteration time. (Solves #1)Update 1.0.0a4:Enables user to get raw text output from chatbot for manual cleaning. Also fixes bugs in cleaning.Installationpipinstallqa_genieorpip3installqa_genieUsageemail="your_email@example.com"# huggingface account emailpassword="your_password"# huggingface account passwordmodel="meta"# use "meta" to use meta-llama/Llama-2-70b-chat-hf or "oasst" to use OpenAssistant/oasst-sft-6-llama-30b# Initialize chatbotchatbot=get_generator(email,password,model)# Example usage with a single texttext="Lorem ipsum dolor sit amet, consectetur adipiscing elit."result_single=extract_qa(chatbot,text,num_qn=3)# returns pandas.DataFrame with num_qn questions and answers# Example usage with multiple textstexts=["Text 1","Text 2","Text 3"]result_multiple=extract_qas(chatbot,texts,num_qn_each=3)# return pandas.DataFrame with num_qn_each questions and answers generated for each textImportant NoteAs mentioned bySoulter, Server resources are precious, it is not recommended to request this API in a high frequency.ContributingFeel free to contribute to QA Genie by creating issues, submitting pull requests, or suggesting improvements. Your contributions are highly appreciated :)
Package: qa-helper
dokr - Make your docker and ecs tasks easyA Helper pip package for docker and ECS tasks. This pip package helps you automate your CI/CD pipeline. If your using docker and Amazon ECS for deployments, this tool can be really helpful. This package uses aws cli and ecs cli. MakAssumptions:Assuming python is installed on your system.Docker is installed on your systemaws-cliis installed and credentials are configured on your system.ecs-cliis installed on system [For Log Command only]Installdokron your system using :pip install dokrECS Optionslogin into ecs directly (Assuming awscli is installed and configured)dokr ecs loginDeploy an image on a clusterdokr ecs deploy --cluster cluster_name --service service_name --tag image_versionCheck ecs running logs of a Task -  this command will ask for cluster/service and task defination.Note:Install ecs-cli before running this command  from here:https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ECS_CLI_installation.htmldokr ecs logDocker Helper CommandsPrune whole system -  Cleans unused images, containers and volumes.dokr dock --clean-allDelete all the images matching the patterndokr dock --clean pattern_xxxAdd a tag to the existing image matching the provided pattern  (for latest tag only)dokr dock --tag pattern_xxx tag_namePush all images on a system matching a patternThis will push all images matching pattern 'pat'dokr dock --push patAWS CommandsCheck current public ip of a machine on AWSdokr aws --ip jenkinsRun Apps (subsitute todocker runcommand and DockerCompose)Configure your default values(like docker registry, port mapping, volume mapping etc. that will be same for all apps):Add an new app for deployment:Run all configured apps:Run a particular  app from ECR tags:Run a particular  app by providing a tag:Development:Clean ununsed:rm -rf build/ dist/ *egg* **.pyc __pycache__Build package:python setup.py bdist_wheeldeploy package:python -m twine upload dist/*follow this link for more detailshttps://dzone.com/articles/executable-package-pip-install
Package: qai-hub
Qualcomm® AI Hubsimplifies deploying AI models
for vision, audio, and speech applications to edge devices.helps to optimize, validate,
and deploy machine learning models on-device for vision, audio, and speech use
cases.With Qualcomm® AI Model Hub, you can:Convert trained models from frameworks like PyTorch for optimized on-device performance on Qualcomm® devices.Profile models on-device to obtain detailed metrics including runtime, load time, and compute unit utilization.Verify numerical correctness by performing on-device inference.Easily deploy models using Qualcomm® AI Engine Direct or TensorFlow Lite.qai_hubis a python package that provides an API for users to upload a
model, submit the profile jobs for hardware and get key metrics to optimize the
machine learning model further.Installation with PyPIThe easiest way to installqai_hubis by using pip, runningpip install qai-hubFor more information, check out thedocumentation.LicenseCopyright (c) 2023, Qualcomm Technologies Inc. All rights reserved.
Package: qalaboratory
Branch nameQAlabQAcodeQAdminQAdocQAtestlinkmasterHow to install ?Clone this repo:git clonehttps://github.com/netzulo/qalab.gitEnter on repo directory:cd qalabClone submodules:git submodule update--init--recursiveAttach branches HEAD:git submodule foreach git checkout masterInstall qalab package: from PIPpip install qalaboratoryor from setup.py filepython setup.py installCommand Usageusage: qaenv.py [-h] [-v] [-sd SERVER_DRIVER] [-m MODE] [-i] [-s]
                [-p PLATFORM] [-dcp DRIVER_CONFIG_PATH]

Performs selenium drivers operations

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         verbose level... repeat up to three times.
  -sd SERVER_DRIVER, --server_driver SERVER_DRIVER
                        Select server driver, values are:
                        [selenium,appium,selendroid]
  -m MODE, --mode MODE  Select mode, values are: [hub, node]
  -i, --install         Download driver server jar
  -s, --start           Start driver server jar
  -p PLATFORM, --platform PLATFORM
                        Select mode, values are: [lin32,lin64,win32,win64]
  -dcp DRIVER_CONFIG_PATH, --driver_config_path DRIVER_CONFIG_PATH
                        Use different absolute PATH+FILE_NAME to read
                        DRIVER_CONFIG

----- help us on , https://github.com/netzulo/qalab -------How to create HUB + Node ?HubCreate configuration :python qalab/qaenv.py--server_driverselenium--modehub--installStart Hub :python qalab/qaenv.py--server_driverselenium--modehub--startNodeCreate configuration :python qalab/qaenv.py--server_driverselenium--modenode--installStart Node :python qalab/qaenv.py selenium--server_driverselenium--modenode--start--platformwin64AppiumMust be installed SDK and appium (from NPM) as global packageInstall SDKInstall appium:npm install-gappiumCreate configuration :python qalab/qaenv.py--server_driverappium--modenode--installStart Node :python qalab/qaenv.py--server_driverappium--modenode--start--platformwin64Env nameEnv descriptionpy27,py34,py35,py36Python supported versionsdocsGenerate doc HTML in /docsflake8Exec linter in qalab/ tests/selenium-hubStart intalled selenium hubselenium-nodeStart intalled selenium nodeselendroid-hubStart intalled selendroid hubselendroid-nodeStart intalled selendroid nodeappium-nodeStart intalled appium nodeHow 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 descriptionpy27,py34,py35,py36Python supported versionsflake8Exec linter in qalab/ tests/coverageGenerate XML and HTML reportsdocsGenerate doc HTML in /docsselenium-hubStart intalled selenium hubselenium-nodeStart intalled selenium nodeselendroid-hubStart intalled selendroid hubselendroid-nodeStart intalled selendroid nodeappium-nodeStart intalled appium nodeselenium-testsExecute Hub+Node testsQADriversDriversLinux 32Linux 64Windows 32Windows 64ChromeOKOKOKOKFirefoxOKOKOKOKPhantomJsOKOKOKOKInternet ExplorerOKOKEdgeOKOKAndroidOKOKOKOK
Package: qalgebra
QAlgebraPython package for symbolic quantum algebra.Development of QAlgebra happens onGithub. You can read the full documentationonline.InstallationTo install the latest released version of QAlgebra, run this command in your terminal:pip install qalgebraThis is the preferred method to install QAlgebra, as it will always install the most recent stable release.If you don't havepipinstalled, thePython installation guide, respectively thePython Packaging User Guidecan guide you through the process.To install the latest development version of QAlgebra fromGithub.pip install git+https://github.com/qalgebra/qalgebra.git@master#egg=qalgebraUsageTo use QAlgebra in a project:import qalgebraHistory0.2.0 (2020-12-30)Initial alpha release0.1.0 (2018-12-06)Placeholder release
Package: qal-swflint
Query Academic LibraryThis application is built to support automatically searching a number of academic digital libraries.  In particular, it currently supports the following:IEEE XploreSpringerLinkScienceDirectToolsqal-queryThis tool can be used to try queries out on different digital libraries, as well as discover information about the capabilities and query options of each individual library.  Options of note include:-Lor--list-libraries: This option will list all libraries known to the system and their abbreviations.-dor--describe: When coupled with-l, describe a library, showing the query option names available and their descriptions.-lor--library: Select a library to query or describe.-ror--results: Select where to store results.-qor--query: Multiple allowed, should be of the formquery-option-name=value.qal-autoThis tool is used to automatically run several queries against supported digital libraries.  There are several options, including:-por--plan-file: The location of the plan file.  Format described below.-sor--status-file: Where to store query status (allows for picking back up if interrupted).-oor--output-file: Where to store the results data.-bor--number-batches: How many batches to run (how many times through one page of each query/provider pair).-vor--verbose: Can show multiple times, more times is more verbose.Plan FilesThe plan file is a JSON-formatted dictionary, with at least the two following keys.sites: an array of dictionaries.  Each dictionary contains minimum aname, and should contain akey, and can also containstartandpage_sizekeys (integer values), and an optionaloptionskey with a dictionary of options.  See documentation for particular APIs.queries: an array of dictionaries.  These dictionaries map query options (symbolic names, seeqal-query --describe -l libraryfor options) to string values.Obtaining API KeysConfer with your institution & institutional library before doing so, however, it's fairly easy to obtain keys.For IEEE Xplore:register here.  Place the API Key inIEEE_XPLORE_API_KEY.For SpringerLink:register here.  Place the API Key inSPRINGER_LINK_API_KEY.For ScienceDirect:register here.  Place the API Key inSCIENCE_DIRECT_API_KEY.Notes on Particular BackendsSpringerLinkThis must be accessed from your institutional IP address allocations.  Springer uses the requester IP as part of the authentication process.  Currently, their API does not give JSON-formatted responses on error: in this case, execution is stopped.ScienceDirectThis must also be accessed from your institutional IP address allocations.  Elsevier will limit results based on your institutions subscriptions (as far as I can tell, at least).  They handle quite well reporting of errors.  If you ever get an error message that is not handled, please make an issue.
Package: qalx-orcaflex
qalx-orcaflexTools to help you run OrcaFlex onqalx.To use this package you'll need aqalxAPI key.
This can be obtained by registering atqalx.net.FeaturesThe current features are:Batches: build batches of OrcaFlex data files from various sourcesResults: attach some required results which will be extracted automatically when the simulation is complete. The results will also be summarised for each batchLoad case information: results in a batch can be linked to information about the load caseModel views: define a set of model views that will be automatically captured at the end of the simulationSmart statics: allows you to add object tags that will be used to iteratively find a model that solves in staticsSome planned features:Custom results specificationLinked Statistics and Extreme StatisticsModel views at key result points (e.g. max of a time history)Model video extractionOptional progress screenshotsAutomatic batch cancellation on result breachAllowed to define “Model deltas” from a base modelOption to extract all model data into qalx (useful if you want to do analytics/ML on model/sim data over time)Installationpipinstallqalx-orcaflexDocumentationThis can be found here.Questions?Pleasesend us an email (info@qalx.net)!
Package: qamasu
Qamasu is JobQueue system that respects TheSchwartz.Suited to load leveling.Implemented using optimistic lock.RequirementsPython>=2.6Django>=1.0Python3 needs Django1.5UsageSet Qamasu up!Qamasu is a Django application.You need add qamasu to your or new django project’s INSTALLED_APPS.And //manage.py syncdb//.Write your worker.DefineGRAB_FORin seconds that is max time worker grabbed for a work.Definedef work_safely(manager, job):that is a work you need.Seesample workerin workers directory for detail.RegistrationYou need add worker to abilities.register_func insert fuction record into database table if not exist.>>> from qamasu import Qamasu
>>> qamasu = Qamasu([])
>>> qamasu.register_func('workers.random_wait')Queue!Once fuction is registered to qamasu, you can enqueue jobs.Add hundreds Queues.:>>> from qamasu import Qamasu
>>> from random import uniform
>>> qamasu = Qamasu(['workers.random_wait',])
>>> for x in xrange(1,500):
      arg = dict(random_number=uniform(1,5))
      qamasu.enqueue('workers.random_wait', arg)Add a highest-priority queue.:>>> qamasu.enqueue('workers.random_wait', dict(random_number=uniform(1,5)), priority=1)Work! Work! Work!Process enqueued job.Instantiate Qamasu with availabilities.call work method. infinite loop inside this method.work method process queue as FIFO.>>> from qamasu import Qamasu
>>> qamasu = Qamasu(['workers.random_wait',])
>>> qamasu.work()Use work_prioritizing method if you tend to process job respects to priority.>>> from qamasu import Qamasu
>>> qamasu = Qamasu(['workers.random_wait',])
>>> qamasu.work_prioritizing()Caution!For MySQL backendYou must set worker’s transaction isolation level to read commited before working qamasu when you use InnoDB.>>> from django.db import connection
>>> from qamasu import Qamasu
>>> connection.cursor().execute('set session transaction isolation level read committed')
>>> qamasu = Qamasu(['workers.random_wait',])
>>> qamasu.work()Or you have to set transaction isolation level read committed. It’s global settings and dangerous.[mysqld]
transaction-isolation=Read-Committed
Package: qamlib
qamlibThis is a library meant to be an easy way to interact with a V4L2 camera, by
having a simple interface to capture images, and change camera controls. It is
a C++20 class (+ a few structs) together with Python bindings viapybind11.While the Python bindings are the main usage ofqamlib, it is also possible to
buildqamlibas a (static) C++ library.Supported featuresGet/set "normal" and extended V4L2 controls.Get/set image formats.Get/set framesize and cropping.Get/set framerate (FPS).Read out frames from a capture video device as a NumPy array. With or without
buffering.Subscribe to events for a V4L2 device.Planned featuresPushing frames to a output video device.Supporting multiplane devices.There are also some features supported that are currently exclusive to
Qtechnology A/S cameras, but these are not compiled when main-line kernel
headers are detected.Exampleimportqamlibcam=qamlib.Camera("/dev/video0")# Use context manager to start and stop streamingwithcam:metadata,frame=cam.get_frame()# gets an image as raw bytes# process imageSee more in thedocumentationBuildingPythonBuilding the Python module is done viamesonpy.Dependenciesgcclibstdc++-devmesonmesonpynlohmann-jsonpybind11pybind11_jsonpython3-buildpython3-devTo build the module:python-mbuildC++Dependiciesgccmesonninjanlohmann-jsonTo build the library we start by runningmesonsetup:mesonsetupbuild-Dpython=falseThen to compile domesoncompile-CbuildTo install the package.mesoninstallTestingUndertests/are some tests, these have only been actually tested on
Qtechnology A/S cameras.
Package: qamlz
QAML-ZwarningThis package is no longer being supported, instead use qamlzim.This is a supervised ML algorithm used to train a Binary Classifier on D-Wave's Quantum Annealers. The library has been set up to be compatible with Scikit-Learn's data representation. The algortihm is intended to be generalizable to any Binary ML problem.In order to run the program you'll need D-Wave credentials, these can be obtained athttps://cloud.dwavesys.com/leap/signup/. You'll need a github account in order to sign up. This account will give you the "endpoint_url" and "account_token" referenced below.InstallationRun the following to install:$pipinstallqamlzContributorsSpecial thanks to everyone who helped me develop this moduleMy PI and Grad student:Javier Duarte and Raghav Kansal (University of California San Diego, La Jolla, CA 92093, USA)All of QMLQCF, with special mentions of:Jean-Roch (California Institute of Technology, Pasadena, CA 91125, USA)Daniel Lidar (University of Southern California, Los Angeles, CA 90007, USA)Gabriel Perdue (Fermi National Accelerator Laboratory, Batavia, IL 60510, USA)Author of the original QAML-Z code:Alexander Zlokapa (Massachusetts Institute of Technology, Cambridge, MA 02139, USA)Mentoring for code practices:Otto Sievert (GoPro, Inc.)Usageimportqamlz# Generate the Environment (Data) for the Modelenv=qamlz.TrainEnv(X_train,y_train,endpoint_url,account_token)# Generate the Config (Hyperparameters) for the Modelconfig=qamlz.ModelConfig()# Create the Model and begin trainingmodel=qamlz.Model(config,env)model.train()Developing QAML-ZTo install qamlz, along with the tools you need to develop and run tests, run the following in your virtualenv:$pipinstall-e.[dev]
Package: qamlzim
QAML-ZIMQuantum Adiabatic Machine Learning with Zooming IMproved. This is a supervised ML algorithm used to train a Binary Classifier on D-Wave's Quantum Annealers. The library has been set up to be compatible with Scikit-Learn's data representation. The algortihm is intended to be generalizable to any Binary ML problem.In order to run the program you'll need D-Wave credentials, these can be obtained athttps://cloud.dwavesys.com/leap/signup/. You'll need a github account in order to sign up. This account will give you the "endpoint_url" and "account_token" referenced below.InstallationRun the following to install:$pipinstallqamlzimContributorsSpecial thanks to everyone who helped me develop this moduleMy PI and Grad student:Javier Duarte and Raghav Kansal (University of California San Diego, La Jolla, CA 92093, USA)All of QMLQCF, with special mentions of:Jean-Roch (California Institute of Technology, Pasadena, CA 91125, USA)Daniel Lidar (University of Southern California, Los Angeles, CA 90007, USA)Gabriel Perdue (Fermi National Accelerator Laboratory, Batavia, IL 60510, USA)Author of the original QAML-Z code:Alexander Zlokapa (Massachusetts Institute of Technology, Cambridge, MA 02139, USA)Mentoring for code practices:Otto Sievert (GoPro, Inc.)Usageimportqamlzim# Generate the Environment (Data) for the Modelenv=qamlzim.TrainEnv(X_train,y_train,endpoint_url,account_token)# Generate the Config (Hyperparameters) for the Modelconfig=qamlzim.ModelConfig()# Create the Model and begin trainingmodel=qamlzim.Model(config,env)model.train()Developing QAML-ZIMTo install qamlzim, along with the tools you need to develop and run tests, run the following in your virtualenv:$pipinstall-e.[dev]
Package: qampy
QAMPy a DSP chain for optical communication signalsQAMPy is a dsp chain for simulation and equalisation of signals from optical communication transmissions.
It is written in Python, but has been designed for high performance and most performance critical
functions are written withpythranto run at speed of compiled c or c++
code.QAMPy can equalise BPSK, QPSK and higher-order QAM signals as well as simulate signal impairments.EqualisationFor signal equalisation it contains:CMA and modified CMA equalisation algorithmsRadius directed equalisersseveral decision directed equaliser implementationsphase recovery using blind phase search (BPS) and ViterbiViterbi algorithmsfrequency offset compensationa complete set of pilot-based equalisation routines, including frame synchronization, frequency offset
estimation, adaptive equalisation and phase recoveryadditional data-aided and real-valued adaptive equaliser routinesImpairmentsIt can simulate the following impairments:frequency offsetSNRPMDphase noisetransceiver impairments such as modulator nonlinearity, DAC frequency response and limited ENOBSignal Quality MetricsQAMpy is designed to make working with QAM signals easy and includes calculations for several
performance metrics:Symbol Error Rate (SER)Bit Error Rate (BER)Error Vector Magnitude (EVM)Generalized Mututal Information (GMI)DocumentationWe put a strong focus on documenting our functions and most public functions should be well documented.
Use help in jupyter notebook to excess the documenation.You can access documentation with an extensive API at ourwebsite.For examples of how to use QAMpy see the Scripts and the Notebooks subdirectory, note that not all files are up-to-date
You should in particular look at thecma_equaliser.pyand64_qam_equalisation.pyfiles.InstallationInstallation instructions can be found herehere.StatusQAMpy is still in alpha status, however we daily in our work. We will try to keep the basic API stable
across releases, but implementation details under core might change without notice.Licence and AuthorsQAMpy was written by Mikael Mazur and Jochen Schröder from the Photonics Laboratory at Chalmers University of Technology
and is licenced under GPLv3 or later.CitingIf you use QAMpy in your work please cite us asJochen Schröder and Mikael Mazur, "QAMPy a DSP chain for optical  communications, DOI: 10.5281/zenodo.1195720".AcknowledgementsThe GPU graphics card used for part of this work was donated by NVIDIA Corporation
Package: qanalytics-python
Library to consume qanalytics API services (https://www.qanalytics.cl)Install:pip3installqanalytics_pythonFor exampleTo consume the service "Webservices QMGPS"endpoint: "/gps_test/service.asmx"method: "WM_INS_REPORTE_PUNTO_A_PUNTO"params:fromdatetimeimportdatetimefromqanalytics_python.qanalyticsimportQAnalyticsqa_client=QAnalytics("WS_test","$$WS17")data={"ID_REG":"test","LATITUD":-32.1212,"LONGITUD":-72.551,"VELOCIDAD":0,"SENTIDO":0,"FH_DATO":datetime.strptime("2019-12-27 08:23:50",'%Y-%m-%d%H:%M:%S'),"PLACA":"TEST","CANT_SATELITES":1,"HDOP":1,"TEMP1":999,"TEMP2":999,"TEMP3":999,"SENSORA_1":-1,"AP":-1,"IGNICION":-1,"PANICO":-1,"SENSORD_1":-1,"TRANS":"TEST",}resp=qa_client.send_request(data,"/gps_test/service.asmx","WM_INS_REPORTE_PUNTO_A_PUNTO")print(f"response code:{resp.code.name}")print(f"response text:{resp.text}")print(f"response code:{resp.http_code}")Note that for fields of type "DATETIME" you must pass an object of type "datetime.datetime" as a parameter.Conversion Example:date_str="2019-12-27 08:23:50"datetime.strptime(date_str,'%Y-%m-%d%H:%M:%S')