Saravanan666/pypi-org huggingface dataset:
Package: qsense
qSenseqsenseis a python library and command line tool for QLIK QlikSense. It contains some useful functions for administrators/developers of QLiksenseIt is built on top of the python librariesqsAPIandpyqlikengineInstallationpip install qsenseUsageexport LOGLEVEL=DEBUGNAMEqsense-qsenseisapythonlibraryandcommandlinetoolforQliksenseadministrators

SYNOPSISqsenseCOMMAND

DESCRIPTIONqsenseisapythonlibraryandcommandlinetoolforQliksenseadministrators

COMMANDSCOMMANDisoneofthefollowing:add_entityaddanewentity(user,stream,dataconnection,...)deallocate_analyzer_licenses_for_professionalsDeallocateanalyzerlicensefomuserswithaprofessionallicensedeallocate_unused_analyzer_licensesDeallocateanalyzerlicensenotusedforNdaysdelete_removed_exernally_usersDeleteusersthatwereremovedexternally(fromactivedirectory?)delete_user_sessiondeleteusersessionentityGetaspecificentitybyIDorentitylistorcountexport_appsExport(publishedorpassinganyotherfilter)applicationstoqvdfilesgetgenericgethttpfromQlik(servicecanbeqrsorqps)get_app_connectionsExtracttheconnectionsfromanappget_app_dataconnectionsExtractthedataconnectionsfoundintheappscriptget_app_scriptExtracttheETLscriptfromanapphealthcheckGetaspecificentitybyIDorentitylistorcountold_appsFindoldappsusing'modified_date'and'last_reload_time'filters:thenyoucanexportthemordeleteornotifyviaemailtheownersopen_docLoadanappinmemory,usefulforpreloadinganapp/cachawarmerpostNOTTESTED:genericposthttptoQlik(servicecanbeqrsorqps)update_custom_property_with_users_listupdatethevaluesofacustompropertywiththelistofallqliksenseusersupdate_entityupdateanentity(user,stream,dataconnection,...)user_sessionsusersessionsusersGetuserswithgroupsusers_with_unpublished_appsFinduserswithtoomanyunpublishedappsLook at the source file qsense/command_line.py for detailsExamplesChanging all data connections after a file server migrationJSONFILE=ds-shares.json
rm$JSONFILEqsensegetqlikserver.redaelli.orgclient.pem/qrs/dataconnection/full--pFilter"connectionstring sw '\\\\\\\amzn'"|jq'.'>$JSONFILEsed-e's/amznfsx94rgsb1e/amznfsxe9chyjel/g'${JSONFILE}>new-${JSONFILE}qsenseupdate_entityqlikserver.redaelli.orgclient.pemdataconnectionnew-${JSONFILE}Exporting and/or deleting old applicationsNot published and not updated/reloaded in the last 120 daysqsenseold_appsqlikserver.redaelli.orgclient.pem/qrs/dataconnection/full--target_path/tmp--modified_days=120--last_reload_days=120--export--deletePublished in some specific streams and not updated/reloaded in the last 120 daysqsenseold_appsqlikserver.redaelli.orgclient.pem/qrs/dataconnection/full--target_path/tmp--modified_days=120--last_reload_days=120--published--pFilter"(stream.name eq '000' or stream.name eq 'AAA' or stream.name eq 'BBB' or stream.name eq 'CCC')"--export--deleteRemoving analyzer licenses for professional usersqsensedeallocate_analyzer_licenses_for_professionalsqlikserver.redaelli.orgclient.pem--nodryrunTop users with unpublished appsqsenseusers_with_unpublished_appsqlikserver.redaelli.orgclient.pem--threshold30
Package: qsentry
QsentryQsentry is a command line wrapper for Sentry's API.Some Command ExamplesTop level commands$ qsentry --help
Usage: qsentry [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  get     Display resources such as members, teams, organizations and etc.
  update  Update a resource such as a client keyGet command$ qsentry members --help
Usage: qsentry get [OPTIONS] COMMAND [ARGS]...

  Display resources such as members, teams, organizations and etc.

Options:
  --help  Show this message and exit.

Commands:
  client-keys  Get all client keys of the given project.
  members      Get the members
  projects     Get the projects
  teams        Get the teams
  users        Get all users of the given organization.
Package: qserverinfo
QServerInfoShows info about quake servers in the trayHow to install (linux only)pipinstallqserverinfoUsageqserverinfo[IP]:[PORT]Additional arguments:-n NAME, --name NAME  server name, it will be shown in GUI
-it ICON_TITLE, --icon-title ICON_TITLE
                    text on top of icon
-rd REQUEST_DELAY, --request-delay REQUEST_DELAY
                    how often server will be requested; in seconds, minimum 30, default 60
-fb, --filter-bots    remove bots from players count if possible
-e EXECUTABLE, --executable EXECUTABLE
                    path to game executable which will start after "Join" button click with parameter "+connect <address>"
-m, --show-mapname    display mapname in the window if possibleSupported gamesPossibly all games that supported byhttps://github.com/cetteup/pyq3serverlistWhat I tested and its working:XonoticOpenArenaWarsowDoombringerNot working:QuakeWorldAlienArena
Package: qs-files
qs-filesqs-files- это Python библиотека, предназначенная для управления файлами и конфигурациями в формате .qs. С помощью этой библиотеки вы можете легко загружать, изменять и сохранять конфигурации в ваших Python проектах.УстановкаВы можете установить библиотеку с помощью pip:pipinstallqs-filesОписаниеqs-filesпредоставляет удобные инструменты для работы с .qs файлами. Основные возможности включают:Загрузку и парсинг .qs файлов.Создание, изменение и сохранение конфигураций.Поиск .qs файлов в указанной директории.Эта библиотека поддерживает Python 3.7 и более новые версии.ИспользованиеПростой пример использования библиотеки:importosimportjsonimportloggingimportrequestsimportrandomfromqs_files.configsimportConfigs# Настроим логгирование для отслеживания событийlogging.basicConfig(filename='app.log',level=logging.INFO,format='%(asctime)s-%(levelname)s:%(message)s')# Создаем объект конфигурации для работы с файлом .env/config.qsconfig=Configs('.env/config.qs')# Загружаем конфигурацию из файлаconfig.load()# Получаем значение из конфигурацииvalue=config.get_value('section_name','key_name')# Изменяем значение и сохраняем конфигурациюconfig.set_value('section_name','key_name','new_value')config.save()# Создаем новую секцию и добавляем в нее значенияnew_section_data={'new_key_1':'value_1','new_key_2':'value_2'}config.set_section('new_section',new_section_data)# Добавляем новое значение в существующую секциюconfig.add_value('existing_section','new_key','new_value')# Устанавливаем значение для бэкаповconfig.set_backup_time(24)# Включаем бэкапыconfig.backup(enable=True)# Сохраняем обновленную конфигурациюconfig.save()# Получаем содержимое секции 'existing_section' после всех измененийsection_data=config.get_section('existing_section')logging.info(f"Содержимое секции 'existing_section':{section_data}")# Создаем и сохраняем JSON-файл с данными из секцииjson_data=json.dumps(section_data)withopen('config_data.json','w')asjson_file:json_file.write(json_data)# Выводим список файлов в текущей директорииfile_list=os.listdir('.')logging.info(f"Список файлов в текущей директории:{file_list}")# Выполняем HTTP-запрос к сайтуresponse=requests.get('https://jsonplaceholder.typicode.com/posts/1')ifresponse.status_code==200:post_data=response.json()logging.info(f"Данные из HTTP-запроса:{post_data}")else:logging.error(f"Ошибка при выполнении HTTP-запроса:{response.status_code}")# Генерируем случайное число и сохраняем его в конфигурацииrandom_number=random.randint(1,100)config.set_value('random','random_number',str(random_number))config.save()ДокументацияПодробная документация и примеры использования доступны наGitHub.СообществоПрисоединяйтесь к нашему сообществу наDiscord, где вы можете задать вопросы, обсудить проект и получить поддержку от других пользователей.Веб-сайтПосетитевеб-сайтпроектаqs-filesдля дополнительной информации и ресурсов.ЛицензияЭтот проект распространяется под лицензией MIT. Подробности доступны в файлеLICENSE.АвторПроект разработанQuadrat.Ik.Связаться с намиЕсли у вас есть вопросы или предложения, не стесняйтесь связаться с нами по адресуquadrat.ik@yandex.com.
Package: qshader
Powerful shading support for PyQt5..#thread QTimer 10#pre_define hue 0#import PyQt5.QtGui QColor#begin_shaderColor1=QColor.fromHsv(hue,50,200)Color2=QColor.fromHsv((hue+30)%360,50,220)Color3=QColor.fromHsv((hue+60)%360,50,240)if@@UNDER_MOUSE:Gradient=f"qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0{Color1.name()}, stop:{@@MOUSE_POSITION_WIDGET.x()/$$parent.width()}{Color2.name()}, stop:1{Color3.name()})"else:Gradient=f"qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0{Color1.name()}, stop:0.5{Color2.name()}, stop:1{Color3.name()})"$$style(f"background:{Gradient}; color: white; border: 0px solid; border-radius: 1px; font-size: 18px; padding: 15px;")$$effect(BloomEffect,100,QColor.fromRgb(Color1.red(),Color2.green(),Color3.blue(),@@RGB_MAX))hue=(hue+1)%360#end_shader#thread QTimer 10#pre_define hue 0#import PyQt5.QtGui QColor#begin_shadercolor=QColor.fromHsv(hue,@@RGB_MAX,@@RGB_MAX)$$style(f'background-color:{color.name()}; color: black; border: 0px; font-size: 20px; padding: 30px;')$$effect(BloomEffect,250,color)$$parent.setText(color.name())hue=(hue+(2if@@UNDER_MOUSEelse1))%360#end_shaderWARNING: QShader is in ALPHA state, please be patient and report all bugs.QShader Documentation.QTS Documentation.QShader V1.0.0-ALPHA.
Package: qsharp
Q# Language Support for PythonQ# is an open-source, high-level programming language for developing and running quantum algorithms.
Theqsharppackage for Python provides interoperability with the Q# interpreter, making it easy
to simulate Q# programs within Python.InstallationTo install the Q# language package, run:pipinstallqsharpUsageFirst, import theqsharpmodule:importqsharpThen, use the%%qsharpcell magic to run Q# directly in Jupyter notebook cells:%%qsharp

open Microsoft.Quantum.Diagnostics;

@EntryPoint()
operation BellState() : Unit {
    use qs = Qubit[2];
    H(qs[0]);
    CNOT(qs[0], qs[1]);
    DumpMachine();
    ResetAll(qs);
}

BellState()SupportFor more information about the Azure Quantum Development Kit, visithttps://aka.ms/AQ/Documentation.ContributingQ# welcomes your contributions! Visit the Q# GitHub repository at [https://github.com/microsoft/qsharp] to find out more about the project.
Package: qsharp-chemistry
Q# Chemistry Library for PythonTheqsharp-chemistrypackage for Python provides interoperability with Microsoft Quantum Development Kit's Chemistry Library.For details on how to get started with Python and Q#, please see theGetting Started with Python guide.For details about the Quantum Chemistry Library, please see theIntroduction to the Quantum Chemistry Library articlein our online documentation.You can also try ourQuantum Computing Fundamentalslearning path to get familiar with the basic concepts of quantum computing, build quantum programs, and identify the kind of problems that can be solved.Installing with AnacondaIf you use Anaconda or Miniconda, installing theqsharppackage will automatically include all dependencies:condainstall-cquantum-engineeringqsharpInstalling from SourceIf you'd like to contribute to or experiment with the Python interoperability feature, it may be useful to install from source rather than from theqsharp-chemistrypackage on the Python Package Index (PyPI).
To do so, make sure that you are in thePython/qsharp-chemistrydirectory, and runsetup.pywith theinstallargument:cdPython/qsharp-chemistry
pythonsetup.pyinstallBuilding theqsharp-chemistryPackageThe Python interoperability feature uses a standardsetuptools-based packaging strategy.
To build a platform-independent wheel, run the setup script withbdist_wheelinstead:cdPython/qsharp-chemistry
pythonsetup.pybdist_wheelBy default, this will create aqsharp-chemistrywheel indist/with the version number set to 0.0.0.1.
To provide a more useful version number, set thePYTHON_VERSIONenvironment variable before runningsetup.py.Support and Q&AIf you have questions about the Quantum Development Kit and the Q# language, or if you encounter issues while using any of the components of the kit, you can reach out to the quantum team and the community of users inStack Overflowand inQuantum Computing Stack Exchangetagging your questions withq#.
Package: qsharp-core
Q# Interoperability for PythonTheqsharp-corepackage for Python provides interoperability with the Quantum Development Kit and with the Q# language, making it easy to simulate Q# operations and functions from within Python.For details on how to get started with Python and Q#, please see theGetting Started with Python guide.You can also try ourQuantum Computing Fundamentalslearning path to get familiar with the basic concepts of quantum computing, build quantum programs, and identify the kind of problems that can be solved.Installing with AnacondaIf you use Anaconda or Miniconda, installing theqsharppackage will automatically include all dependencies:condainstall-cquantum-engineeringqsharpInstalling from SourceIf you'd like to contribute to or experiment with the Python interoperability feature, it may be useful to install from source rather than from theqsharp-corepackage on the Python Package Index (PyPI).
To do so, make sure that you are in thePythondirectory, and runsetup.pywith theinstallargument:cdiqsharp/src/Python/
pythonsetup.pyinstallBuilding theqsharp-corePackageThe Python interoperability feature uses a standardsetuptools-based packaging strategy.
To build a platform-independent wheel, run the setup script withbdist_wheelinstead:cdiqsharp/src/Python/
pythonsetup.pybdist_wheelBy default, this will create aqsharp-corewheel indist/with the version number set to 0.0.0.1.
To provide a more useful version number, set thePYTHON_VERSIONenvironment variable before runningsetup.py.Support and Q&AIf you have questions about the Quantum Development Kit and the Q# language, or if you encounter issues while using any of the components of the kit, you can reach out to the quantum team and the community of users inStack Overflowand inQuantum Computing Stack Exchangetagging your questions withq#.
Package: qsharp-jupyterlab
Q# Language Support for JupyterLabQ# is an open-source, high-level programming language for developing and running quantum algorithms.
Theqsharp-jupyterlabextension provides syntax highlighting for Q# documents and Q# notebook
cells in JupyterLab.InstallationTo install the Q# JupyterLab extension, run:pipinstallqsharp-jupyterlabTo run Q# in Jupyter notebooks, remember to also install theqsharppackage: [https://pypi.org/project/qsharp].SupportFor more information about the Azure Quantum Development Kit, visithttps://aka.ms/AQ/Documentation.ContributingQ# welcomes your contributions! Visit the Q# GitHub repository at [https://github.com/microsoft/qsharp] to find out more about the project.
Package: qsharp-lang
Q# Language Support for PythonQ# is an open-source, high-level programming language for developing and running quantum algorithms.
Theqsharp-langpackage for Python provides interoperability with the Q# interpreter, making it easy
to simulate Q# programs within Python.InstallationTo install the Q# language package, run:pipinstallqsharp-langUsageFirst, import theqsharpmodule:importqsharpThen, use the%%qsharpcell magic to run Q# directly in Jupyter notebook cells:%%qsharp

open Microsoft.Quantum.Diagnostics;

@EntryPoint()
operation BellState() : Unit {
    use qs = Qubit[2];
    H(qs[0]);
    CNOT(qs[0], qs[1]);
    DumpMachine();
    ResetAll(qs);
}

BellState()SupportFor more documentation and to browse issues, please visit the Q# project wiki at [https://github.com/microsoft/qsharp/wiki].ContributingQ# welcomes your contributions! Visit the Q# GitHub repository at [https://github.com/microsoft/qsharp] to find out more about the project.
Package: qsignal
QSignal – A QT Signal/slot for pythonThis project provides easy to use Signal class for implementing Signal/Slot mechanism in Python.
It does not implement it strictly but rather creates the easy and simple alternative.ClassesSignalqsignal.Signalis the main class.To create a signal, just make asig = qsignal.Signaland set up an emitter of it. Or create it withsig = qsignal.Signal(emitter=foo).To emit it, just call yoursig().
Or emit it in asynchronous mode with the methodasync.Example:>>> from qsignal import Signal>>> # Creating signal
>>> sig = Signal()>>> # Or
>>> myobject.signal = Signal(emitter=myobject)>>> # Connecting to signals
>>> sig.connect(callback)
>>> myobject.signal.connect(sig)
>>> myobject.signal.connect(otherobject.callback_method)>>> # Emitting
>>> sig()
>>> myobject.signal('argument(s)', optional=True)>>> # Emitting in asynchronous mode
>>> sig.async()To connect slots to it, pass callbacks intoconnect. The connections are maintained throughweakref, thus
you don’t need to search for them and disconnect whenever you’re up to destroy some object.SignallerThe base class for objects to maintain their Signals.The only purpose of this class is to automate Signal names and emitter references.>>> from qsignal import Signal, Signaller>>> # For example, this is a class...
>>> class MyClass(Signaller):
>>>     my_signal = Signal()>>> # This is a slot...
>>> def my_slot():
>>>     sig = Signal.emitted()
>>>     assert sig.name == my_signal
>>>     assert sig.emitter.__class__ == MyClass>>> # And the connections...
>>> o = MyClass()
>>> o.my_signal.connect(my_slot)
>>> #...
>>> o.my_signal()
Package: qsilver
QSilverQSilver is a tiny python library, which provides api to run multiply tasks concurrent in only one thread using async/await syntax.InstallationYou can install QSilver via pip:pipintallqsilverUsageimportqsilverasyncdefexample():print("Hello")awaitqsilver.sleep(1)print("World!")qsilver.create_scheduler()qsilver.add_coroutine(example())qsilver.run_forever()LicenseMIT
Package: qsimov-cloud-client
Unlock Quantum Potential with Neighborhood Quantum Superposition!🌌 Welcome to a new era of quantum computing! 🌌Are you ready to elevate your quantum algorithms to unprecedented heights? IntroducingNeighborhood Quantum Superpositiona cutting-edge service designed to revolutionize your quantum computations.What isNeighborhood Quantum Superposition?Neighborhood Quantum Superpositionis not just a service; it's a quantum leap into enhanced efficiency and performance. This groundbreaking offering provides a diverse range of superposition options, allowing you to tailor quantum states precisely to your algorithm's needs.Key Features:🚀Versatility:Choose from various superposition types to optimize your quantum computations.🧠Efficiency Boost:Achieve superior algorithmic performance with strategically crafted superpositions.⚙️Flexible Integration:Seamlessly integrateNeighborhood Quantum Superpositioninto your existing quantum workflows.🌐Cloud-Powered:Harness the power of quantum superposition conveniently through our cloud-based platform.How It Works:Neighborhood Quantum Superpositionleverages advanced quantum algorithms to generate highly efficient superposition states. Whether you're exploring optimization problems, simulating quantum systems, or running complex quantum circuits, this service empowers you to unlock the full potential of quantum computing. Here you can see a visual example!!Get Started Today with Qsimov Cloud Client!Qsimov Cloud Client is a tool designed to facilitate interaction with Qsimov'sNeighborhood Quantum Superpositionservices. This client allows you to connect to the company's services and leverage the capabilities ofNeighborhood Quantum Superpositionfor your quantum computing needs.InstallationTo install the Qsimov Python Client, use the following pip command:pipinstallqsimov-cloud-clientBasic Usagefromqsimov_cloud_clientimportQsimovCloudClient# Initialize QsimovCloudClient with your access tokenclient=QsimovCloudClient("your_access_token")# Set parameters for the serviceclient.set_metric("ample")client.set_state(state_bin='0111010')client.set_distances(["0","9/4","inf"])client.can_have_nan(False)client.set_ancilla_mode("clean")# Generate a quantum circuitcircuit_superposition=client.generate_circuit()# Access the resultprint("The resulting circuit in qasm is:",circuit_superposition.get_qasm_code())# Additional usage examples can be found in the documentation.Don't miss out on the quantum revolution! Enhance your algorithms with Neighborhood Quantum Superposition and experience the future of quantum computing today.Ready to elevate your quantum computations?Visit QSimov!🚀Neighborhood Quantum Superposition - Transforming Quantum Computing!🚀
Package: qsiprep
qsiprep borrows heavily from FMRIPREP to build workflows for preprocessing q-space images
such as Diffusion Spectrum Images (DSI), multi-shell HARDI and compressed sensing DSI (CS-DSI).
It utilizes Dipy and ANTs to implement a novel high-b-value head motion correction approach
using q-space methods such as 3dSHORE to iteratively generate head motion target images for each
gradient direction and strength.Since qsiprep uses the FMRIPREP workflow-building strategy, it can also generate methods
boilerplate and quality-check figures.Users can also reconstruct orientation distribution functions (ODFs), fiber orientation
distributions (FODs) and perform tractography, estimate anisotropy scalars and connectivity
estimation using a combination of Dipy, MRTrix and DSI Studio using a JSON-based pipeline
specification.[Documentationqsiprep.org]
Package: qsi-tk
qsi-tkData science toolkit (TK) from Quality-Safety research Institute (QSI)Installationpip install qsi-tkContentsThis package is a master library containing various previous packages published by our team.modulesub-moduledescriptionstandalone pypi packagepublicationqsi.ioFile I/O, Dataset loadingTODO qsi-tk open datasets with algorithmsqsi.io.augData augmentation, e.g., generative modelsTODO Data aug with deep generative models. e.g., " variational autoencoders, generative adversarial networks, autoregressive models, KDE, normalizing flow models, energy-based models, and score-based models. "qsi.io.preData processing, e.g., channel alignment and 1D-laplacian kernel fs for e-nose data; x-binning, baseline removal for TOF MS.TODOqsi.visPlottingqsi.cscompressed sensingcs1Adaptive compressed sensing of Raman spectroscopic profiling data for discriminative tasks [J]. Talanta, 2020, doi: 10.1016/j.talanta.2019.120681Task-adaptive eigenvector-based projection (EBP) transform for compressed sensing: A case study of spectroscopic profiling sensor [J]. Analytical Science Advances. Chemistry Europe, 2021, doi: 10.1002/ansa.202100018Compressed Sensing library for spectroscopic profiling data [J]. Software Impacts, 2023, doi: 10.1016/j.simpa.2023.100492Secured telemetry based on time-variant sensing matrix – An empirical study of spectroscopic profiling, Smart Agricultural Technology, Volume 5, 2023, doi: 10.1016/j.atech.2023.100268qsi.fsqsi.fs.nch_time_series_fsmulti-channel enose data fs with 1d-laplacian conv kernel基于电子鼻和一维拉普拉斯卷积核的奶粉基粉产地鉴别qsi.fs.glassoStructured-fs of Raman data with group lassoin progressqsi.kernelkernelsacklAnalytical chemistry kernel library for spectroscopic profiling data, Food Chemistry Advances, Volume 3, 2023, 100342, ISSN 2772-753X, https://doi.org/10.1016/j.focha.2023.100342.qsi.drqsi.dr.metricsDimensionality Reduction (DR) quality metricspyDRMetrics, wDRMetricspyDRMetrics - A Python toolkit for dimensionality reduction quality assessment, Heliyon, Volume 7, Issue 2, 2021, e06199, ISSN 2405-8440, doi: 10.1016/j.heliyon.2021.e06199.qsi.dr.mfmatrix-factorization based DRpyMFDRMatrix Factorization Based Dimensionality Reduction Algorithms - A Comparative Study on Spectroscopic Profiling Data [J], Analytical Chemistry, 2022. doi: 10.1021/acs.analchem.2c01922qsi.claqsi.cla.metricsclassifiability analysispyCLAMs, wCLAMsA unified classifiability analysis framework based on meta-learner and its application in spectroscopic profiling data [J]. Applied Intelligence, 2021, doi: 10.1007/s10489-021-02810-8pyCLAMs: An integrated Python toolkit for classifiability analysis [J]. SoftwareX, 2022, doi: 10.1016/j.softx.2022.101007qsi.cla.ensemblehomo-stacking, hetero-stacking, FSSEpyNNRWSpectroscopic Profiling-based Geographic Herb Identification by Neural Network with Random Weights [J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2022, doi: 10.1016/j.saa.2022.121348qsi.cla.kernelkernel-NNRWqsi.cla.nnrwneural networks with random weightsqsi.pipelineGeneral data analysis pipelines.qsi.guiWeb-based apps. e.g., `python -m qsi.gui.chaihu` will launch the app for bupleurum origin discrimination.