Saravanan666/pypi-org huggingface dataset:
Package: qbertconfig
Fetches kubeconfig from qbert APIkubectl configcan be used used to manage kubeconfig files. However,
gathering a kubeconfig file for a Platform9 Managed Kubernetes cluster is
a manual process today. This aims to solve that problem by downloading
and merging clusters’ kubeconfigs with existing kubeconfig files.InstallationIt’s strongly recommended to use a python virtualenvpipinstallqbertconfigUsageqc[-h][-kKUBECONFIG]<operation>[--namecluster_name][--uuidcluster_uuid][-c]Supported Operationsfetch- get a kubeconfig for a PMK clusterhelp- show this messagelist-clusters- list available PMK clusters in the target Platform9 Managed CloudProviding CredentialsQbertconfig uses theOpenstack SDKto perform authentication against a
Platform9 Cloud. Credentials can be provided in either aclouds.yamlfile,
environment variables, or by using the--oscommand-line arguments. For more
information, please refer to theofficial documentationExamplesource~/openstack.rcqcfetch--namedev-cluster-kdev-cluster.kcfg.ymlexportKUBECONFIG=$(pwd)/dev-cluster.kcfg.ymlkubeconfiggetnodes--contextdev-clusterkubeconfiggetpods-nfooFor more information on openstack rc files and how to generate them, seeInstalling Openstack CLI Clients.TestingRunning Testspipinstall-rrequirements.txtnosetests-v-dtests/Lintingflake8--excludeversioneer.pyHow it worksHere is the basic structure of a Kubeconfig:apiVersion:v1kind:Configpreferences:{}current-context:defaultclusters:[]contexts:[]users:[]Each of cluster, context, or user, has anameassociated with it.
This is the unique identifier for each object, and each context uses
these names to tie it all together.Each of these sections can be managed with thekubectl configcommand. [Documentation]This utility will fetch a fresh kubeconfig from the Qbert API, and merge
it’s details into the specified kubeconfig.With the fresh kubeconfig, the following sections are renamed to resolve
common collisions when managing many PMK clouds.useris renamed tofqdn-usernameto align with unique
keystone environmentscontextis renamed to thecluster_nameclusteris renamed to thecluster_uuid
Package: qbiome
Info:Draft link will be posted hereAuthor:ZeD@UChicago<zed.uchicago.edu>Description:Application of quasinets (https://pypi.org/project/quasinet/) for microbiome analysis  for learning rules of organization, maturation, competition and succession in microbiome ecosystems.Documentation:https://zeroknowledgediscovery.github.io/qbiome/qbiome/index.htmlUsage:import qbiome
from qbiome.data_formatter import DataFormatter
from qbiome.quantizer import Quantizer
from qbiome.qnet_orchestrator import QnetOrchestrator
from qbiome.forecaster import Forecaster
Package: qbirthday
QBirthday is a birthday reminder status icon.FeaturesSeveral backends available (CSV file, Lightning, MySQL databse).Extendable to other backends.iCalendar export.RequirementsQBirthday runs on Python >= 3.7.It requires PyQt5 and optionally depends on mysqlclient if a MySQL database is used as backend.InstallationEither as root or in a Python virtual environment:$ pip install qbirthdayTo use MySQL backend, install mysqlclient:$ pip install mysqlclientHistoryQBirthday is a Qt port of GBirthday, a GTK application.Project linksPyPI:https://pypi.python.org/pypi/marshmallowChangelog:https://github.com/lafrech/qbirthday/blob/master/CHANGELOG.rstIssues:https://github.com/lafrech/qbirthday/issuesLicenseQBirthday is distributed under GPLv2 license (see LICENSE file).
Package: qbism
QBismpython tools for the budding quantum bettabilitarianInstallationpip install qbismNote thatqbismrelies onqutip!UsageLet's start off with a random density matrix:from qbism import *
import qutip as qt
import numpy as np

d = 2
rho = qt.rand_dm(d)We construct a random Weyl-Heisenberg IC-POVM, and get the magical quantum coherence matrix. We find the probabilities with respect to this POVM.povm = weyl_heisenberg_povm(qt.rand_ket(d))
phi = povm_phi(povm)
p = dm_probs(rho, povm)
print("probs: %s" % p)probs: [0.20215649 0.20215649 0.29784351 0.29784351]We can compare the classical probabilities (for a Von Neumann measurement after a POVM measurement whose outcome we are ignorant of) to the the quantum probabilities (in the case where we go directly to the Von Neumann measurement):H = qt.rand_herm(d)
vn = [v*v.dag() for v in H.eigenstates()[1]]

classical_probs = conditional_probs(vn, povm) @ p
quantum_probs = conditional_probs(vn, povm) @ phi @ p

print("classsical probs: %s" % classical_probs)
print("quantum probs: %s" % quantum_probs)

post_povm_rho = sum([(e*rho).tr()*(e/e.tr()) for e in povm])
assert np.allclose(classical_probs, [(v*post_povm_rho).tr() for v in vn])
assert np.allclose(quantum_probs, [(v*rho).tr() for v in vn])classsical probs: [0.55802905 0.44197095]
quantum probs: [0.65778315 0.34221685]Now let's get a SIC-POVM and explore time evolution:sic = sic_povm(d)
sic_phi = povm_phi(sic)
sic_p = dm_probs(rho, sic)

U = qt.rand_unitary(d)
evolved_sic = [U*e*U.dag() for e in sic]
R = conditional_probs(evolved_sic, sic).T

time_evolved_sic_p = R @ sic_phi @ sic_p
print("time evolved probs: %s" % time_evolved_sic_p)
assert np.allclose(dm_probs(U*rho*U.dag(), sic), time_evolved_sic_p)time evolved probs: [0.20445193 0.20445193 0.29554807 0.29554807]We could also use:time_evolved_sic_p2 = povm_map([U], sic) @ sic_phi @ sic_p
assert np.allclose(time_evolved_sic_p, time_evolved_sic_p2)Finally, let's check out partial traces:entangled = qt.rand_dm(4)
entangled.dims = [[2,2],[2,2]]

povm2 = weyl_heisenberg_povm(qt.rand_ket(2))
povm4 = apply_dims(weyl_heisenberg_povm(qt.rand_ket(4)), [2,2])
phi = povm_phi(povm4)
p = dm_probs(entangled, povm4)

ptrA = povm_map(partial_trace_kraus(0, [2,2]), povm4, povm2)
ptrB = povm_map(partial_trace_kraus(1, [2,2]), povm4, povm2)

assert np.allclose(dm_probs(entangled.ptrace(0), povm2), ptrA @ phi @ p)
assert np.allclose(dm_probs(entangled.ptrace(1), povm2), ptrB @ phi @ p)Check out the tutorial for the full story!Thanks tonbdev!
Package: qbit-py-sdk
qbit-python-sdkQbit 概念开发者 API 旨在允许企业与 Qbit 系统集成，并轻松将其作为其工作流程的一部分。该 API 允许开发者使用【全球账户】、【量子卡】业务等。项目状态当前版本1.0.0为正式版本。暂时支持了 auth 相关的接口，其他接口带后续完善，同时也提供了 Qbit Api 所需的 Post、put、delete、get 请求，方便使用者更好调用其他接口，具体使用请看下面代码示例。注意：请商户的专业技术人员在使用时注意系统和软件的正确性和兼容性，以及带来的风险。环境要求Python 3.7+安装最新版本已经在pypi发布。pip install qbit-py-sdk或者pipenv install qbit-py-sdk使用importqbit_py_sdkasQbitqbit=Qbit.QbitClient("qbit1f6efee44ceb8ca2","8f70d42a1393802aebf567be27a47879","https://api-global.qbitnetwork.com")名词解释Client，合作伙伴在 Qbit 我们称之为 Client。Account， 合作伙伴的客户在 Qbit 我们称之为 AccountclientId，商户 id，请联系我们申请。clientSecret，商户密钥，用于签名，请联系我们申请。开始获取 access tokencodeRes=qbit.get_code(state='324',redirect_uri='')res=qbit.get_access_token(codeRes.code)print(res)刷新 access tokenres=qbit.refresh_access_token("refreshToken")print(res)调用其他接口示例# 返回值 status 在 200 - 300 内表示请求正常params={"id":"5d890eda-16aa-4760-90af-3d60837f5616","limit":10}res=qbit.config("access_token").get_request("https://api-global.qbitnetwork.com/open-api/v1/budget",**params)敏感信息加解密加密-HmacSHA256fromqbit_py_sdkimportencryptHmacSHA256data={"id":"ee74c872-8173-4b67-81b1-5746e7d5ab88","accountId":None,"holderId":"d2bd6ab3-3c28-4ac7-a7c4-b7eed5eee367","currency":"USD","settlementCurrency":None,"counterparty":"SAILINGWOOD;;US;1800948598;;091000019","transactionAmount":11,"fee":0,"businessType":"Inbound","status":"Closed","transactionTime":"2021-11-22T07:34:10.997Z","transactionId":"124d3804-defa-4033-9f30-1d8b0468e506","clientTransactionId":None,"createTime":"2021-11-22T07:34:10.997Z","appendFee":0,}res=encryptHmacSHA256("25d55ad283aa400af464c76d713c07ad",**data)print(res=="8287d5539c03918c9de51176162c2bf7065d5a8756b014e3293be1920c20d102")联系我们如果你发现了BUG或者有任何疑问、建议，请通过 issue 进行反馈。也欢迎访问我们的官网。
Package: qbittorrentui
qBittorrenTUIConsole UI for qBittorrent. Not feature-complete but is usable for low volume and everyday torrenting.Key MapAny Windowq : exitn : open connection dialogTorrent List Windowa : open add torrent dialogenter : open context menu for selected torrentright arrow: open Torrent WindowTorrent Windowleft : return to Torrent Listesc : return to Torrent ListContententer : bump priorityspace : bump priorityInstallationInstall from pypi:pipinstallqbittorrentuiIn most cases, this should allow you to run the application simply with theqbittorrentuicommand. Alternatively, you can specify a specific python binary with./venv/bin/python -m qbittorrentuior similar.ConfigurationConnections can be pre-defined within a configuration file (modeled after default.ini). Specify the configuration file using --config_file. Each section in the file will be presented as a separate instance to connect to.Sample configuration file section:[localhost:8080]
HOST = localhost
PORT = 8080
USERNAME = admin
PASSWORD = adminadmin
CONNECT_AUTOMATICALLY = 1
TIME_AFTER_CONNECTION_FAILURE_THAT_CONNECTION_IS_CONSIDERED_LOST = 5
TORRENT_CONTENT_MAX_FILENAME_LENGTH = 75
TORRENT_LIST_MAX_TORRENT_NAME_LENGTH = 60
TORRENT_LIST_PROGRESS_BAR_LENGTH = 40
DO_NOT_VERIFY_WEBUI_CERTIFICATE = 1Only HOST, USERNAME, AND PASSWORD are required.
DO_NOT_VERIFY_WEBUI_CERTIFICATE is necessary if the certificate is untrusted (e.g. self-signed).TODO/WishlistApplicationFigure out the theme(s)Configuration for connectionsLog/activity output (likely above status bar)Implement window for editing qBittorrent settingsTorrent List WindowTorrent sortingAdditional torrent filtering mechanismsTorrent searchingTorrent status icon in torrent nameTorrent name color codingTorrent list column configurationTorrent WindowMake focus more obvious when switching between tabs list and a displayScrollbar in the displaysSpeed graph displayTorrent Window Content DisplayLeft key should return to tab listMIT LicenseCopyright (c) Russell MartinPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: qbiz-airflow-presto
The QBiz, inc. Airflow-Presto package provides anAirflow Operatorwhich
is designed to run containerized Presto nodes using Amazon'sElastic Container Service.  It is designed to dynamically create and destroyFargatecompute resources to minimize cost.  This package does not include
the Docker Image nor will it attempt to create other AWS resources necessary
launch ECS tasks -- although examples are provided.  A Dockerfile to
build your own image can be found in theQbiz Code Repository.
Package: qbm
Quantum Boltzmann MachinesTheqbmPython package is designed for training and analyzing quantum Boltzmann machines (QBMs) using either a simulation or a D-Wave quantum annealer.
The QBM implemented here is based on the work inQuantum Boltzman Machineby Amin et al.[1].
This package originated as part of the thesisQuantum Boltzmann Machines: Applications in Quantitative Finance.Table of ContentsInstallationConda EnvironmentUsageBasic ConfigurationBQRBM ModelInstantiationTrainingSamplingSaving and LoadingExampleReferencesInstallationTheqbmpackage can be installed withpip install qbmUsageBasic ConfigurationTheqbmpackage is mainly configured around the project directory, which can be set with theQBM_PROJECT_DIRenvironment variable.
Once the environment variable is set one can use theqbm.utils.get_project_dir()function to get a path object to the directory.BQRBM ModelThe BQRBM, or bound-based quantum restricted Boltzmann machine, is a quantum Boltzmann machine that has intra-layer restrictions and is trained via maximization of the log-likelihood lower bound.
The model currently only has the ability to train in the specific case wheres_freeze = 1, i.e., when it reduces to a classical RBM trained with quantum assistance, because estimating the effective inverse temperature is nontrivial for the general case.All of the arguments to the methods below are further explained in their respective docstrings.InstantiationA BQRBM model can be instantiated as (for example)model = BQRBM(
    V_train,
    n_hidden,
    A_freeze,
    B_freeze,
    beta_initial=1.0,
    simulation_params={"beta": 1.0},
    seed=0,
)One needs to choose whether or not they want to train a model using a simulation or an annealer, and this is done by passing eithersimulation_paramsorannealer_params.
Whichever is passed decides how the model is trained.TrainingThe model can be trained by runningmodel.train(
    n_epochs=100,
    learning_rate=1e-1,
    learning_rate_beta=1e-1,
    mini_batch_size=10,
    n_samples=10_000,
    callback=None,
)SamplingThe model can generate samples by runningmodel.sample(
    n_samples,
    answer_mode="raw",
    use_gauge=True,
    binary=False,
)Saving and LoadingThe model can be saved withmodel.save("/path/to/model.pkl")and loaded again withmodel = BQRBM.load("/path/to/model.pkl")ExampleAn example notebook can be foundhereReferences[1]Mohammad H. Amin et al. “Quantum Boltzmann Machine”. In: Phys. Rev. X 8 (2 May 2018), p. 021050. doi: 10.1103/PhysRevX.8.021050. url:https://link.aps.org/doi/10.1103/PhysRevX.8.021050.
Package: qbosdk
QuickbooksOnlineSDKPython SDK for accessing QBO APIs.InstallationThis project requiresPython 3+andRequestslibrary (pip install requests).Download this project and use it (copy it in your project, etc).Install it frompip.$ pip install qbosdkUsageTo use this SDK you'll need these QBO credentials used for OAuth2 authentication:client ID,client secretandrefresh token.This SDK is very easy to use.First you'll need to create a connection using the main class QuickbooksOnlineSDK.fromqbosdkimportQuickbooksOnlineSDKconnection=QuickbooksOnlineSDK(client_id='<YOUR CLIENT ID>',client_secret='<YOUR CLIENT SECRET>',refresh_token='<YOUR REFRESH TOKEN>',realm_id='<REALM / COMPANY ID>',environment='<sandbox / production>')After that you'll be able to access any of the API classes"""USAGE: <QuickbooksOnlineSDK INSTANCE>.<API_NAME>.<API_METHOD>(<PARAMETERS>)"""# Get a list of all Employees (with all available details for Employee)response=connection.employees.get()# Get a list of all Accountsresponse=connection.accounts.get()See more details about the usage into the wiki pages of this project.Integration TestsTo run integration tests, you will need a mechanism to connect to a real qbo account. Save this info in a test_credentials.json file in your root directory:{"client_id":"<client_id>","client_secret":"<client_secret>","realm_id":"<realm_id>","refresh_token":"<refresh_token>","environment":"<environment sandbox / production>"}$pipinstallpytest

$python-mpytesttest/integrationLicenseThis project is licensed under the MIT License - see theLICENSEfile for details
Package: qbox
FeaturesTODORequirementsTODOInstallationYou can installQuantum BoxviapipfromPyPI:$pipinstallqboxUsagePlease see theCommand-line Referencefor details.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,Quantum Boxis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz’sHypermodern Python Cookiecuttertemplate.
Package: qbraid-cli
Command Line Interface for interacting with all parts of the qBraid platform.TheqBraid CLIis a specialized command-line interface tool designedexclusivelyfor use within theqBraid Labplatform. It is not intended for local installations or use outside the qBraid Lab environment. This tool ensures seamless integration and optimized performance specifically tailored for qBraid Lab's unique cloud-based quantum computing ecosystem.Getting StartedTo use the qBraid CLI, login to qBraid (or create an account), launch Lab, and then open Terminal. You can also access the CLI directly from withinNotebooksusing the!operator. Seequantum jobs example.Launch qBraid Lab →Make an account →For help, see qBraid Lab User Guide:Getting Started.Basic Commands$qbraid
-------------------------------
*WelcometotheqBraidCLI!*
--------------------------------Use`qbraid-h`toseeavailablecommands.-Use`qbraid--version`todisplaythecurrentversion.

ReferenceDocs:https://docs.qbraid.com/projects/cli/en/latest/cli/qbraid.htmlA qBraid CLI command has the following structure:$qbraid<command><subcommand>[optionsandparameters]For example, to list installed environments, the command would be:$qbraidenvslistTo view help documentation, use one of the following:$qbraidhelp$qbraid<command>help$qbraid<command><subcommand>helpFor example:$qbraidhelpGroupqbraid

Subgroupsenvs:ManageqBraidenvironments.kernels:ManageqBraidkernels.jobs:ManageqBraidQuantumJobs.

Arguments-V,--version:ShowversionandexitGlobalArguments-h,--help:Showthishelpmessageandexit.

ReferenceDocs:https://docs.qbraid.com/en/latest/cli/qbraid.htmlTo get the version of the qBraid CLI:$qbraid--version
Package: qbraid-qir
Work in progressqBraid-SDK extension providing support for QIR conversions.This project aims to makeQIRrepresentations accessible via the qBraid-SDKtranspiler, and by doing so, open the door to language-specific conversions from any and all high-level quantum languagessupportedbyqbraid. See QIR Alliance:why do we need it?.Getting startedInstallationpipinstallqbraid-qirExampleimportcirqfromqbraid_qirimportcirq_to_qirq0,q1=cirq.LineQubit.range(2)circuit=cirq.Circuit(cirq.H(q0),cirq.CNOT(q0,q1),cirq.measure(q0,q1))module=cirq_to_qir(circuit,name="my-circuit")ir=str(module)DevelopmentInstall from sourcegitclonehttps://github.com/qBraid/qbraid-qir.gitcdqbraid-qir
pipinstall-e.Run testspipinstall-rrequirements-dev.txt
pytesttestswith coverage reportpytest--cov=qbraid_qir--cov-report=termtests/Build docscddocs
pipinstall-rrequirements.txt
makehtmlArchitecture diagram
Package: qb-sdk
qb-sdk-pythonBrand SDK for the qiibee blockchain.RequirementsConsult thedocsto see how to obtain the credentials necessary for this.Installationpip install qb-sdkExample usageImport the SDK library:import qbsdkInitialize the API object:api_key = os.environ['QB_API_KEY']
brand_address_private_key = os.environ['BRAND_ADDRESS_PRIVATE_KEY']
token_symbol = os.environ['BRAND_TOKEN_SYMBOL']
api = qbsdk.Api(api_key)Fetch existing tokens:tokens = api.get_tokens()
for token in tokens.private:
    print(vars(token))Reward a particular user:wallet = qbsdk.Wallet(brand_address_private_key, token_symbol, api, transfer_strategy=qbsdk.TransferStrategy.brand)

wallet.setup()

transfer_receiver = '0x87265a62c60247f862b9149423061b36b460f4bb'
tx = wallet.send_transaction(transfer_receiver, 10)Check out theexamplesdirectory for more comprehensive examples.
Package: qbstyles
QB StylesQB Styles is a python package with a light and a darkmatplotlibstyle.Dark styleLight styleHow do I install QB Styles?qbstylesis a Python package. To install it, simply run:pipinstallqbstylesHow do I use QB Styles?You can use the dark Matplotlib style theme in the following way:fromqbstylesimportmpl_stylempl_style(dark=True)And to use the light Matplotlib style theme, you can do the following:fromqbstylesimportmpl_stylempl_style(dark=False)How do I use QB Styles in Jupyter Notebooks?⚠️ Please make sure you runfrom qbstyles import mpl_styleandmpl_style()indifferent cellsas shown below. Seethis issuefor more details.# first cellfromqbstylesimportmpl_style# second cellmpl_style()What chart types can use QB Styles?Line plotsScatter plotsBubble plotsBar chartsPie chartsHistograms and distribution plots3D surface plotsStream plotsPolar plotsCan you show me a few examples?To run the examples inexample.ipynb, install the required packages usingpip install -r requirements_notebook.txtin a Python virtual environment of your choice.importmatplotlib.pyplotaspltfromqbstylesimportmpl_styledefplot(dark):mpl_style(dark)fig,axes=plt.subplots(2,2,figsize=(15,10))# the following functions are defined in example.ipynbline_plot(axes[0,0])scatter_plot(axes[0,1])distribution_plot(axes[1,0])ax=plt.subplot(2,2,4,projection='polar')polar_plot(ax)plot(dark=True)plot(dark=False)How do I create my own styles?Have a look at the filesqb-common.mplstyle,qb-dark.mplstyleandqb-light.mplstyle. They contain many elements that you may want to customise.To do so, create a file similar to the above files at the root of your project, and apply it after theqbstyleas follows:importmatplotlib.pyplotaspltfromqbstylesimportmpl_stylempl_style()plt.style.use('./your-style.mplstyle')All ofmatplotlibrc's options can be foundhere.What licence do you use?QB Styles is licensed under theApache 2.0 License.
Package: qbwc
## QBWC: Quickbooks Desktop WebconnectorExperimentalDjango package for syncing data between django application and
QuickBooks Desktop via the Quickbooks Webconnector (QBWC).Implementation includes transfer services (push, pull, re-sync) for:gl accountsother name listexpenses (credit card charges)customerscredit cardsvendorsRoad map:vendor billsjournal entriesQuickBooks Reports## InstallationInstall the latest development version from github using:` pip installgit+https://github.com/bill-ash/qbwc`or from pypi:` pip install qbwc `## ExampleExample directory includes application with sample apps for each of the entites mentioned above.Repeated patterns will be abstracted in theBaseObjectMixinmodel and are likely to change.
Package: qbwrap
QBwrapQuick Bubblewrap chroot management tool.AboutQBwrap is a tool that allows for quick bwrap chroot management with toml
configuration files.SampleA sample QBwrap configuration file might look something like this:[core]location="/chroots/my-chroot"hostname="test"process="bash -l"[mount]rw=[["/chroots/my-chroot","/"],]ro=["/etc/resolv.conf",]dev=["/dev",]proc=["/proc",]tmpfs=[["/dev/shm",{}],["/tmp",{perms="1777"}],]DocumentationDocumentation can either be built locally or browsed online onGitLab pages.Command-line interfaceUsageqbwrap [-h] [-V] [-q] [-f] [-r]
       [-R ROOT_METHOD] [-S DEV_SIZE] [-e EXECUTABLE] [-p PROCESS] [-m MERGE]
       [-P COLLECTION_PATH]
       [-c {default,always,never}]
       [--no-setup] [--force-setup]
       qbwrap_filePositional argumentsqbwrap_file           path to the QBwrap config fileOptions-h, --help            show this help message and exit
  -V, --version         show program's version number and exit
  -q, --quiet           be quiet, limit information output
  -f, --fake            do not actually call bwrap
  -r, --root            call bwrap as root
  -R ROOT_METHOD, --root-method ROOT_METHOD
                        which method to use to become root
  -S DEV_SIZE, --dev-size DEV_SIZE
                        default size for special devices, eg. tmpfs
  -e EXECUTABLE, --executable EXECUTABLE
                        bubblewrap executable to call, defaults to "bwrap"
  -p PROCESS, --process PROCESS
                        override process command to start inside bwrap chroot
  -m MERGE, --merge MERGE
                        override any stanza of given QBwrap config
  -P COLLECTION_PATH, --collection-path COLLECTION_PATH
                        overwrite path of the QBwrap collection
  -c {default,always,never}, --use-collection {default,always,never}
                        whether and how to use the QBwrap collection
  --no-setup            do not run setup (from the setup stanza) even if required
  --force-setup         force to run setup defined in the setup stanzaInstallationPyPiAvailable atpypi.org/project/qbwrap.pipinstall--user--break-system-packagesqbwrapRepositorySee instructions in thexgqt-python-app-qbwraprepository'sREADME.mdfile.