Saravanan666/pypi-org huggingface dataset:
Package: connsearch
connsearchis a Python package for analysis of functional connectivity data.
It is premised on dividing the connectome into network components and fitting an independent model for each component.See our GitHub repo:GitHub repoSee also our paper: Bogdan, P.C., Iordan, A. D., Shobrook, J., & Dolcos, F. (2023). ConnSearch: A Framework for Functional Connectivity Analysis Designed for Interpretability and Effectiveness at Limited Sample Sizes. NeuroImage, 120274.
Package: conntest
This library helps you check credentials.It support the following protocols:RDPSSHVNCVMware vCenter AuthUsageimport conntest
conntest.ssh_connection(
    '127.0.0.1', username='root', password='something', port=22
)
conntest.rdp_connection(
    '127.0.0.1', port=3389, username='Administrator',
    domain='example.com', password='something'
)
conntest.vnc_connection(
    '127.0.0.1', port=5900, password='something'
)
conntest.vcenter_connection(
    '127.0.0.1', port=443, username='administrator',
    domain='vsphere.local', password='something'
)
Package: conntextual
conntextual (1.3.2)A network-application TUI using textual.DocumentationGeneratedBysphinx-apidoc(What'ssphinx-apidoc?)Bypydoc(What'spydoc?)Python Version SupportThis package is tested with the following Python minor versions:python3.11python3.12Platform SupportThis package is tested on the following platforms:ubuntu-latestmacos-latestIntroductionCommand-line Options$ ./venv3.11/bin/conntextual -h

usage: conntextual [-h] [--version] [-v] [-q] [--curses] [--no-uvloop]
                   [-C DIR]
                   {client,ui,noop} ...

A network-application TUI using textual.

options:
  -h, --help         show this help message and exit
  --version          show program's version number and exit
  -v, --verbose      set to increase logging verbosity
  -q, --quiet        set to reduce output
  --curses           whether or not to use curses.wrapper when starting
  --no-uvloop        whether or not to disable uvloop as event loop driver
  -C DIR, --dir DIR  execute from a specific directory

commands:
  {client,ui,noop}   set of available commands
    client           attempt to connect a client to a remote session
    ui               run a user interface for runtimepy applications
    noop             command stub (does nothing)Internal Dependency GraphA coarse view of the internal structure and scale ofconntextual's source.
Generated usingpydeps(viamk python-deps).
Package: conoha
conohaA command-line interface to the ConoHa.conohaはConoHaをコマンドライン上で操作するために作られたCLIツールです。RequirementsPython 3.0Installation以下のコマンドを実行することで conoha コマンドが利用可能になります。:$ pip3 install conohaConfiguration以下のコマンドを実行することで初期設定が完了します。:$ conoha
username: XXXX
password: XXXX
tenant_id: XXXX
Authentication success.初期設定を行った内容は ~/.conoha/config にTOML形式で保存されます。※ユーザ情報が登録されているリージョンは自動で判定されます。Usage各コマンドには様々なオプションが存在し –help オプションで調べることができます。VM一覧取得:$ conoha compute vm list
$ conoha compute vm list --outline
$ conoha compute vm list --textVMプラン一覧取得:$ conoha compute flavor list
$ conoha compute flavor list --outline
$ conoha compute flavor list --textイメージ一覧取得(nova):$ conoha compute image list
$ conoha compute image list --outline
$ conoha compute image list --textVM作成:$ conoha compute vm create -i [IMAGE_ID] -f [FLAVOR_ID] --password [ADMIN_PASSWORD]VM起動:$ conoha compute vm up [VM_ID]VM再起動:$ conoha compute vm reboot [VM_ID]VM通常停止:$ conoha compute vm stop [VM_ID]VM強制停止:$ conoha compute vm stop [VM_ID] --forceLicenseMIT
Package: conoha-cli
conoha-cli注意:
このプロジェクトは長い間メンテナンスされていません。
conoha-cliの代わりにpython-openstackclientを使うことをお勧めします。
他の選択肢はgiginc/conohaです。もしメンテナンスを引き継ぎたい方がいらっしゃったら、私に連絡をしてください。conoha-cliはConoHaAPI のコマンドラインインターフェースとPython3用ライブラリです。インストールconoha-cliはこのコマンドを実行するとインストールでいます:$ pip3 install conoha-cli次に、~/.config/conoha/configへこのようなファイルを作成してください。
user, passwd, tenantは、Conohaコントロールパネルから作成したAPIユーザの値を入力してください。:[api]
user = xxxxx
passwd = xxxxx
tenant = xxxxx使い方VPSの一覧を確認する:$ conoha-cli compute list-vmsマシンリーダブルな形式でも出力できます:$ conoha-cli --format plain --header no compute list-vmsVPSを起動・終了・削除する:$ conoha-cli compute start-vm $VM_NAME
$ conoha-cli compute stop-vm  $VM_NAME
$ conoha-cli compute delete-vm $VM_NAMEライセンスMIT
Package: conpagnon
ConPagnonConPagnon is a Python module designed to facilitate the analysis
of Resting State data.It leverages, among others, thescikit-learnPython toolbox,
and thenilearnPython toolbox. ConPagnon
also contain numerous utilities for manipulating text data, visualizing
and plotting results.Important linksOfficial source code repo:https://github.com/ConPagnon/conpagnonHTML documentation (stable release):https://conpagnon.github.io/conpagnon/index.htmlDependenciesThe required dependencies to use the software are:Python >= 3.6,setuptoolsNumpySciPyScikit-learnJoblibNibabelPyPDF2WebcolorsSeabornPandasStatsmodelsPsutilNetworkxPatsyxlrdtabulateInstallFirst make sure you have installed all the dependencies listed above.
Then you can install ConPagnon by running the following command in
a command prompt:pip install conpagnonMore detailed instructions are available athttps://conpagnon.github.io/conpagnon/install.html.ContributorsDhaif BEKHA
Package: conpass
ConPassPython tool for continuous password spraying taking into account the password policy.ChaptersDescriptionWarningBefore using this tool, read thisInstallationLsassy installationUsageConPass usageWarningAlthough I have made every effort to make sure the tool get the correct password policy, there can be some password policy settings that are not taken into account by the tool, which may lead to accounts lockout.Installationconpassworks with python >= 3.7pip (Recommended)python-mpipinstallconpassFrom source for developmentpython setup.py installUsageconpasswill get all domain users and try a list of password provided in a password file. When a user can be locked out, the tool will wait for the lockout reset period before trying another password.conpass-ddomain-upixis-pP4ssw0rd-P/tmp/passwords.txtAll passwords and NT hashes provided in/tmp/passwords.txtwill be added to a testing Queue, and will be tested against all users, whenever it is possible without locking users out.
Package: conport
conportconportis an Python tool for generating continuous regression testing reports. It supports both HTML report and email report. In each report, testing build trend, testing build metrics and test case summary are demonstrated. Both English report and Chinese report are supported. Check thedemoas an example.公众号文章How to installUse pip to install:pipinstallconportYou can also clone the repo, and inside the directory, run:pythonsetup.pydevelopnote: when install conport, all denpendent libs specified inrequirements.txtwill be automatically installed. Due to the requirement of dependencymatplotlib, you may need to install Linux librarypython-tkif necessary. In ubuntu, you can run commandapt install python-tkto install it.How to useAfter install conport successfully, shell commandconportwill be avaiable.Check version ofconport:conport--versionGet help ofconport:comport-hAvailable arguments can be found from help command output:usage:conport[-h][--job_urlJOB_URL][--report_titleREPORT_TITLE][--past_hoursPAST_HOURS][--send_emailSEND_EMAIL][--mail_hostMAIL_HOST][--mail_userMAIL_USER][--mail_pwdMAIL_PWD][--senderSENDER][--receiversRECEIVERS][--receivers_ccRECEIVERS_CC][--pure_htmlPURE_HTML][--version]optionalarguments:-h,--helpshowthishelpmessageandexit--job_urlJOB_URLJenkinsjoburl--report_titleREPORT_TITLEcontinuoustestingreporttitle--past_hoursPAST_HOURSnumberofpasthourstobemonitored--send_emailSEND_EMAILwhethertosendemailornot--mail_hostMAIL_HOSTemailhost--mail_userMAIL_USERemailuser--mail_pwdMAIL_PWDemailpassword--senderSENDERemailsender--receiversRECEIVERSemailreceivers,formatisreceiver1,receiver2,...--receivers_ccRECEIVERS_CCemailreceiverscc,formatisreceivercc1,receivercc2,...--pure_htmlPURE_HTMLpurehtmlornot--report_lanREPORT_LANreportlangurage,englishorchinese,defaultisenglish--versionprintversionThe arguments, if not speficied, will be fetched from thedefault configuration file. Anyway, for your specific configuration, these arguments needs to be given.note: parameterpure_htmlis needed because email cannot support HTML5 figure. In that case,conportcreate figure by usingmatplotliband then embed that figure into email message withemail.MIMEImage.demoThe following demo picture is created from command:conport--job_urlhttp://test-jenkins:8080/job/demo-job-conport--past_hours8--report_title"Here is regression testing report for demo usage of conport"--pure_htmlfalse--send_emailtrueYou can obtain some key information from the picture:Tetsing build trendTesting build metricsTest case summary, with a focus on failed cases statisticsEnglish demoChinese demo(中文示例)How to developFirst installtoxwith:pipinstalltoxRun UT and coverage:python-mpytest-vv-s--cov=conport--cov-reportterm--cov-reporthtmlRun tox:tox-etestLocal install and test:pythonsetup.pydevelopLisenseMITMaintenanceThis tool is developed byslxiao. You are welcome to raise anyissuesabout the tool.
Package: conpy
ConpyCall python functions on (CERN) HTCondor batch cluster - with tqdm progress bars.How to useHigh-level interface use:import conpy

tasks = [...] # list of dicts with keys ["task", "args", "kwargs"] run on each node as task(*args, **kwargs)

results = conpy.local_submit(tasks)
results = conpy.mp_submit(tasks, ncores=4)
results = conpy.sge_submit("name", "/tmp/conpy-temporaries", tasks=tasks, options="-q hep.q")The return value is a list of results for each task, in order o tasks.
Package: conpygure
ConpygureA library to conpygure little projects :)Conpygure is a library that eases the burden of configuring little python projects.
Configurator objects are classes defined extending the conpygure.Conf class.
Defining your configuration is as easy as:>>> import conpygure as c
>>>
>>> class A(c.Conf):
>>>     option_one = "default_value"
>>>     option_two = 2
>>>
>>> @c.subconfig_of(A)
>>> class B(c.Conf):
>>>     option_three = ["ab", "cd"]
>>>     option_four = {"a":1}To print your serialized configuration:>>> print(c.dumps())Dump and load your config to file:>>> c.dump("/path/to/conf.toml")
>>> c.load("/path/to/conf.toml")Serializers just need to define dumps and loads methods:>>> import json
>>> c.serializer = json
Package: conqur
ConQuRBatch effects removal for microbiome data via conditional quantile regression on Python.Free software: MIT licenseDocumentation:https://conqur.readthedocs.io.DescriptionWe implement in Python the batch effect removal algorithm described inLing_et_al.Implementationused in the paper is written in R. This implementation attempts to
be as close as possible, but has some new features (see Features for details).FeaturesThis implementation supportsusing non-negative float features, batch and covariate variables;using logistic regression with an L2 penalty;using arbitrary quantiles in quantile regression.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History1.0.0 (2022-08-03)First release on PyPI.
Package: conrad-relaycard
Control Conrad relaycard via pythonhttps://www.conrad.com/p/conrad-components-197720-relay-card-component-12-v-dc-24-v-dc-197720Usagefromconrad_relaycardimportRelaycardrly=Relaycard()rly.setup()rly.get_port(1,1)rly.set_port(1,1,True)via CLIusage: conrad-relaycard [-h] [-v] [-q] [-i INTERFACE] [-a ADDRESS] [-p PORT] [--scan] [--get-ports] [--set-ports STATE] [--toggle-ports]

options:
  -h, --help            show this help message and exit
  -v, --verbose         Output verbosity
  -q, --quiet           Minimized output (allow easier parsing)
  -i INTERFACE, --interface INTERFACE
                        Serial interface to use
  -a ADDRESS, --address ADDRESS
                        Relaycard address (not needed for --scan)
  -p PORT, --port PORT  Ports to get/set (only for some commands)
  --scan                Scan for relay cards
  --get-ports           Get port states on relay card
  --set-ports STATE     Set port states on relay card <on/off>
  --toggle-ports        Toggle port states on relay card
Package: conrich
conrichWhat's conrich for?conrich為康和綜合證券程式下單API，整合了.net framework 4.5架構下的交易介面，讓使用者可以輕鬆的運用python 3.6+ 執行快速下單。conrichis a securities, futures, option API, which send orders to Concord Securities Group.
This package integrated with .net framework 4.5, and is compatible with Python 3.6+.
It is distributed under the MIT license.Package DocumentMore information aboutconrichis available atconrich_docs.RequirementsUsing package requires:WindowsPython3.6+康和金好康.net framework 4.5+InstallationRefer toInstall steps.Summarized TableThe functions which are provided inconrichare summarized in the below link.Functions summarizedReferenceClick these two links can get the zip file of Stock API and Futures API. The zip file includes the C# example and the relative document of C# API.Stock API zip fileFutures API zip file
Package: cons
PythonconsAn implementation ofconsin Python.Usage and DesignTheconspackage attempts to emulate the semantics of Lisp/Scheme'sconsas closely as possible while incorporating all the built-in Python sequence types:>>>fromconsimportcons,car,cdr>>>cons(1,[])[1]>>>cons(1,())(1,)>>>cons(1,[2,3])[1,2,3]In general,consis designed to work withcollections.abc.Sequencetypes.According to theconspackage,Nonecorresponds to the empty built-inlist, asnildoes in some Lisps:>>>cons(1,None)[1]Theconspackage follows Scheme-like semantics for empty sequences:>>>car([])Traceback(mostrecentcalllast):File"<stdin>",line1,in<module>ConsError:Notaconspair>>>cdr([])Traceback(mostrecentcalllast):File"<stdin>",line1,in<module>ConsError:NotaconspairBy default,strtypes are not considered cons-pairs, although they are sequences:>>>cons("a","string")ConsPair('a''a string')This setting can be overridden and other types can be similarly excluded from consideration by registering classes with theabc-based classesMaybeConsandNonCons.FeaturesBuilt-in support for the standard Python ordered sequence types: i.e.list,tuple,Iterator,OrderedDict.>>>fromcollectionsimportOrderedDict>>>cons(('a',1),OrderedDict())OrderedDict([('a',1)])Existingconsbehavior can be changed and support for new collections can be added through the generic functionscons.core._carandcons.core._cdr.Built-in support forunification.>>>fromunificationimportunify,reify,var>>>unify([1,2],cons(var('car'),var('cdr')),{}){~car:1,~cdr:[2]}>>>reify(cons(1,var('cdr')),{var('cdr'):[2,3]})[1,2,3]>>>reify(cons(1,var('cdr')),{var('cdr'):None})[1]InstallationpipinstallconsDevelopmentFirst obtain the project source:gitclonegit@github.com:pythological/python-cons.gitCreate a virtual environment and install the development dependencies:$pipinstall-rrequirements.txtSet uppre-commithooks:$pre-commitinstall--install-hooks
Package: consciousnet
Module providing Consciousness Exploration Tools for PyTorch.Home-page:https://github.com/neurospin-deepinsight/consciousnetAuthor:
consciousnet developersAuthor-email:antoine.grigis@cea.frLicense: CeCILL-B
Description: Module providing Consciousness Exploration Tools for PyTorch.Platform: OS Independent
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: X11 Applications :: Qt
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Utilities
Package: conscommon
Common module for Sirius applicationsCommon features for Sirius scripts.
Available atPyPihttps://pypi.org/project/conscommon/DataWeb API interface.Data ModelData model.SpreadsheetXLSX parser, this module should be avoided in favor of the WEB API, usefull in applications that will deal directly with the spreadsheet. Requirespandasandxlrd.
Package: conscript
Conscript: console scripts in your own Swiss Army Knife.Conscript provides a console script you can use to conveniently expose all other console scripts in
a virtual environment. In a traditional virtual environment this is of little use. In azipapp, it gives you
capabilities similar toBusyBox.UseTo create a BusyBox, simply addconscriptto your dependencies and set your application main
entrypoint to theconscriptconsole script.ExamplesExamples are probably the best way to demonstrate the utility of Conscript. BothPexandShivare popular tools
for creating zipapps with third party dependencies; so they lead to concise examples.PexCreate the BusyBox PEX zipapp:$pexcowsayfortuneconscript--scriptconscript--output-filespeakExamine the available embedded apps:$./speak-husage: speak [-h] [PROGRAM]A speak busy box.positional arguments:PROGRAM     The program to execute.The following programs are available:+ cowsay+ fortuneoptional arguments:-h, --help  Show this help message and exit.Run an embedded app:$./speakcowsay"Conscript is my Swiss Army Knife!"_________________________________| Conscript is my Swiss Army Knife! |=================================\\^__^(oo)\_______(__)\       )\/\||----w |||     ||Or another one:$./speakfortune-hUsage: speak [OPTIONS] [fortune_file]Options:-h, --help     show this help message and exit-V, --version  Show version and exit.If fortune_file is omitted, fortune looks at the FORTUNE_FILE environmentvariable for the path.$./speakfortune/usr/share/fortune/scienceNondeterminism means never having to say you are wrong.Target an embedded app via a symlink:$ln-sspeakcowsay$./cowsayNifty.______| Nifty. |======\\^__^(oo)\_______(__)\       )\/\||----w |||     ||ShivThe capabilities exposed by Conscript are the same as in the Pex example, so this example is
abbreviated to the basics. The primary difference is that Shiv does not fully isolate requested
dependencies in the zipapp from incidental dependencies and so we see more available console
scripts than in the Pex case.Create the BusyBox shiv zipapp:$shivcowsayfortuneconscript--python'/usr/bin/env python'--console-scriptconscript--output-filesayCollecting cowsayUsing cached cowsay-4.0-py2.py3-none-any.whl (24 kB)Collecting fortuneUsing cached fortune-1.1.0-py2.py3-none-any.whl (5.9 kB)Collecting conscriptUsing cached conscript-0.1.1-py2.py3-none-any.whl (7.5 kB)Collecting grizzled-python>=1.0Using cached grizzled_python-2.2.0-py2.py3-none-any.whl (36 kB)Installing collected packages: grizzled-python, fortune, cowsay, conscriptSuccessfully installed conscript-0.1.1 cowsay-4.0 fortune-1.1.0 grizzled-python-2.2.0Use it:$./say-husage: say [-h] [PROGRAM]A say busy box.positional arguments:PROGRAM     The program to execute.The following programs are available:+ cowsay+ easy_install+ easy_install-3.8+ fortune+ pip+ pip3+ pip3.8+ shiv+ shiv-infooptional arguments:-h, --help  Show this help message and exit.$./saycowsay--version4.0
Package: conseal
consealPython package, containing implementations of modern image steganographic algorithms.:warning: The package only simulates the embedding, which is useful for steganalysis research. We do not provide any end-to-end steganography method.InstallationSimply install the package with pip3pip3installconsealor using the cloned repositorygitclonehttps://github.com/uibk-uncover/conseal/cdconseal
pip3install.UsageImport the library in Python 3importconsealasclThis package currently contains the three JPEG steganography methods J-UNIWARD, UERD, and nsF5. The following examples show how to embed a JPEG cover imagecover.jpegwith an embedding rate of 0.4 bits per non-zero AC coefficient (bpnzAC):J-UNIWARD# load coverim_spatial=jpeglib.read_spatial("cover.jpeg",jpeglib.JCS_GRAYSCALE)im_dct=jpeglib.read_dct("cover.jpeg")# embed J-UNIWARD 0.4im_dct.Y=cl.juniward.simulate_single_channel(cover_spatial=im_spatial.spatial[...,0],cover_dct_coeffs=im_dct.Y,quantization_table=im_dct.qt[0],embedding_rate=0.4,seed=12345)# save result as stego imageim_dct.write_dct("stego.jpeg")UERD# load coverim_dct=jpeglib.read_dct("cover.jpeg")# embed UERD 0.4im_dct.Y=cl.uerd.simulate_single_channel(cover_dct_coeffs=im_dct.Y,quantization_table=im_dct.qt[0],embedding_rate=0.4,seed=12345)# save result as stego imageim_dct.write_dct("stego.jpeg")nsF5# load coverim_dct=jpeglib.read_dct("cover.jpeg")# embed nsF5 0.4im_dct.Y=cl.nsF5.simulate_single_channel(cover_dct_coeffs=im_dct.Y,quantization_table=im_dct.qt[0],embedding_rate=0.4,seed=12345)# save result as stego imageim_dct.write_dct("stego.jpeg")Acknowledgements and DisclaimerDeveloped byMartin BenesandBenedikt Lorch, University of Innsbruck, 2023.The J-UNIWARD and nsF5 implementations in this package are based on the original Matlab code provided by the Digital Data Embedding Lab at Binghamton University. We also thank Patrick Bas and Rémi Cogranne for sharing their implementations of UERD with us.We have made our best effort to ensure that our implementations produce identical results as the original Matlab implementations. However, it is the user's responsibility to verify this.
Package: consensual-http
consensual_httpIn what followspythonis an alias forpython3.7or any later version (python3.8and so on).InstallationInstall the latestpip&setuptoolspackages versionspython-mpipinstall--upgradepipsetuptoolsUserDownload and install the latest stable version fromPyPIrepositorypython-mpipinstall--upgradeconsensual_httpDeveloperDownload the latest version fromGitHubrepositorygitclonehttps://github.com/lycantropos/consensual_http.gitcdconsensual_httpInstall dependenciespython-mpipinstall-rrequirements.txtInstallpythonsetup.pyinstallUsage>>>fromconsensual.raftimportNode>>>fromconsensual_httpimportcommunication>>>fromyarlimportURL>>>node_url=URL.build(scheme='http',...host='localhost',...port=6000)>>>heartbeat=0.1>>>fromtypingimportAny>>>processed_parameters=[]>>>defdummy_processor(parameters:Any)->None:...processed_parameters.append(parameters)>>>defstop(parameters:Any=None)->None:...receiver.stop()>>>processors={'dummy':dummy_processor,'stop':stop}>>>sender=communication.Sender(heartbeat=heartbeat,...urls=[node_url])>>>fromasyncioimportEvent,get_event_loop>>>loop=get_event_loop()>>>node=Node.from_url(node_url,...heartbeat=heartbeat,...loop=loop,...processors=processors,...sender=sender)>>>node_is_running=Event()>>>receiver=communication.Receiver(node,...on_run=node_is_running.set)>>>fromaiohttp.clientimportClientSession>>>defvalidate_response(response:Any)->None:...assertisinstance(response,dict)...assertresponse.keys()=={'error'}...assertresponse['error']isNone>>>asyncdefrun()->None:...awaitnode_is_running.wait()...asyncwithClientSession(node.url)assession:...validate_response(await(awaitsession.post('/')).json())...validate_response(await(awaitsession.post('/dummy',...json=42)).json())...validate_response(await(awaitsession.delete('/',...json=[str(node.url)])).json())...validate_response(await(awaitsession.delete('/')).json())...stop(None)>>>_=loop.create_task(run())>>>receiver.start()>>>all(parameters==42forparametersinprocessed_parameters)TrueDevelopmentBumping versionPreparationInstallbump2version.Pre-releaseChoose which version number category to bump followingsemver
specification.Test bumping versionbump2version--dry-run--verbose$CATEGORYwhere$CATEGORYis the target version number category name, possible
values arepatch/minor/major.Bump versionbump2version--verbose$CATEGORYThis will set version tomajor.minor.patch-alpha.ReleaseTest bumping versionbump2version--dry-run--verbosereleaseBump versionbump2version--verbosereleaseThis will set version tomajor.minor.patch.Running testsInstall dependenciespython-mpipinstall-rrequirements-tests.txtPlainpytestInsideDockercontainer:docker-composeupBashscript:./run-tests.shPowerShellscript:.\run-tests.ps1
Package: consensusclustering
Consensus ClusteringDescriptionconsensusclusteringis a python implementation of the Consensus Clustering
algorithm for finding the optimal number of clusters by means of clustering
stability under resampling, as originally proposed byMonti et al. Consensus
Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene
Expression Microarray Data (2003).Installationconsensusclusteringcan be installed from PyPi using pip:pipinstallconsensusclusteringUsageSee the 'examples' directory for a detailed overview of the package's functionality.LicenseCytotransform is licensed under the MIT license, is free to use, and comes with no warranty whatsoever.