Saravanan666/pypi-org huggingface dataset:
Package: sala
Sala lets you store passwords and other bits of sensitive plain-text
information to encrypted files on a directory hierarchy. The
information is protected by GnuPG’s symmetrical encryption.Basic usagePasswords are stored in a directory hierarchy, each file containing
one secret.Commands:sala initInitialize a password storesala getFILE...Read secret(s)sala setFILE...Create or modify secret(s)salaFILE...Read or modify, depending on whether the first file exists or notFor more information, see sala(1) andhttp://www.digip.org/sala/.InstallationInstall sala by invoking:pip install salaTo install from source, invoke:python setup.py installRequirements:Python2.6, 2.7, or 3.2 (or newer 3.x).GnuPGSuggested packages:pwgen: With the default configuration, ifpwgenis installed,
it’s used to suggest good passwords to the userDevelopmentThe test suite is in thetest/directory. Use therun-tests.pyscript to run the test suite. For more info, invoke:python run-tests.py --helpcram0.5 or newer is required to run the tests. Ifcoverageis
installed, the script can also show test coverage.The documentation is in thedoc/directory. To build a manpage
and a HTML documentation page, invokemake -C docin the top directory.Docutils0.8 or newer is required.Release historyVersion 1.3Released on 2012-06-13Respect SALADIR when creating subdirectories with ‘set’.Add a bash completion script: contrib/sala-completion.bash.Move the repository state to the .sala subdirectory. .salakey
becomes .sala/key and sala.conf becomes .sala/config.Add support for hooks in .sala/hooks. Initially, the post-set hook
is supported.Version 1.2Released on 2011-12-31Add -r option for raw output format.Allow setting the store location with SALADIR environment variable
and -C command line option.Add support for Python 3.2, drop support for Python 2.5.Drop the GnuPGInterface dependency. Sala now implements its own
GnuPG wrapper.Add man page (in ReST format), move most of README there.Improve portability.Add a regression test suite (cram is required to run the tests).Version 1.1Released on 2011-02-02Add support for Python 2.5.Read configuration from$XDG_CONFIG_HOME/sala.conf, too.Version 1.0.1Released on 2011-01-19Distribute README.rst, LICENSE and CHANGES with the source tarball.Version 1.0Released on 2011-01-18Initial release.
Package: salabim
salabimObject oriented discrete event simulation and animation in Python.Discrete event simulation (DES) can be used in a variety of logistics applications:production facilitieswarehousing(air)portshospitalsminingmaterials handlingsteel mills, but also in other areas, like computer network analysis.Salabim follows a well proven and very intuitive process description method (like Tomas and Must).
The package providescomponentsqueuesresourcesstoresstatesmonitors for data collection and presentation2D animation (including video production)3D animation (including video production)tracing facilitiesadvanced statistical samplingIn contrast to some other Python DES packages it is not required to use yield statements for the process control.
Salabim has very minimal requirements (without animation none).Salabim runs under Windows, MacOS, Linux, iOS/iPadOS (Pythonista) and can be used with "Python In Excel".Seewww.salabim.orgfor details.Seewww.salabim.org/manualfor the documentation.
Package: saladpy
SaladPyKey FeaturesModern async API usingasyncio.Allows you to access all the Salad web API endpointsSupports token cachingInstallingTo install the library, you can just run the following command:# Linux/macOSpython3-mpipinstall-Usaladpy# Windowspy-3-mpipinstall-UsaladpyQuick Exampleimportsaladpyimportasyncioasyncdefmain():client=saladpy.SaladClient()verify=awaitclient.login("john.doe@example.com")# Verify using the OTP sent to your emailawaitverify(input("Enter OTP: "))# Get Balancebalance=awaitclient.balance()print("Your Salad balance is",balance)# Safely close SaladClient (Highly Recommended)awaitclient.close()asyncio.run(main())DocsThe docs are available athttps://saladpy.gitbook.io/saladpy-docs/.ContactYou can contact me atCoddo#3210on Discord! Ensure to first send me a friend request.
Package: salahs-twitterbot
Twitter Bottwitter botdelete tweetsget trending tweetsInstallpip install salahs-twitterbot[scripts]Twitter APIcreate a newtwitter applicationsave consumer key, consumer secret, access  key and access secret as environment variablescat <<EOF > keys.env
export TWITTER_CONSUMER_KEY='aaaaaaaaaaaaaaaaaaaaaaaaa'
export TWITTER_CONSUMER_SECRET='aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa'
export TWITTER_ACCESS_TOKEN='aaaaaaaaaa-aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa'
export TWITTER_ACCESS_TOKEN_SECRET='aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa'
EOF
source keys.envdelete_old_tweets --helpget_trending_tweets --helpremove_favorites --help
Package: salal
Salal is a Python package to manage building websites from templates and content, much like you might handle building of a C++ program usingMake, or building of a Java program usingMaven.Installationpip install salalBasic usageChoose a directory to hold all your site files.Add aconfigdirectory, and in that put aprofiles.jsonfile to configure how the site is built.Add adesigndirectory. Create atemplatessubdirectory, and populate that with the template files you want to use.Make acontentdirectory, and populate it with source files for the pages of your site.Run Salal to build the site, for example,python -m salal build production.DocumentationFull documentation and tutorials can be found at theSalal Documentation Wiki.
Package: salam
A Python tool for High-Throughput Virtual Screening of organic molecules driven by structural mutation and machine learning.!PyPI version!Documentation statusDocumentation|Colab Tutorial|Table of contents:RequirementsInstallationGetting StartedAbout UsCiting SALAMRequirementsSALAM currently supports Python 3.7 and later versions, and requires these packages on any condition.joblibNumPypandasscikit-learnSciPyrdkitDeepChemThe source for this project is available here.InstallationSALAM can be installed using pip aspipinstallsalamGetting StartedThe SALAM project maintains a collection oftutorials.Typical usage is:pythonsalamsalam_paras.inp&>salam.logfile&About UsSALAM is currently maintained by C. J. Tu. Anyone is free to join and contribute!Citing SALAMIf you have used SALAM in the course of your research, we ask that you cite it.There is still no official SALAM publication, the recommended citation is:SALAM: a HTVS tool for organic materials.https://github.com/yidapa/salam
Package: salamander-learn
SalamanderSalamander is a non-negative matrix factorization (NMF) framework for signature analysis.
It implements multiple NMF algorithms, common visualizations, and can be easily customized & expanded.InstallationPyPI:pipinstallsalamander-learnUsageThe following example illustrates the basic syntax:importpandasaspdimportsalamander# samples and features have to be named appropriatelydata_path="..."data=pd.read_csv(data_path,index_col=0)# NMF with a Poisson noise modelmodel=salamander.KLNMF(n_signatures=5)model.fit(data)# barplotmodel.plot_signatures()# stacked barplotmodel.plot_exposures()# signature correlationmodel.plot_correlation()# sample_correlationmodel.plot_correlation(data="samples")# dimensionality reduction of the exposures# method: umap, pca or tsnemodel.plot_embeddings(method="umap")For examples of how to customize any NMF algorithm and the plots, check outthe tutorial. The following algorithms are currently available:NMF with KL-divergence lossminimum-volume NMFa variant of correlated NMFLicenseMITChangelogConsult theCHANGELOGfile for enhancements and fixes of each version.
Package: salamemojify
salamemojify :) 😊DescriptionObfuscate your python script (or indeed any plain text file) by converting an input script to an output script that functions the same (hopefully) but encodes the code as emoji icons, currently emoticons or emojis.Installationpip install salamemojifyIf installation fails withpipfor some reason, you can just clone the repository, since there are no dependancies apart from python 3:Usagesalamemojify --input input_script.py --output output_script.pyFor help:salamemojify --helpFor unit testing:test_salamemojifyExampleinput_script.pycontains:print('hello world')
def add(n1,n2):
    return n1 + n2
print('4 + 4 = {}'.format(add(4,4)))runningsalamemojify --input input_script.py --output output_script.pyoutput_script.pycontains:
from salamemojify import *
exec("".join(map(chr,[int("".join(str({':(': 4,
':)': 0,
':/': 7,
':D': 1,
':P': 2,
':S': 3,
':{': 8,
';)': 9,
'=)': 5,
'=/': 6}[i]) for i in x.split())) for x in
":D :D :P  :D :D :(  :D :) =)  :D :D :)  :D :D =/  :( :)  :S ;)  :D :):(  :D :) :D  :D :) :{  :D :) :{  :D :D :D  :S :P  :D :D ;)  :D :D :D:D :D :(  :D :) :{  :D :) :)  :S ;)  :( :D  :D :)  :D :) :)  :D :) :D:D :) :P  :S :P  ;) :/  :D :) :)  :D :) :)  :( :)  :D :D :)  :( ;):( :(  :D :D :)  =) :)  :( :D  =) :{  :D :)  :S :P  :S :P  :S :P  :S :P  :D :D :(  :D :) :D  :D :D =/  :D :D :/  :D :D :(  :D :D :)  :S :P:D :D :)  :( ;)  :S :P  :( :S  :S :P  :D :D :)  =) :)  :D :)  :D :D :P:D :D :(  :D :) =)  :D :D :)  :D :D =/  :( :)  :S ;)  =) :P  :S :P:( :S  :S :P  =) :P  :S :P  =/ :D  :S :P  :D :P :S  :D :P =)  :S ;)  :( =/  :D :) :P  :D :D :D  :D :D :(  :D :) ;)  ;) :/  :D :D =/  :( :);) :/  :D :) :)  :D :) :)  :( :)  =) :P  :( :(  =) :P  :( :D  :( :D  :( :D  :D :)"
.split("  ")])))runningpython output_script.pyoutputs:hello world
4 + 4 = 8similarly, runningsalamemojify --input input_script.py --output output_script.py --emojioutput_script.pycontains:
from salamemojify import *
exec("".join(map(chr,[int("".join(str({'😀': 0, '😁': 3, '😂': 6, '😃': 1, '😄': 2, '😅': 4, '😉': 7, '😊': 8, '😛': 9, '🤣': 5}[i]) for i in x.split())) for x in
"😃 😃 😄  😃 😃 😅  😃 😀 🤣  😃 😃 😀  😃 😃 😂  😅 😀  😁 😛  😃 😀 😅  😃 😀 😃  😃 😀 😊  😃 😀😊  😃 😃 😃  😁 😄  😃 😃 😛  😃 😃 😃  😃 😃 😅  😃 😀 😊  😃 😀 😀  😁 😛  😅 😃  😃 😀  😃 😀 😀😃 😀 😃  😃 😀 😄  😁 😄  😛 😉  😃 😀 😀  😃 😀 😀  😅 😀  😃 😃 😀  😅 😛  😅 😅  😃 😃 😀  🤣😀  😅 😃  🤣 😊  😃 😀  😁 😄  😁 😄  😁 😄  😁 😄  😃 😃 😅  😃 😀 😃  😃 😃 😂  😃 😃 😉  😃 😃😅  😃 😃 😀  😁 😄  😃 😃 😀  😅 😛  😁 😄  😅 😁  😁 😄  😃 😃 😀  🤣 😀  😃 😀  😃 😃 😄  😃 😃😅  😃 😀 🤣  😃 😃 😀  😃 😃 😂  😅 😀  😁 😛  🤣 😄  😁 😄  😅 😁  😁 😄  🤣 😄  😁 😄  😂 😃😁 😄  😃 😄 😁  😃 😄 🤣  😁 😛  😅 😂  😃 😀 😄  😃 😃 😃  😃 😃 😅  😃 😀 😛  😛 😉  😃 😃 😂  😅😀  😛 😉  😃 😀 😀  😃 😀 😀  😅 😀  🤣 😄  😅 😅  🤣 😄  😅 😃  😅 😃  😅 😃  😃 😀"
.split("  ")])))and runningoutput_script.pyouputs the same as above.DisclaimerOnly tested on Python 3+ and on CPython. Not tested on complex scripts, so don't rely on this script to work, not guaranteed to work at all, and it is probably easy to break. One case it will fail is with non-unicode characters. Also note, if you want to securely protect your code from the eyes of others, this is NOT a good way.
Package: salamencrypt
salamencryptIt is a useful project for developers that includes useful tools for Encrypt & Run CodeInstallation :pip install salamencryptEncrypt Usagefrom salamencrypt import *
code = '''
print('Dev : @T5B55')
'''
enc = salamencrypt.enc(code)
print(enc)Run Code Usagefrom salamencrypt import *
runn = salamencrypt.run(b'\xe3\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00@\x00\x00\x00s\x0c\x00\x00\x00e\x00d\x00\x83\x01\x01\x00d\x01S\x00)\x02z\x0cDev : @T5B55N)\x01\xda\x05print\xa9\x00r\x02\x00\x00\x00r\x02\x00\x00\x00\xfa\x08<@T5B55>\xda\x08<module>\x02\x00\x00\x00\xf3\x00\x00\x00\x00')
print(runn)Follow us on social media
Package: salami
Salami: lightweight BSM NLO cross-sections, to goAbsolute cross-sections for BSM processes are an essential component,
along with analysis acceptances & efficiencies and collider luminosities,
of signal event-yield predictions in particle collider experiments.
Leading-order (LO) cross-sections can be calculated quickly, but next-to-LO
ones are hundreds of times slower to calculate: too slow for use in
large-scale parameter scan and fitting codes. Salami solves this problem
by rapid prediction of pre-trained NLO cross-sections as functions of the
SUSY/BSM spectrum.TODO:More careful treatment of negative masses (implies phases, not simply a modulus)
Package: salary-stone
Salary StoneThe purpose of this project is to host the methods related to the salary stone project. These include a skill extractor from text, a model that allows for prediction of and a variety of other method.Free software: Apache Software License 2.0Documentation:https://salary-stone.readthedocs.io.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.4.0 (2022-11-27)Add in the skill recommender module.Add testing for the skill recommender module.0.3.0 (2022-11-22)Add in the metrics module.Include a method to do skill frequency calculations.Include a method to do salary distribution by skill.Include a method to do a similarity score for a skill list passed in and return a list of job titles.0.2.3 (2022-11-21)fix the issue where the package wasn’t building due to Jinja / Markupplus.0.2.2 (2022-11-21)Fix issue with requirements not being correct.0.2.1 (2022-11-15)Fix issue where dependencies weren’t bundling.0.2.0 (2022-11-15)Add salary extractor.Add skill extractor.Add testing for the extractors.Add trained model.0.1.0 (2022-11-10)First release on PyPI.
Package: salary-timetracker
A handy tool with a command line interface for logging time worked. At
any time you can see how much you have spent time on a particular
project and how much you have earned on it.FeaturesTrack-file with data on time costs is created in theroot directory
of the project’s git repository, which you are working on at the
moment.Data is saved in**CSV**format.Requirements and restrictionsGit: The app will only work when you are inside a git repository.Python 3: The application was not tested to work under Python 2.Installationpipinstallsalary-timetrackerUsagett[-h][-s]{log}...positionalarguments:{log}logCreateanewtimetrackerlogrecordwithtimeandcomments(optional)optionalarguments:-h,--helpshowthishelpmessageandexit-s,--summaryShowsummary.ttlog[-h]minutes[comments[comments...]]positionalarguments:minutesTimeinminutesspentonwork.commentsCommensontheworkdone(optional)optionalarguments:-h,--helpshowthishelpmessageandexitThe app settings can be changed through the configuration file, which
should be located along the path:*~/.config/timetracker.conf*. And
to have the following format:[main]# default settingscurrency='USD'hourly_rate=20default_comment=''# comment when adding timedate_format='%d %b %Y'time_format='%H:%M'csv_delimiter=','ExamplesShow help:tt-hYou have worked for 2 hours and want to write this down:ttlog120'I did this'# Data was successfully addedShow summary:tt-s# Hours worked: 2.6 | Salary: 52 USD
Package: salary-timetracker-sqlite
A handy tool with a command line interface for logging time worked. At
any time you can see how much you have spent time on a particular
project and how much you have earned on it.Installationpipinstallsalary-timetracker-sqliteUsagetts[OPTIONS]Toaddanewentryjustenter'tts'.Iftherearenooptions,aninteractivesessionisstarted.Options:-a,--show-allShowallentries-p,--show-paidShowpaid-u,--show-unpaidShowunpaid-s,--statsShowunpaidhours-m,--mark-paidMarkallhoursworkedaspaid--del-paidDeleterecordsmarkedaspaid--helpShowthismessageandexit.
Package: saldet
saldetSaliencyDetection (saldet) is a collection of models and tools to perform Saliency Detection with PyTorch (cuda, mps, etc.).ModelsList of saliency detection models supported by saldet:U2Net -https://arxiv.org/abs/2005.09007v3(U2Net repo)PGNet -https://arxiv.org/abs/2204.05041(follow training instructions fromPGNet repo)PFAN -https://arxiv.org/pdf/1903.00179v2.pdf(PFAN repo)WeightsPGNet -> weights fromPGNet repoconverted to saldet version fromhereU2Net Lite -> weights fromhere(U2Net repository)U2Net Full -> weights fromhere(U2Net repository)U2Net Full - Portrait -> weights for portrait images fromhere(U2Net repository)U2Net Full - Human Segmentation -> weights for segmenting humans fromhere(U2Net repository)PFAN -> weights fromPFAN repoconverted to saldet version fromhereTo load pre-trained weights:fromsaldetimportcreate_modelmodel=create_model("pgnet",checkpoint_path="PATH/TO/pgnet.pth")TrainEasy ModeThe library comes with easy access to train models thanks to the amazing PyTorch Lightning support.fromsaldet.experimentimporttraintrain(data_dir=...,config_path="config/u2net_lite.yaml",# check the config folder with some configurationsoutput_dir=...,resume_from=...,seed=42)Once the training is over, configuration file and checkpoints will be saved into the output dir.[WARNING]The dataset must be structured as follows:dataset
    ├── train                    
    |       ├── images          
    |       │   ├── img_1.jpg
    |       │   └── img_2.jpg                
    |       └── masks
    |           ├── img_1.png
    |           └── img_2.png   
    └── val
           ├── images          
           │   ├── img_10.jpg
           │   └── img_11.jpg                
           └── masks
               ├── img_10.png
               └── img_11.pngPyTorch Lighting ModeThe library provides utils for model and data PyTorch Lightning Modules.importpytorch_lightningasplfromsaldetimportcreate_modelfromsaldet.plimportSaliencyPLDataModule,SaliencyPLModelfromsaldet.transformimportSaliencyTransform# datamoduledatamodule=SaliencyPLDataModule(root_dir=data_dir,train_transform=SaliencyTransform(train=True,**config["transform"]),val_transform=SaliencyTransform(train=False,**config["transform"]),**config["datamodule"],)model=create_model(...)criterion=...optimizer=...lr_scheduler=...pl_model=SaliencyPLModel(model=model,criterion=criterion,optimizer=optimizer,lr_scheduler=lr_scheduler)trainer=pl.Trainer(...)# fitprint(f"Launching training...")trainer.fit(model=pl_model,datamodule=datamodule)PyTorch ModeAlternatively you can define your custom training process and use thecreate_model()util to use the model you like.InferenceThe library comes with easy access to inference saliency maps from a folder with images.fromsaldet.experimentimportinferenceinference(images_dir=...,ckpt=...,# path to ckpt/pth model fileconfig_path=...,# path to configuration file from saldet trainoutput_dir=...,# where to save saliency mapssigmoid=...,# whether to apply sigmoid to predicted masks)To-Dos[ ] Improve code coverage[ ] ReadTheDocs documentation
Package: saleae
This library implements the control protocol for theSaleae Logic Analyzer. It is based off of the
documentation and example here:https://github.com/saleae/SaleaeSocketApiIMPORTANT: You must enable the ‘Remote Scripting Server’ in Saleae.Click
on “Options” in the top-right, the “Developer” tab, and check “Enable scripting
socket server”. This should not require a restart.This library requires Saleae Logic 1.2.x or greater. Unfortunately there is no
way to check the version of Logic running using the scripting protocol so this
is difficult to check at runtime.Currently, this is basically a direct mapping of API calls with some small
sanity checking and conveniences. It has not been extensively tested beyond
my immediate needs, but it also should not have any known problems.To get a feel for how the library works and what it can do, try the built-in demo:#!/usr/bin/env python3
import saleae
saleae.demo()Issues, updates, pull requests, etc should be directedto github.InstallationThe easiest method is to simply use pip:(sudo) pip install saleaeUsageimport saleae
s = saleae.Saleae()
s.capture_to_file('/tmp/test.logicdata')
Package: salem
Salem is a small library to do geoscientific data
processing and plotting. It extendsxarrayto add geolocalised
subsetting, masking, and plotting operations to xarray’sDataArrayandDataSetstructures.DocumentationA draft of documentation is hosted on ReadTheDocs:http://salem.readthedocs.ioLicenseSalem is available under the open source3-clause BSD license.AboutStatus:Beta - in developmentLicense:3-clause BSDContact:Fabien Maussion
Package: saleor-flat-tax-plugin
Saleor Flat Tax Pluginsaleor-flat-tax-pluginis a small plugin to enable flat taxes in Saleor.It's based out of Saleor'sVatlayerPlugin. Using the same
calculations but modified to use the taxes configured through the plugin and not take Country into consideration.Disclaimer: This project is not connected/endorsed by saleor's communityInstallationUsing pip:pipinstallsaleor-flat-tax-pluginOnce installed Saleor will automatically detect the plugin, and you'll be able to see it in Saleor's dashboard
Package: saleor-postfinance-plugin
Saleor PostFinance pluginPostFinance gateway plugin for Saleor. This is a partially implementation, it only supports
PostFinancepayment page integrationand it does not support refunds and void payments.Setup PostFinance PluginNavigate to Saleor Dashboard configuration and go to Plugins (Miscellaneous)Select the channel where you want to activate the plugin, fill in the  settings and set the
plugin as active. (Keep in mind some payment methods used in PostFinance (ex: Twint) will only
support CHF currency.)Steps to use the pluginCreate a checkout using the channel where the plugin is active.mutationcheckoutCreate{checkoutCreate(input:{channel:"switzerland-channel-example",email:...,shippingAddress:...,lines:[{quantity:1,variantId:"UHJvZHVjdFZhcmlhbnQ6Mzg0"}]}){checkout{id}}}Set checkout delivery method and set checkout billing address, for that you can usecheckoutDeliveryMethodUpdateandcheckoutBillingAddressUpdatemutations.Create checkout payment with PostFinance gateway ("significa.payments.postfinance")mutationcheckoutPaymentCreate{checkoutPaymentCreate(id:"Q2hlY2tvdXQ6NTE3M2ViNzAtNjMxOC00YzA3LTgwYzktY2VlNjkyMTdjZGE2",input:{gateway:"significa.payments.postfinance",}){payment{id}}}CallcompleteCheckoutmutation with the following payment data, to get the PostFinance payment url.mutationcheckoutComplete{checkoutComplete(id:"Q2hlY2tvdXQ6NTE3M2ViNzAtNjMxOC00YzA3LTgwYzktY2VlNjkyMTdjZGE2",paymentData:"{\"successUrl\":\"https://example.com/success?checkout_id=Q2hlY2tvdXQ6NTE3M2ViNzAtNjMxOC00YzA3LTgwYzktY2VlNjkyMTdjZGE2\",\"failUrl\":\"https://example.com/fail?checkout_id=Q2hlY2tvdXQ6NTE3M2ViNzAtNjMxOC00YzA3LTgwYzktY2VlNjkyMTdjZGE2\"}"){confirmationNeededconfirmationData}}# Response{"data":{"checkoutComplete":{"confirmationNeeded":true,"confirmationData":"{\"payment_page_url\":\"https://checkout.postfinance.ch/s/00000/payment/transaction/pay/10000000?securityToken=example-00000\"}",}}}Finally callcheckoutCompleteto confirm the payment was captured successfully.mutationcheckoutComplete{checkoutComplete(id:"Q2hlY2tvdXQ6NTE3M2ViNzAtNjMxOC00YzA3LTgwYzktY2VlNjkyMTdjZGE2",){confirmationNeededconfirmationData}}
Package: saleor-sdk-python
Saleor SDK PythonA set of tools that help Python developers work with Saleor. This is a very early stage in the life of this library and many things are not yet figured out.Documentation, contribution rules, process and the code itself (this includes the APIs) are expected to change rapidly.InstallationInstallHatch.ToolingThis library provides a CLI that contains a growing set of commands that are useful in day-to-day development around Saleor.There are two entrypoints (here is agood articleon why this is important):python-msaleor_sdktools
saleor_sdktoolsSaleor ID encodingsaleor-sdktoolsdecode-idVXNlcjoyMg==saleor-sdktoolsencode-idUser22Documentationhatch run docs:serveand navigate tohttp://127.0.0.1:8000Testshatch run testBuild and deployhatch build -c
hatch publishCode stylehatch run lint:fmt
hatch run lint:all