Saravanan666/pypi-org huggingface dataset:
Package: bisecter
============
IntroductionThis tool is inspired by thegit bisecttool but allows to bisect over
multiple arbitrary lists of arguments like nightly builds, tool versions or
simply list of arguments.SetupBisecter is available frompipso one can install it by executing::python3 -m pip install bisecteror to install directly the latest version from git::python3 -m pip install git+https://github.com/ldoktor/bisecter.gitBasic usageThe workflow is similar togit bisect, only when starting the bisection
one needs to specify the lists of arguments this bisection is going to use::bisecter start '20230101,20230102,20230103,20230104,20230105,20230106,20230107'
bisecter run ./test.sh
bisecter logOne can also manually tag variants::bisecter start '20230101,20230102,20230103,20230104,20230105,20230106,20230107'
./test.sh $(bisecter args)
bisecter good
./test.sh $(bisecter args)
bisecter bad
...Seebisecter --helpfor more details.
Package: bisect-find-first-bad
bisect-find-first-badgit bisect runcan only search through commits.
This search don't use git to switch versions. You can check arbitrary options foris_bad.installpip install bisect-find-first-badusageyou should subclass fromBisectFindFirstBadand implement 1 methodis_badoris_good.
This method should returnTrue/Falsewhich indicates is option good or bad.
When instantiating your class you should provide a sequence of options to check. They should be sorted byis_bad. Left side of options are good options and on the right are bad ones.simple examplefrombisect_find_first_badimportBisectFindFirstBadclassFirstGreaterThan3(BisectFindFirstBad):defis_bad(self,op)->bool:returnop>3# or you can use is_good:classFirstGreaterThan3(BisectFindFirstBad):defis_good(self,op)->bool:returnop<=3first_greater_than_3=FirstGreaterThan3(options=(1,2,3,4,5,6))>>>first_greater_than_3.is_bad(2)False>>>first_greater_than_3.is_bad(5)True>>>first_greater_than_3()4start4:BAD2start2:GOOD3start3:GOOD====================================================================================================firstbadoptionis:44more complex example - find first bad version of poetry dependencyimportsubprocessfrombisect_find_first_badimportBisectFindFirstBadclassFirstBadDependencyVersion(BisectFindFirstBad):defis_bad(self,op)->bool:# kinda setupsubprocess.check_call(f'git checkout HEAD -- poetry.lock pyproject.toml && poetry add "my_library=={op}"',shell=True)# check is_badreturnbool(subprocess.run('make test',shell=True).returncode)first_bad_dependency_version=FirstBadDependencyVersion(options='''\v0.0.1v0.0.2v0.0.3v0.1.0v0.1.1v0.1.2v0.2.0v0.2.1'''.splitlines())>>>first_bad_dependency_version()v0.1.1startv0.1.1:GOODv0.2.0startv0.2.0:BADv0.1.2startv0.1.2:BAD====================================================================================================firstbadoptionis:v0.1.2'v0.1.2'
Package: bisectlib
bisectlibarray bisection algorithm helperinstallpip install bisectlibusageyou should subclass fromBisectand implement 1 methodis_badoris_good.
This method should returnTrue/Falsewhich indicates is option good or bad.
When instantiating your class you should provide a sequence of options to check. They should be sorted byis_bad. By default, left side of options are considered good options and on the right are bad ones. (You can change this by passingsort_order='bad-good'argument)simple examplefrombisectlibimportBisectclassFirstGreaterThan3(Bisect):defis_bad(self,op)->bool:returnop>3# or you can use is_good:classFirstGreaterThan3(Bisect):defis_good(self,op)->bool:returnop<=3first_greater_than_3=FirstGreaterThan3(options=(1,2,3,4,5,6))>>>first_greater_than_3.is_bad(2)False>>>first_greater_than_3.is_bad(5)True>>>first_greater_than_3()4start4:BAD2start2:GOOD3start3:GOOD====================================================================================================firstbadoptionis:44more complex example - find first bad version of poetry dependencyimportsubprocessfrombisectlibimportBisectclassFirstBadDependencyVersion(Bisect):defis_bad(self,op)->bool:# kinda setupsubprocess.check_call(f'git checkout HEAD -- poetry.lock pyproject.toml && poetry add "my_library=={op}"',shell=True)# check is_badreturnbool(subprocess.run('make test',shell=True).returncode)first_bad_dependency_version=FirstBadDependencyVersion(options='''\v0.0.1v0.0.2v0.0.3v0.1.0v0.1.1v0.1.2v0.2.0v0.2.1'''.splitlines())>>>first_bad_dependency_version()v0.1.1startv0.1.1:GOODv0.2.0startv0.2.0:BADv0.1.2startv0.1.2:BAD====================================================================================================firstbadoptionis:v0.1.2'v0.1.2'
Package: bisect-list
bisect-listFind bad values in a list, logarithmically (FAST!)!installpoetryaddbisect-listusagebisect_exception(values, func)Logarithmically removes items that don't trigger an exception.Useful for finding the minimal list of items that triggers an excetpion.fromunittest.mockimportMagicMockfrombisect_listimportbisect_exceptiondeferror_3705_and_7399(values):if3705invaluesand7399invalues:raiseException(f"Never mix 2 with 5!")mock_func=MagicMock(side_effect=error_3705_and_7399)values=list(range(10_000))result=bisect_exception(values,mock_func)assertresult==[3705,7399]assertmock_func.call_count==53bisect_same_exception(values, func)Same asbisect_exception, except makes sure the type of the exception is the same as when callingfunc(values)before starting the bisection.frombisect_listimportbisect_same_exceptionclassSpecialException(Exception):passdeferror_2_and_5(values):if2invaluesand5invalues:raiseSpecialException(f"Never mix 2 with 5!")raiseException("different exception")values=[1,2,3,4,5,6,7,8,9]result=bisect_same_exception(values,error_2_and_5)assertresult==[2,5]biest(values, test)frombisect_listimportbisectresult=bisect([1,2,3,4,5,6,7,8,9],test=lambdaxs:2inxsand8inxs)assertresult==[2,8]licenseMIT
Package: bisect-scanner
bisect_scannerDetermine crypro accounts balance history using bisection algorithmUsage>>>frombisect_scannerimportEtherScanner,EthereumERC20Scanner>>>account='0x790370ff5045bCeCc2161f0913302FCCC7Ee256d'>>>ETHEREUM_W3_URL='...'# url for an archive node web3 or a web3 client instance>>>scanner=EtherScanner(w3=ETHEREUM_W3_URL)>>>[*scanner.balance_history(account='0x790370ff5045bCeCc2161f0913302FCCC7Ee256d')][(0,0.0),(11503731,0.005),(12103372,0.015),(12107610,0.009),(12425773,0.0)]>>>scanner=EthereumERC20Scanner(w3=ETHEREUM_W3_URL,contract_address='0x0258f474786ddfd37abce6df6bbb1dd5dfc4434a')>>>[*scanner.balance_history(account='0x790370ff5045bCeCc2161f0913302FCCC7Ee256d')][(10939214,0.0),(11503719,200.0),(12107610,0.0)]Installing bisect_scannerbisect_scanner is available on PyPI:$pipinstallbisect-scannerCommand line utility$python-mbisect_scanner--helpusage: python -m bisecect_scanner [-h] --account ACCOUNT[--contract_address CONTRACT_ADDRESS][--scan_step SCAN_STEP][--precission PRECISSION][--interpolation_step INTERPOLATION_STEP][--start_block START_BLOCK][--end_block END_BLOCK] [--polygon][--ethereum] [--fake]Outputs account balance history on stdout in the csv format,Example: python -mbisect_scanner --account=0xCD6909C37CCEA877a5c8e9a3ffd69d9D9943409Foptional arguments:-h, --help            show this help message and exit--account ACCOUNT     address--contract_address CONTRACT_ADDRESSERC20 contract address--scan_step SCAN_STEPscan step--precission PRECISSIONprecission--interpolation_step INTERPOLATION_STEPinterpolation step--start_block START_BLOCKStart Block--end_block END_BLOCKEnd Block--polygon             Polygon (MATIC native Token)--ethereum            Ethereum (default)--fake                fake chain for testing purposes onlyAn Example$python-mbisect_scanner--account='0x790370ff5045bCeCc2161f0913302FCCC7Ee256d'block,balance0,0.011503731,0.00512103372,0.01512107610,0.00912425773,0.0ConfigurationConfiguration can be stored in~/.config/bisect_scanner.inifileExample of bisect_scanner.ini file[node_urls]
W3_URL=archive_node_url_for_general_w3scanner
POLYGON_URL=argive_node_url_for_polygonscanner
ETHEREUM_URL=archive_node_url_for_etherscannerAuthorsbisect_scannerwas written byKarel Novak.
Package: bisectsearch
Functions for performing binary search on sorted listspip install bisectsearchTested against Windows 10 / Python 3.10 / Anacondafrombisectsearchimport(rightmost_value_equal_to,leftmost_value_equal_to,rightmost_value_less_than,rightmost_value_less_than_or_equal,leftmost_value_greater_than,leftmost_value_greater_than_or_equal,)l=sorted(list(range(0,5))*3)print(f'{(h:=rightmost_value_equal_to(l,4)),l[h]=}')print(f'{(h:=rightmost_value_equal_to(l,0)),l[h]=}')print(f'{(h:=rightmost_value_equal_to(l,1)),l[h]=}')print(f'{(h:=leftmost_value_equal_to(l,4)),l[h]=}')print(f'{(h:=leftmost_value_equal_to(l,0)),l[h]=}')print(f'{(h:=leftmost_value_equal_to(l,1)),l[h]=}')print(f'{(h:=rightmost_value_less_than(l,4)),l[h]=}')print(f'{(h:=rightmost_value_less_than(l,2)),l[h]=}')print(f'{(h:=rightmost_value_less_than(l,1)),l[h]=}')print(f'{(h:=rightmost_value_less_than_or_equal(l,40)),l[h]=}')print(f'{(h:=rightmost_value_less_than_or_equal(l,0)),l[h]=}')print(f'{(h:=rightmost_value_less_than_or_equal(l,1)),l[h]=}')print(f'{(h:=leftmost_value_greater_than(l,2)),l[h]=}')print(f'{(h:=leftmost_value_greater_than(l,3)),l[h]=}')print(f'{(h:=leftmost_value_greater_than(l,0)),l[h]=}')print(f'{(h:=leftmost_value_greater_than(l,1)),l[h]=}')print(f'{(h:=leftmost_value_greater_than_or_equal(l,3)),l[h]=}')print(f'{(h:=leftmost_value_greater_than_or_equal(l,0)),l[h]=}')print(f'{(h:=leftmost_value_greater_than_or_equal(l,1)),l[h]=}')# output# (h:=rightmost_value_equal_to(l, 4)), l[h]=(14, 4)# (h:=rightmost_value_equal_to(l, 0)),l[h]=(2, 0)# (h:=rightmost_value_equal_to(l, 1)),l[h]=(5, 1)# (h:=leftmost_value_equal_to(l, 4)),l[h]=(12, 4)# (h:=leftmost_value_equal_to(l, 0)),l[h]=(0, 0)# (h:=leftmost_value_equal_to(l, 1)),l[h]=(3, 1)# (h:=rightmost_value_less_than(l, 4)),l[h]=(11, 3)# (h:=rightmost_value_less_than(l, 2)),l[h]=(5, 1)# (h:=rightmost_value_less_than(l, 1)),l[h]=(2, 0)# (h:=rightmost_value_less_than_or_equal(l, 40)),l[h]=(14, 4)# (h:=rightmost_value_less_than_or_equal(l, 0)),l[h]=(2, 0)# (h:=rightmost_value_less_than_or_equal(l, 1)),l[h]=(5, 1)# (h:=leftmost_value_greater_than(l, 2)),l[h]=(9, 3)# (h:=leftmost_value_greater_than(l, 3)),l[h]=(12, 4)# (h:=leftmost_value_greater_than(l, 0)),l[h]=(3, 1)# (h:=leftmost_value_greater_than(l, 1)),l[h]=(6, 2)# (h:=leftmost_value_greater_than_or_equal(l, 3)),l[h]=(9, 3)# (h:=leftmost_value_greater_than_or_equal(l, 0)),l[h]=(0, 0)# (h:=leftmost_value_greater_than_or_equal(l, 1)),l[h]=(3, 1)
Package: bise.diazotheme
Introduction============This product may contain traces of nuts.Changelog=========1.4-dev - (unreleased)------------------* dummy release[iulianpetchesi #100969]1.3 - (2018-12-27)------------------* Task: Removed google analytics code for bise[iulianpetchesi #100969]1.2 - (2018-10-26)------------------* Bug fix: Check for content and screentimeAnalytics before activation[olimpiurob #96812]* Task: Added matomo code for bise[iulianpetchesi #99202]* Bug fix: avoid double search icon on IOS devices[ichim-david refs #90214]* Change: html template cleanups[ichim-david refs #95380]* Change: load theme css from portal_css when applied to plone site[ichim-david refs #95380]* Change: removed canonical from theme when applied to plone site[ichim-david refs #95380]* Change: fixed links for favicons for 16x and 32x[ichim-david refs #95380]- Feature: added page to detect linked "foreign" images[tiberich #92205]- Bug fix: don't break portlet manager when all portlets are blocked[tiberich, krisztina]- Fix country highlight when changing countries from country factsheets dropdown[krisztina #85826]- Added overlay over images so that the links work in the pdf version[iulianpetchesi #85140]- Added links to footer[iulianpetchesi #85140]- Added pdf cover & styling[iulianpetchesi #85140]- Prevent unwanted slicing of content at pdf export[iulianpetchesi #85140]- Improve pdf styling[iulianpetchesi #85140]1.1 - (2017-06-08)------------------- Improve styling of mtr pages[mihaim]1.0dev (unreleased)-------------------- Added script to enable screentimeAnalytics from eea.jquery[iulianpetchesi #85252]- Fixed diazo rule for base tag[iulianpetchesi #81711]- Improve styling of country checkout actions[tiberich]- Initial release
Package: bise.theme
IntroductionTheme package for biodiversity.europa.euChangelog1.20 (2015-09-04)Fix link to termite in shared tags api.1.19 (2015-01-18)Reorder personal barRemove target filters1.18 (2015-01-08)Catalog client changes [arriejon]1.17.1 (2014-12-03)Minor fixes in catalogue client1.17 (2014-11-26)Revert catalogue client1.16 (2014-11-18)Remove banner.1.15 (2014-09-23)Sort actions and targets.1.14.4 (2014-09-18)Style improvements1.14.3 (2014-09-17)Fix catalogue library issues.1.14.2 (2014-09-17)Fix catalogue library issues1.14.1 (2014-09-17)Add help link  [MikelS]Fix jquery compatibility issue  [MikelS]1.13 (2014-09-15)Add catalogue client library [MikelS]1.12 (2014-08-08)Update banner and add counter for biodiversity targets [MikelS]1.11.1 (2014-06-30)Fix method caching. Fixes #20265 [erral]1.11 (2014-06-19)Styling changes [msantamaria]New behavior fields for target and actions [erral]1.10 (2014-05-27)Reenable MAES banner [msantamaria]1.9 (2014-05-23)Remove MAES banner [msantamaria]1.8 (2014-5-9)Install versioning and staging support [erral]1.7 (2014-4-11)Add a feed-updating method that requires no credentials [erral]1.6 (2014-3-28)Include Products.RedirectionTool as dependency [erral]1.5 (2014-3-27)Fix fonts [msantamaria]1.4 (2014-3-27)Styles, new theme
[msantamaria]1.3 (2014-01-20)Style changes
[msantamaria]1.2.2 (2013-12-17)Add a browser view to import creator information
[erral]1.2.1 (2013-12-16)Spell the dependency correctly
[erral]1.2 (2013-12-16)Add bise.catalogueindexer as a dependency
[erral]1.1 (2013-11-26)Upgrade steps for upgrading to EEA KGS 1.6
[erral]1.0 (2013-10-7)Initial release
[erral]
Package: bis-fetcher
BIS FetcherA Python library that scrapes the BIS website to download and extract text from central bank speeches.Documentation:https://entelecheia.github.io/bis-fetcherGitHub:https://github.com/entelecheia/bis-fetcherPyPI:https://pypi.org/project/bis-fetcherbis-fetcheris a Python library that programmatically scrapes the website of the Bank for International Settlements (BIS) to download PDF files of all available central bank speeches. It then extracts the text from these PDFs.The library allows easy access to a comprehensive dataset of central bank speeches over time. Researchers can use this data to perform quantitative analysis of central bank communication and track changes in tone, topics, and sentiment.The BIS hosts speeches by governors and other senior officials from central banks around the world. However, scraping the website manually is tedious. bis-fetcher automates the entire workflow from crawling the site to text extraction.It provides a simple API to get a list of available speeches, download the PDFs, and extract text. The text can be further processed or fed into models for analysis.ChangelogSee theCHANGELOGfor more information.ContributingContributions are welcome! Please see thecontributing guidelinesfor more information.LicenseThis project is released under theMIT License.
Package: bisheng
A set of utility functions for processing Chinese text. Current features
include:Add spaces between Chinese/Japanese/Korean characters and halfwidth
charactersConvert traditional Chinese to simplified Chinese and the other way aroundInstallation$ pip install bishengUsageAdd spaces between Chinese characters and halfwidth characters:>>> import bisheng
>>> print bisheng.add_spaces(u'BiSheng由Python寫成，名字源自1000多年前的活字印刷術發明人-畢昇。')
BiSheng 由 Python 寫成，名字源自 1000 多年前的活字印刷術發明人 - 畢昇。Specify the characters you want to exclude:>>> print bisheng.add_spaces(u'中[括]弧')
中 [括] 孤

>>> print bisheng.add_spaces(u'中[括]弧', exclude='[]')
中[括]弧Traditional/simplified Chinese convertion:>>> print bisheng.to_simp(u'畢昇')
毕升

>>> print bisheng.to_trad(u'毕升')
畢升
Package: bisheng-langchain
What is bisheng-langchain?bisheng-langchain is an open-source langchain extending library built to power building LLM application.
bisheng-langchain provides more components to support Chinese LLMs and and Chinese based token environments for prompt engineering and ICL template.The project is a sub-module ofbisheng.Key featuresRetrival Enhancement components, like ESIndex, DBIndex, GraphIndexSupporting Open LLMs and embeddings modelsHigh performance QAs ChainsHigh Semanticly Chinese token processingQuick startStart with Bisheng Platform.We provide a open cloud service for easily use. Seefree trial.Install bisheng-langchainInstall from pip:pip install bisheng-langchainQuick Start GuideDocumentationFor guidance on installation, development, deployment, and administration,
check outbisheng-langchain Dev Docs.Acknowledgmentsbisheng-langchain adopts dependencies from the following:Thanks tolangchainfor the main framework.
Package: bisheng-unstructured
What is bisheng-unstructured?Bisheng-unstructured is an open-source unstructured data parsing library built to
power LLM applications like pretrain, finetune, prompting engineering.
Bisheng-unstructured makes the unstructured data porcessing more easily and provides a consistent user experience regardless of any file types.The project is a sub-project ofbisheng.Key featuresHigh precision pdf layout parserHigh precision table structure recoveringHigh precision OCR abilityMore friendly for token prossing for the visual text element, like table, listQuick startStart With Bisheng PlatformUse as a chain nodeElemUnstructureLoaderStart with DataElem Services.We provide a open cloud service for easily use. Seefree trial.Install bisheng-unstructuredInstall from pip:pip install bisheng-unstructuredQuick Start GuideUsing from pre-builded imageDocumentationFor guidance on installation, development, deployment, and administration,
check outbisheng-unstructured Docs.IssuesReporting problems, asking questions
We appreciate any feedback, questions or bug reporting regarding this project.User can postingIssues,
follow the process outlined in theStack Overflow document.For questions, we recommend posting in our community GitHubDiscussions.Acknowledgmentsbisheng-unstructured adopts dependencies from the following:Thanks tounstructuredfor the main framework.
Package: bisi
bisiBisi (BuildItShipIt) is a tool to help run workloads locally and in the cloud.
Bisi lets you define and run all your containerized resources from one file stored at the root of your project all in python.Currently supported providers are:Locally with dockerAWS BatchInstallationPrerequisitesDockeris required to use bisiThe awscli and credentials are required to use the AWS Batch features.pipinstallawscli
awsconfigureInstall bisiInstall bisi frompypipipinstallbisiQuickstartGetting started with bisi is as simple as creating a Dockerfile and a bisi_resources.py file.Create a clean directory.mkdirbisi_examplecdbisi_exampleRun the following to create an example Dockerfile.echo'FROM python:slimRUN pip install numpyADD . /bisiWORKDIR /bisi'>DockerfileNext define an example workload as a python script.echo'import numpyprint(numpy.random.rand())'>workload.pyFinally create a bisi_resources.py file to tell bisi about our Dockerfile and how to run our workload.echo'import bisi.resources as bsrdf = bsr.Dockerfile(name="bisi_example", file="Dockerfile")bsr.Job(name="numpy_example", entrypoint="workload.py", dockerfile=df)'>bisi_resources.pyNow you can use bisi to build your dockerfile and run it.bisibuild
bisirunnumpy_exampleFrom here you are free to use bisi to run anything from data processing to deep learning training in docker!
Check out the sections below for more detailed usage.AWS BatchWithAWS Batchyou can run your batch workloads in the cloud at any scale and bisi makes that easy for you.
To get started follow theAWS Batch Setup Guideto stand up a minimal Batch infrastructure setup running on CPU instances.Once you have a batch job queue, you can configure bisi to utilize your batch infrastructure to run jobs.
Assuming you ran the quickstart guide you can run the following to update yourbisi_resources.pyfile.echo'import bisi.resources as bsrfrom bisi.resources.config import BatchJobConfig, ECRConfigdf = bsr.Dockerfile(name="bisi_example", file="Dockerfile", ecr_config=ECRConfig("bisi_example"))bsr.Job(name="numpy_example", entrypoint="workload.py", dockerfile=df,batch_config=BatchJobConfig(jobQueue="bisi-test-jq"))'>bisi_resources.pyThis configures bisi to upload your docker image toAmazon ECRand tells bisi where to submit your batch job.
Next you can deploy your container and run it in batch.bisideploy
bisirun--providerawsnumpy_exampleAt this point you can navigate tohttps://console.aws.amazon.com/batch/hometo see your pending job.
From there you can also navigate to the job to see the logs for the job.ContactOpen an issue athttps://github.com/nrfrank/bisiContactbisi1.user@gmail.comabout any questions/reports.
Package: biskit
Biskit is a modular, object-oriented Python library for structuralbioinformatics research. It facilitates the manipulation and analysis
of macromolecular structures, protein complexes, and molecular
dynamics trajectories. For efficient number crunching, Biskit objects
tightly integrate with numpy. Biskit also offers a platform for the
rapid integration of external programs and new algorithms into complex
workflows. Calculations are thus often delegated to established
programs like Xplor, Amber, Hex, Prosa, T-Coffee, TMAlign, Reduce and
Modeller; interfaces to further software can be added easily.
Package: biskotaki
BISKOTAKIProject generated from thehttps://github.com/boromir674/cookiecutter-python-package/tree/master/src/cookiecutter_pythoncookiecutterCode:https://github.com/boromir674/biskotakiDocs:https://biskotaki.readthedocs.io/en/master/PyPI:https://pypi.org/project/biskotaki/CI:https://github.com/boromir674/biskotaki/actions/Featuresbiskotakipython packageTODO Document aGreat FeatureTODO Document anotherNice FeatureTested against multipleplatformsandpythonversionsDevelopmentHere are some useful notes related to doing development on this project.Test Suite, usingpytest, located intestsdirParallel Executionof Unit Tests, on multiple cpu’sDocumentation Pages, hosted onreadthedocsserver, located indocsdirAutomation, usingtox, driven by singletox.inifileCode CoveragemeasuringBuild Command, using thebuildpython packagePypi Deploy Command, supporting upload to bothpypi.organdtest.pypi.orgserversType Check Command, usingmypyLintCheckandApplycommands, usingisortandblackCI Pipeline, running onGithub Actions, defined in.github/Job Matrix, spanning differentplatform’s andpython version’sPlatforms:ubuntu-latest,macos-latestPython Interpreters:3.6,3.7,3.8,3.9,3.10Parallel Jobexecution, generated from thematrix, that runs theTest SuitePrerequisitesYou need to havePythoninstalled.QuickstartUsingpipis the approved way for installingbiskotaki.python3-mpipinstallbiskotakiTODO Document a use caseLicenseGNU Affero General Public License v3.0LicenseFree software: GNU Affero General Public License v3.0
Package: bism
bism - Biomedical Image Segmentation ModelsThis is a collection of generic PyTorch model constructors usefull for biomedical segmentation tasks.
Something akin to thetimmpackage for 2D image tasks.
When at all possible, each model will offer a 2D or 3D implementation, however we will not provide pre-trained model files.Current ModelsModel2D3DScriptableUNet✓✓✓UNeXT✓✓✓Recurrent UNet✓✓✓Residual UNetUnet++✓✓✓CPnet✓✓✓Current Generic BlocksBLOCK NAME2D3DUNeXT Block✓✓ConcatConv✓✓Recurrent UNet BLock✓✓Residual UNet BLock✓✓DropPath✓✓LayerNorm✓✓UpSample✓✓ViT BlockSegmentation ImplementationAPPROACH2D3DCellposeAffinities✓Local Shape Desc.✓