Saravanan666/pypi-org huggingface dataset:
Package: openram
OpenRAMAn open-source static random access memory (SRAM) compiler.What is OpenRAM?OpenRAM is an award winning open-source Python framework to create the layout,
netlists, timing and power models, placement and routing models, and
other views necessary to use SRAMs in ASIC design. OpenRAM supports
integration in both commercial and open-source flows with both
predictive and fabricable technologies.DocumentationPlease see ourdocumentationand let us know if anything needs
updating.Get InvolvedPort itto a new technologyReport bugs by submittingGithub issuesDevelop new features (seehow to contribute)Submit code/fixes using aGithub pull requestFollow ourprojectRead and cite ourICCAD paperFurther HelpDocumentationOpenRAM Slack WorkspaceOpenRAM Users Group(subscribe here)OpenRAM Developers Group(subscribe here)LicenseOpenRAM is licensed under theBSD 3-Clause License.PublicationsM. R. Guthaus, J. E. Stine, S. Ataei, B. Chen, B. Wu, M. Sarwar, "OpenRAM: An Open-Source Memory Compiler," Proceedings of the 35th International Conference on Computer-Aided Design (ICCAD), 2016.S. Ataei, J. Stine, M. Guthaus, "A 64 kb differential single-port 12T SRAM design with a bit-interleaving scheme for low-voltage operation in 32 nm SOI CMOS," International Conference on Computer Design (ICCD), 2016, pp. 499-506.E. Ebrahimi, M. Guthaus, J. Renau, "Timing Speculative SRAM," IEEE International Symposium on Circuits and Systems (ISCAS), 2017.B. Wu, J.E. Stine, M.R. Guthaus, "Fast and Area-Efficient Word-Line Optimization,"  IEEE International Symposium on Circuits and Systems (ISCAS), 2019.B. Wu, M. Guthaus, "Bottom Up Approach for High Speed SRAM Word-line Buffer Insertion Optimization," IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC), 2019.H. Nichols, M. Grimes, J. Sowash, J. Cirimelli-Low, M. Guthaus "Automated Synthesis of Multi-Port Memories and Control," IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC), 2019.M. Guthaus, H. Nichols, J. Cirimelli-Low, J. Kunzler, B. Wu, "Enabling Design Technology Co-Optimization of SRAMs though Open-Source Software," IEEE International Electron Devices Meeting (IEDM), 2020.H. Nichols, "Statistical Modeling of SRAMs," M.S. Thesis, UCSC, 2022.Contributors & AcknowledgmentMatthew GuthausfromVLSIDAcreated the OpenRAM project and is the lead architect.James StinefromVLSIARCHco-founded the project.Many students: Hunter Nichols, Michael Grimes, Jennifer Sowash, Yusu Wang, Joey Kunzler, Jesse Cirimelli-Low, Samira Ataei, Bin Wu, Brian Chen, Jeff Butera, Sage WalkerIf I forgot to add you, please let me know!
Package: openrange
OpenRangeOpenRangeprovides a simple interface for building custom range-like
objects for any type that can be represented numerically.DocumentationSee the full documentation:openrange.readthedocs.orgInstallationOpenRangeis easy to install using pip.$pipinstallopenrangeSupportOpenRangeis tested against:python2.7,3.2,3.3,3.4pypyandpypy3Primary development and testing were for python2.7.ContributeContribution is welcome from those who propose new features, have ideas for improvement, or submit bug fixes. Here’s a checklist for contributing to this project:Open or respond to an issue to discuss a feature or bugFork the repo on GitHub and start making changesWrite test(s) for the bug or featureAdd yourself toCONTRIBUTORS.rstSend a pull request
Package: open-rarity
OpenRarityWe’re excited to announce OpenRarity, a new rarity protocol we’re building for the NFT community. Our objective is to provide a transparent rarity calculation that is entirely open-source, objective, and reproducible.With the explosion of new collections, marketplaces and tooling in the NFT ecosystem, we realized that rarity ranks often differed across platforms which could lead to confusion for buyers, sellers and creators. We believe it’s important to find a way to provide a unified and consistent set of rarity rankings across all platforms to help build more trust and transparency in the industry.We are releasing the OpenRarity library in a Beta preview to crowdsource feedback from the community and incorporate it into the library evolution.See the full announcement in theblog post.Developer documentationReaddeveloper documentationon how to integrate with OpenRarity.Setup and run tests locallypoetry install # install dependencies locally
poetry run pytest # run testsSome tests are skipped by default due to it being more integration/slow tests.
To run resolver tests:poetry run pytest -k test_testset_resolver --run-resolversLibrary usageYou can install open rarity as apython packageto use OpenRarity in your project:pip install open-rarityPlease refer to thescripts/folder for an example of how to use the library.If you have downloaded the repo, you can use OpenRarity shell tool to generate json or csv outputs of OpenRarity scoring and ranks for any collections:python -m scripts.score_real_collections boredapeyachtclub proof-moonbirdsReaddeveloper documentationfor advanced library usageContributions guide and governanceOpenRarity is a community effort to improve rarity computation for NFTs (Non-Fungible Tokens). The core collaboration group consists of four primary contributors:Curio,icy.tools,OpenSeaandProofOpenRarity is an open-source project and all contributions are welcome. Consider following steps when you request/propose contribution:Have a question? Submit it on OpenRarity GitHubdiscussionspageCreate GitHub issue/bug with description of the problemlinkSubmit Pull Request with proposed changesTo merge the change in themainbranch you required to get at least 2 approvals from the project maintainer listAlways add a unit test with your changesWe use git-precommit hooks in OpenRarity repo. Install it with the following commandpoetry run pre-commit installProject Setup and Core technologiesWe used the following core technologies in OpenRarity:Python ≥ 3.10.xPoetry for dependency managementNumpy ≥1.23.1PyTest for unit testsLicenseApache 2.0 , OpenSea, ICY, Curio, PROOF
Package: openre
# OpenREБиблиотека для моделирования распределенной нейронной сети.
Моделирование происходит на GPU или CPU устройствах.## InstallПример установки для Ubuntu.Перед установкой было проделано:`bash sudoapt-getinstallbuild-essentialpython-dev`Желательно поставить virtualenvwrapper - иструкция написана исходя из предположения что он стоит.Для установки pyopencl нужно сначала скачать заголовочные файлы и любой ICD Loader, а так же опционально реализацию (ICD implementation).
Согласно [этой странице](http://wiki.tiker.net/OpenCLHowTo#Debian) устанавливаем:
ICD Loader:`bash sudoapt-getinstallamd-libopencl1`Далее я установил AMD ICD loader и CPU ICD согдласно инструкции [тут](http://wiki.tiker.net/OpenCLHowTo#Installing_the_AMD_ICD_loader_and_CPU_ICD_.28from_the_.22APP_SDK.22.29) (Installing the AMD ICD loader and CPU ICD (from the “APP SDK”)):
Скачиваем архив для системы, распаковываем и запускаем:`bash sudo./AMD-APP-SDK-VERSION-GA-linux64.sh`После установки я дополнительно сделал (если pip install -r requirements.txt отрабатывает без ошибки, то этот шаг не нужен):`bash cd /usr/lib sudo ln-s../../opt/AMDAPPSDK-3.0/lib/x86_64/libOpenCL.so`В этом случае ошибки (/usr/bin/ld: cannot find -lOpenCL) при компиляции pyopencl (это будет ниже) не возникает, но очевидно это не очень правильно.Далее сама установка`bash git clonehttps://github.com/openre/openre.gitcd openre mkvirtualenv openre pip install-rrequirements.txt # без этого setup.py не работает ./setup.py install `
Package: openregister
OpenRegisterPublishing tools for GOV.UK style registers.A self-contained and easily installedGOV.UK register productintended for use by devolved authorities and administrations.Installationpip3 install openregisterBasic usageopenregister serve /path/to/register.jsonRuns a web server serving an index of registers onhttp://localhost:8088/Command options$ openregister --help

Usage: openregister [OPTIONS] COMMAND [ARGS]...

  OpenRegister:  publishing tools for GOV.UK style registers.

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  serve*Server options$ openregister serve --help

Usage: openregister serve [OPTIONS]

Options:
  -h, --host TEXT      host for server, defaults to 127.0.0.1
  -r, --register TEXT  Serve a single register, otherwise serve all know
                       registers as a catalog
  -p, --port INTEGER   port for server, defaults to 8088
  -d, --debug          More verbose logging and automatically reload on
                       changes
  --help               Show this message and exit.DevelopmentDevelopment requires Python 3.5 or later, we recommend using avirtual environment:make init
make
python -m openregister --help
Package: openregister-client
Usageopenregister_client.registers.OpenRegisteris the base class that accesses a single register and ignores field data typing information.openregister_client.registers.Registerandopenregister_client.registers.AlphaRegisteruse
the “register” register to discover available registers and the field data types their items should contain.
Records, entries and items (the actual data contained in records) returned aredictsubclasses that provide typed attributes to access the register data more easily.Currently, only compatible with Python 3.6+.Install usingpip installopenregister-client.Usage samples:# direct register usage; field types will not have correct types, but data can be loaded more simplyfromopenregister_client.registersimportOpenRegistercountry_register=OpenRegister(name='country')country_record=country_register.get_record('GB')print('The official name for GB is%s'%country_record.item.official_name)ifnotcountry_record.item.is_current:print('This country is not currently recognised')# using register auto-discovery to process fields and datatypesfromopenregister_client.registersimportRegisterregister=Register()territory_register=register.get_register('territory')territory_items=sorted(map(lambdarecord:record.item,territory_register.get_records()),key=lambdaitem:item.territory)forterritoryinterritory_items:print('The official name for territory%sis%s'%(territory.territory,territory.official_name))# make a Django model class; works best when using auto-discoveryfromopenregister_client.django_compat.model_factoryimportModelFactoryfromopenregister_client.registersimportRegistercountry_register=Register().get_register('country')withopen('models.py','wt')asf:f.write(ModelFactory(country_register).get_model_code())# an API key can be provided when instantiating a register classcountry_register=OpenRegister(name='country',api_key='YOUR API KEY')Caching is not implemented. Users of the library can store results of queries or subclassOpenRegister.requestto add caching.Consuming non-json input formats is not supported and probably not necessary.DevelopmentPlease report bugs and open pull requests onGitHub.Usepython setup.py testto run all tests.Distribute a new version by updating theVERSIONtuple inopenregister_client/__init__.pyand
publishing a release in GitHub (this triggers a GitHub Actions workflow to automatically upload it).
Alternatively, runpython setup.py sdist bdist_wheel uploadlocally.To-doDo not paginate past the end since numbers of entries and records are knownPerhaps lower minimum Python version to 3.4 or 3.5; usetypingmoduleReferenceshttps://www.registers.service.gov.uk/https://docs.registers.service.gov.uk/http://openregister.github.io/specification/(outdated?)http://open-registers-docs.readthedocs.io/en/latest/(deleted)CopyrightCopyright (C) 2020 HM Government (Ministry of Justice Digital & Technology).
See LICENSE.txt for further details.
Package: openremote-cli
OpenRemote CLIIntroThis is Command Line Interface to OpenRemote IoT platform. It's purpose is to reduce friction of using OpenRemote by Do It Yourself users.It is published as a Python package onPyPi.orgAbout OROpenRemote is a great real OpenSource IoT platform.UsageTo install/upgrade the CLI:pipinstall--upgradeopenremote-cli
or--versionDocker imageThere is also docker image with embedded openremote-cli and other tools used during
stack management, likepython,awsordocker-compose. Therefore,dockeris
the only tool installed when you need to work on remote host. Example:% docker run -ti --rm openremote/openremote-cli -V
openremote-cli/0.6.46 3.8.10 (default, May 12 2021, 15:56:47) [GCC 8.3.0] Linux/#1 SMP Tue Mar 23 09:27:39 UTC 202
% docker run -ti --rm --entrypoint bash -v ~/.aws:/root/.aws openremote/openremote-cli
root@4438b25d0834:/# aws --version
aws-cli/2.1.39 Python/3.8.8 Linux/5.10.25-linuxkit exe/x86_64.debian.10 prompt/offDeploy local OpenRemote stackordeployWhen it finishes you should be able to openhttps://localhostand login with admin:secret to the manager. Depending on your machine it can take few minutes to
start or services (usually less than 10 minutes). If it does not work yet, check it withdocker psto see if all services are in healthy or starting stage.Remove local OpenRemote stackordeploy--actionremoveDeploy OpenRemote stack on AWS with DNS entryordeploy--platformaws--dnsnamemyiot.mydomain.comCheck health of the openremote stackordeploy-ahealth--dnsnamemyiot.mydomain.com-vInstall shell completion extensionzshTODObashTODOConfigure existing OpenRemote instanceAdd usersAdd assetsAdd customizationDevelop openremote-cliFollowing tools are used:python (pip, poetry, black, PyPI.org)git (pre-commit, actions)docker (docker-compose, swarm)AWS (CloudFormation)makepoetry run openremote-cli -VAdding featureIn this project we use Behavior-driven development (or BDD). BDD is an agile
software development technique that encourages collaboration between developers,
QA and non-technical or business participants in a software project.This project uses Gherkin to define what features which should be covered. Features
files can be generated by people on manager level or even higher. An example
of file defining a feature:Feature:deployScenario:deploy to localhostGivenwe have docker and docker-compose installedWhenwe call openremote-cli --dry-run deploy --action createThenshow what will be doneWhen the feature is implemented it can be checked with behave:>behave
Feature:deploy# features/deploy.feature:1Scenario:deploytolocalhost# features/deploy.feature:3Givenwehavedockeranddocker-composeinstalled# features/steps/deploy_steps.py:8 0.453sWhenwecallopenremote-cli--dry-rundeploy--actioncreate# features/steps/deploy_steps.py:16 0.591sThenshowwhatwillbedone# features/steps/deploy_steps.py:24 0.000s1featurepassed,0failed,0skipped1scenariopassed,0failed,0skipped3stepspassed,0failed,0skipped,0undefined
Took0m1.044s
Package: openrepos-webclient
OpenRepos.net Web Clientopenrepos-webclientis a Python package to interface the OpenRepos.net
website in an automated way.InstallationTo install, run from the repository root:python3-mpipinstall--user.or install it fromPyPi:python3-mpipinstall--useropenrepos(Runsudo apt-get update && sudo apt-get -y install python3-pip && pip3 install --user -U pipif it
complains aboutpipnot being found)You will also need to install at least oneselenium-compatible browser with webdriver, for exampleFirefoxtogether withgeckodriver.If you want to runopenreposheadlessly, you will also needXvfb.What canopenreposdo?OpenRepos CredentialsDefine yourOpenRepos.netlogin credentials as environment variables.
You can either run this in each terminal session once before running theopenreposcommand:exportOPENREPOS_USERNAME=myuserOPENREPOS_PASSWORD=mypass... or you can put the above in a file (sayopenrepos-credentials.sh) and thensourcethis file, which is safer as you don't specify any credentials on the command-line:sourceopenrepos-credentials.shExecutingIf running justopenreposerrors out with something likecommand not found, then just runpython3 -m openreposinstead.Create a new appThis command creates a new SailfishOS appTestAppin theLibrariescategory.openrepos-inew-app-nTestApp-pSailfishOS-cLibrariesUpload RPMs to an appThis command uploads all RPMs in the current directory to an app on OpenRepos calledTestApp:openreposupload-rpm-nTestApp*.rpmIf you want to create the app if it doesn't exist yet, you have to provide the same metadata as above fornew-app:openreposupload-rpm-nTestApp-pSailfishOS-cLibraries*.rpmDelete an AppThis delete aTestApp:openreposdelete-app-nTestAppUsing this to automate uploading RPMs to OpenRepos in GitLab CIThis package was designed to automate RPM upload toOpenRepos.net.
To do that in GitLab CI, you canincludethe fileopenrepos-upload-rpm.gitlab-ci.ymlfrom this repository in your CI config.
The file contains an example of how to do that.TroubleshootingIf you experience problems and want to debug them, you can runopenreposinteractively (--interacitve) and verbosely (-vvv):openrepos--interactive-vvvupload-rpm...This will ask you before every step and tell you what's going on.To run without opening the browser window (like in CI), run it with--xvfb:openrepos--xvfb--interactive-vvvupload-rpm...DocumentationDocumentation of theopenrepospackage can be foundhere on
GitLab.Also, the command-line help pageopenrepos -his your friend.
Package: open-request-core
open-request-core - This is the core module of standard use for requestsAboutThis is the core module of standard use for requestsRequirementsPython3.7Install通过pip命令安装：pipinstallopen-request-coreLoggingAll log messages by this library are made using theDEBUG level, under theopen-request-corename. On how to control displaying/hiding that please consult thelogging documentation of the standard library. E.g. to hide these messages you can use:logging.getLogger("open-request-core").setLevel(logging.INFO)AuthorRocky Peng
Package: openresty-edge-sdk
OpenResty Edge SDKIntroductionPython SDK ofOpenResty Edge, which provides users with the ability to write custom tools to use OpenResty Edge.Installationpip install openresty-edge-sdkUsagefromedge2clientimportEdge2Clientclient=Edge2Client('http://127.0.0.1:8080','username','password')client.login()app_id=client.new_app(domains=['orig.foo.com'],label='origin site for foo.com')print(app_id)
Package: open-retrievals
Documentation|Tutorials|Release Notes|中文Retrievalsis an easy-to-use python framework supporting state-of-the-art embeddings, especially for retrieval and rerank in NLP/LLM, based on PyTorch and Transformers.UsagePretrained weight embeddingContrastive finetune transformers modelfromretrievalsimportAutoModelForEmbedding,AutoModelForMatchfromretrievals.lossesimportArcFaceAdaptiveMarginLossfromretrievals.trainerimportCustomTrainer,train_fntrain_dataset=RetrievalTrainDataset(topic_df,tokenizer,CFG.max_len,aug=False)train_loader=DataLoader(train_dataset,batch_size=CFG.batch_size,shuffle=True,num_workers=CFG.num_workers,pin_memory=False,drop_last=True,)loss_fn=ArcFaceAdaptiveMarginLoss(criterion=cross_entropy,in_features=768,out_features=CFG.num_classes,scale=CFG.arcface_scale,margin=CFG.arcface_margin,)model=AutoModelForEmbedding(CFG.MODEL_NAME,pooling_method="cls",loss_fn=loss_fn)optimizer=get_optimizer(model,lr=CFG.learning_rate)scheduler=get_scheduler(optimizer=optimizer,cfg=CFG,total_steps=len(train_dataset))trainer=CustomTrainer(model,device="cuda",apex=CFG.apex)trainer.train(train_loader=train_loader,criterion=None,optimizer=optimizer,epochs=CFG.epochs,scheduler=scheduler,dynamic_margin=True,)torch.save(model.state_dict(),CFG.output_dir+f"model_{CFG.exp_id}.pth")Contrastive finetune for LLMmodel=AutoModelForEmbedding('llama',pooling_method='last',query_instruction='')KNN searchretrieval_model=TextRetrievals(vec_model)retrieval_model.query('knn')Faiss searchIntegrated with sentence-transformerRerankAcknowledge and Reference
Package: openreviewio
/!\ Still in alpha stage and on its way to be stable. Every feedback is welcome!OverviewOpenReviewIOis a standard that describes a format for sharing media (shots, animations...) review informations.
It's main purpose is to guarantee review informations compatibility across media reviewing tools.
Please read the specifications for more informations.OpenReviewIO Python APIis the main Python API for the ORIO standard, maintained by the designer of the standard.VersionThe version of the API is related to the version of the standard.API 1.x.y <=> Standard 1.xIn Alpha stage, the API is 0.x.y but related to 1.x Standard. Will become 1.x.y when first stable release.Last standard version:1.0UsageimportopenreviewioasorioCreate a media reviewreview=orio.MediaReview("/path/to/media.ext")Create a notenote=orio.Note(author="Alice")Create contentContents are defined by the standardversionThere is a namingconventionabout the contents:-Commentmeans something related to the whole media.-Annotationmeans something related to a specific frame and duration of the media.Text commenttext_comment=orio.Content.TextComment(body="My text comment")Text annotationtext_annotation=orio.Content.TextComment(body="My text comment",frame=17,duration=20)Image commentimage=orio.Content.Image(path_to_image="/path/to/image_comment.png")Image annotationimage_annotation=orio.Content.ImageAnnotation(frame=17,duration=20,path_to_image="/path/to/image_annotation.png")Add content to note# Single contentnote.add_content(text_comment)# Several contentsnote.add_content([text_annotation,image,image_annotation])Add note to reviewreview.add_note(note)Write media review to disk# Write next to the mediareview.write()# Specifying a directoryreview.write("/path/to/review_dir")Export/Import a note as zip# Exportexported_note_path=note.export("/path/to/folder",compress=True)# Importreview.import_note(exported_note_path)ExamplesFrom content to review# Contenttext=orio.Content.TextComment(body="Banana")# Notenew_note=orio.Note("Michel",content=text)# Reviewreview=orio.MediaReview("/path/to/media.ext",note=new_note)Reply to a note# Main notetext=orio.Content.TextComment(body="Make the logo bigger.")main_note=orio.Note("Michel",content=text)# Reply to the main notereply=orio.Content.TextComment(body="Done, I'm waiting for my visibility payment.")note_reply=orio.Note("Michel",content=reply,parent=main_note)Add a reference image to an image annotationUseful for keeping an image as reference of the drawing.image_annotation=orio.Content.ImageAnnotation(frame=17,duration=20,path_to_image="/path/to/image_annotation.png",reference_image="/path/to/reference_image.png")# Orimage_annotation.reference_image="/path/to/reference_image.png"Copyright 2020, Félix David ©
Package: openrgb-python
OpenRGB-PythonOpenRGB-Python is a client for theOpenRGB SDK,
a manufacturer-independent, cross-platform way to control your RGB devices.
OpenRGB-Python can be used to interface with other programs, create custom
effects, or anything else you can think of!I started this project because I wanted to be able to control the color of my lights based on temps. I tried to implement the features in the best (easiest to use) way possible.SDK Feature Support:Setting client nameGetting device infoSetting color by deviceSetting color by zoneSetting color by ledSetting modeSetting custom modeResizing zonesAdditional Features (Not part of the OpenRGB SDK):Loading profilesSaving profilesInstallationrequires python >= 3.7Use this method for the newest, but possibly buggy, package:pip3 install git+https://github.com/jath03/openrgb-python#egg=openrgb-pythonArch Linux:yay -S python-openrgb-gitFor a more stable package:pip3 install openrgb-pythonArch Linux:yay -S python-openrgbThanks to @GabMus for the AUR packagesUsagefromopenrgbimportOpenRGBClientfromopenrgb.utilsimportRGBColor,DeviceTypeclient=OpenRGBClient()client.clear()# Turns everything offmotherboard=client.get_devices_by_type(DeviceType.MOTHERBOARD)[0]motherboard.set_color(RGBColor(0,255,0))motherboard.zones[0].set_color(RGBColor(255,0,0))motherboard.zones[1].leds[0].set_color(RGBColor.fromHSV(0,100,100))motherboard.set_mode("breathing")client.save_profile("profile1")Documentationhttps://openrgb-python.readthedocs.io/en/latest/For an alternative python implementation, check outB Horn'sOpenRGB-PyClient