Saravanan666/pypi-org huggingface dataset:
Package: danceschool-dancervax
TheDancerVaxsite is designed to allow for simple
vaccination record lookup for participating organization. Lookups can happen
using the site itself, or using the included REST API.This package provides easy DancerVax Integration to the Django Dance School
project.  Once installed, the default check in views will automatically call
the DancerVax API to check the vaccination status of potential participants at
the time of check in.  This way, vaccination checks are completely integrated
with the existing check-in workflow.Installation instructionsSign up as an organization with DancerVax, and await approval to begin using
the lookup system.Request a DancerVax API key (client key and secret key).Install this package.:pip install danceschool-dancervaxNote that in a production environment, you may need to simply add danceschool-dancervax
to your project’s requirements.txt.Edit your project’s settings.py to add danceschool-dancervax to INSTALLED_APPS,
and to add your API keys:INSTALLED_APPS = [
   ''' Other packages '''
   'danceschool_dancervax',
]

DANCERVAX_CLIENT_ID = '<your_id_here>'
DANCERVAX_CLIENT_SECRET = '<your_secret_here>'Once these steps are complete, automatic lookup of DancerVax vaccination status
should be enabled by default. On the ‘View registrations’ pages, each individual’s
DancerVax status will be reported. The app also makes use of Django’s built-in
cache capabilities; a request for information related to a specific event
registration will only be repeated once per day, unless the name or email address
associated with the registration changes.Who do I talk to about additional questions?For technical questions related to this library, use the issue tracker or email
Lee Tucker:lee.c.tucker@gmail.comFor issues related to DancerVax itself, please contactinfo@dancervax.org.
Package: dandanleinad-image-processing
package_nameDescription.
The package package_name is used to:
Processing:
- Histrogram Matching
- Structural similarity
- Resize image
Utils:
- Read image
- Save image
- Plot image
- Plot result
- Plot histrogramInstallationUse the package managerpipto install package_namepipinstallpackage_nameUsagefrompackage_name.module1_nameimportfile1_namefile1_name.my_function()AuthorDanielLicenseMIT
Package: dandelion-data-schema
Dandelion Data SchemaThe Dandelion Data Schema describes the structure of data used in the Data Validation Service provided by Dandelion Health.Example usageLoad an example dataset into python.importjsonfromdandelion_data_schema.recordimportRecordwithopen('tests/data/dataset.json','r')asfp:dataset=json.load(fp)dataset=Record.model_validate(dataset)Distribute on pypipython3setup.pysdistbdist_wheel
python3-mtwineuploaddist/*
Package: dandelion-eu
python-dandelion-euBring the power of thedandelion.eusemantic to your python applications and scripts!
Semantic in python couldn’t be easier.>>>fromdandelionimportDataTXT>>>datatxt=DataTXT(token='YOUR_TOKEN')>>>response=datatxt.nex('The doctor says an apple is better than an orange')>>>forannotationinresponse.annotations:print(annotation)...Register ondandelionto obtain your authentication token and enrich your application with our semantic intelligence.Installationdandelion-euis available onPyPIinstall it simply with:pip install dandelion-euDocumentationDocumentation is available onReadTheDocs.
Package: dandi
DANDI ClientTheDANDI Python clientallows you to:DownloadDandisetsand individual subject folders or filesValidate data to locally conform to standardsOrganize your data locally before uploadUploadDandisetsInteract with the DANDI archive's web API from PythonDelete data in the DANDI archivePerform other auxiliary operations with data or the DANDI archiveNote: This project is under heavy development. Seethe issues logorWork-in-Progress (WiP).InstallationDANDI Client releases areavailable from PyPIandconda-forge.  Install them in your Python (native, virtualenv, or
conda) environment viapip install dandiorconda install -c conda-forge dandiCLI ToolThis package provides a command line utility with a basic interface
to help you prepare and upload your data to, or obtain data from, theDANDI archive.$>dandi
Usage:dandi[OPTIONS]COMMAND[ARGS]...AclienttosupportinteractionswithDANDIarchive(http://dandiarchive.org).Toseehelpforaspecificcommand,rundandiCOMMAND--helpe.g.dandiupload--help

Options:--version-l,--log-level[DEBUG|INFO|WARNING|ERROR|CRITICAL]Loglevel(caseinsensitive).Maybespecifiedasaninteger.[default:INFO]--pdbFallintopdbiferrorsout--helpShowthismessageandexit.

Commands:deleteDeletedandisetsandassetsfromtheserver.digestCalculatefiledigestsdownloadDownloadafileorentirefolderfromDANDI.instancesListknownDandiArchiveinstancesthattheCLIcan...lsList.nwbfilesanddandisetsmetadata.moveMoveorrenameassetsinalocalDandisetand/oron...organize(Re)organizeNWBfilesaccordingtotheirmetadata.shell-completionEmitshellscriptforenablingcommandcompletion.uploadUploadDandisetfilestoDANDIArchive.validateValidatefilesfordatastandardscompliance.Rundandi --helpordandi <subcommand> --help(e.g.dandi upload --help) to see manual pages.ResourcesTo learn how to interact with the DANDI archive and for examples on how to use the DANDI Client in various use cases,
seethe handbook(specifically the sections on using the CLI todownloadanduploadDandisets).To get help:ask a question:https://github.com/dandi/helpdesk/discussionsfile a feature request or bug report:https://github.com/dandi/helpdesk/issues/new/choosecontact the DANDI team:help@dandiarchive.orgTo understand how to contribute to the dandi-cli repository, see theDEVELOPMENT.mdfile.
Package: dandischema
DANDI Schemadandi-schemais a Python library for maintaining and managing DANDI metadata schemata.Installationpip install dandischemaDescriptionEveryDandisetand associated asset has a metadata object that can be retrieved using
the DANDI API. This library helps create and validate DANDI schema-compliant metadata forDandisetsand assets. It usesPydanticto implement
all the metadata classes. Schemas are generated on schema modifications and placed intothis repository.dandi-schemaalso generates JSON schema definitions and an associatedcontext.jsonfile for JSON-LD compliance of the metadata models.Important files in this repository include:models.py- contains the modelsmetadata.py- contains functions for validating, migrating, and aggregating metadatadatacite.py- converts theDandisetmetadata to a Datacite metadata structureThe generated JSON schemas can be used together withVJSFto create a UI
forDandisetmetadata modification as used for Dandiset metadata modification onhttps://dandiarchive.org.Also Pydantic models are used byDANDI Client/Libraryto validate
metadata while submitting data to the archive, and later by theDANDI Archiveitself to ensure
that all metadata conforms the model before Dandiset is allowed to be published and gain a DOI.
Such DOI generation is done via Datacite service, anddandi-schemalibrary produces Datacite metadata records
out of the Pydantic models.ResourcesTo learn how to interact with the DANDI archive,
seethe handbook.To file a feature request or bug report, go tohttps://github.com/dandi/helpdesk/issues/new/choose.For all other issues, contact the DANDI team:help@dandiarchive.org.
Package: dandori
dandori: GitHub Actions with Pythondandori runs on your Actions, and automate workflow with using Python.Current Status is Super Early Alpha. DO NOT USE IT in your production repository.How to UseFirst, You need to define workflow.
You can hook anyeventswithout manual/scheduled workflow such aspull_request,pushorpull_request_review.name:dandori_actionon:[pull_request,issue_comment]jobs:run:runs-on:ubuntu-lateststeps:-uses:actions/checkout@v2-uses:actions/setup-python@v2with:python-version:'3.9'-run:pip install dandori-run:dandori runenv:GITHUB_TOKEN:${{ secrets.GITHUB_TOKEN }}Next, you can write your Python script or package on your repo, likedandori_handler.pyor 'dandori_handlers/'. dandori automatically import your code and run handler functions defined in your code:## dandori_handler.pydefhandle_pull_request(ctx):ifctx.gh.payload.action=='synchronize':ctx.gh.create_comment("You pushed new commits!!")defhandle_pull_request_comment(ctx):"""It's a special handler type, issue_comment event on PR"""comment_body=ctx.gh.comment_body().strip()ifcomment_body.startswith('/some-command'):some_code_as_you_like()If you want more than one file, you need to make a package:## handlers/__init__.py# Must be relative importsfrom.pull_requestimporthandle_pull_requestfrom.issueimporthandle_issue## handlers/pull_request.pydefhandle_pull_request(ctx):...## -- handlers/issue.pydefhandle_issue(ctx):...Configurationdandori supportspyproject.toml, or make any toml file as you like (default isdandori.toml).In pyproject.toml, write config intool.dandorisection:# pyproject.toml[tool.dandori]handlers=['path/to/handler']In independent toml file, write config indandorisection:# dandori.toml[dandori]handlers=['path/to/handler']Use caseShare CI code with multiple repo:name:dandori_actionon:[pull_request,issue_comment]jobs:run:runs-on:ubuntu-lateststeps:-uses:actions/checkout@v2-uses:actions/checkout@v2with:repository:your/dandori-handlerref:v1# something you needssh-key:${{ secrets.your_repo_key }}path:dandori-handler-uses:actions/setup-python@v2with:python-version:'3.9'-run:pip install dandori-run:dandori run -f dandori-handler/dandori.tomlenv:GITHUB_TOKEN:${{ secrets.GITHUB_TOKEN }}Use third party package in handlerYour handler module or package will be imported dynamically, so you can install any library in the "(virtual)env" same as dandori installed.Most simple cale, install dandori with other library:# Install libraries with pip
- run: pip install dandori requests python-dateutilIf you want use just a "command" and not to use global env, usectx.ops.run_venv():defhandle_pull_request(ctx):ctx.ops.run_venv(["pip","install","twine"])ctx.ops.run_venv(['twine','upload','dist/*'])Or dynamically install it and use it:importimportlibdefhandle_pull_request(ctx):ctx.ops.run(["pip","install","requests"])requests=importlib.import_module('requests')
Package: dane
DANEThe Distributed Annotation 'n' Enrichment (DANE) system handles compute task assignment and file storage for the automatic annotation of content.This repository contains contains the building blocks for with DANE, such as creating custom analysis workers or submitting new task.InstallationThis package can be installed through pip:pip install daneConfigurationDANE components are configured through the dane.config module, which is described here:https://dane.readthedocs.io/en/latest/intro.html#configurationIt is however noteable that, because all DANE components are expected to rely on it, some of the DANE-server, ElasticSearch and RabbitMQ configuration
are included in the default config. As such it is recommended that you create a$HOME/.dane/config.ymlor$DANE_HOME/config.ymlwhich contain machine-wide settings for how to connect to these services, which involves specifying the following settings:DANE:
    API_URL: 'http://localhost:5500/DANE/'
    MANAGE_URL: 'http://localhost:5500/manage/'
RABBITMQ:
    HOST: 'localhost'
    PORT: 5672
    EXCHANGE: 'DANE-exchange'
    RESPONSE_QUEUE: 'DANE-response-queue'
    USER: 'guest'
    PASSWORD: 'guest'
ELASTICSEARCH:
    HOST: ['localhost']
    PORT: 9200
    USER: 'elastic'
    PASSWORD: 'changeme'
    SCHEME: 'http'
    INDEX: 'your_dane_index'The values given here are the default values.UsageExamples of how to use DANE can be found in theexamples/directory.Local DevelopmentWe moved fromsetup.py&requirements.txtto a singlepyproject.toml. For local builds and publishing we usepoetry.For local installation:poetryinstall
poetryshellAfter installation the following unit test should succeed:python-mtest.test_daneTo build a wheel + source package (will end up indistdirectory):poetrybuildThe wheel can be conveniently tested in e.g. your own DANE worker by installing it e.g. usingpip:pipinstallpath_to_dane_wheel_fileor with poetrypoetryaddpath_to_dane_wheel_fileBreaking changes after 0.3.1Since version 0.3.1 DANE must be imported in lowercase letters:importdaneBefore version 0.3.1 you should import using uppercase letters:importDANE
Package: dane-discovery
A library for usingDANE TLSA recordsfor
certificate discovery.Quick StartInstallationpip installdane-discoveryLoad a certificate from DNS and print the PEM representationfromdane_discovery.daneimportDANEfromdane_discovery.pkiimportPKIdns_name="dns.name.having.a.tlsa.record"tlsa_record=DANE.get_first_leaf_certificate(dns_name)ifnottlsa_record:raiseValueError("No leaf certificate found for{}.".format(dns_name))der_cert=PKI.certificate_association_to_der(tlsa_record["certificate_association"])print(PKI.der_to_pem(der_cert))Load a DANE identity from DNS and print the request contextfromdane_discovery.identityimportIdentitydns_name="dns.name.having.a.tlsa.record"dane_identity=Identity(dns_name)print(dane_identity.report())Name:abc123.air-quality-sensor._device.example.netRequestcontext:DNSSEC:FalseTLS:FalseTCP:TrueCredentialindex:0certificateusage:DANE-EEselector:Fullcertificatematchmatchingtype:Exactmatchagainstcertificateassociationx509attributes:{'extensions':{'BasicConstrints':{'ca':False,'path_length':None},'KeyUsage':{'content_commitment':True,'crl_sign':False,'data_encipherment':False,'digital_signature':True,'key_agreement':False,'key_cert_sign':False,'key_encipherment':True}},'subject':{'commonName':'abc123.air-quality-sensor._device.example.net','countryName':'US','organizationName':'Example Networks','stateOrProvinceName':'CA'}}More examples
Package: dane-jwe-jws
A library for using JWE and JWS with DANE for identity-secured messaging.This library enables the easy generation of signed and encrypted messages,
using TLSA records in DNS for public key discovery. This library places the
DNS URI in thex5uprotected header field. The receiving party uses this
field for discovering the public key which is used for verifying message
payload.Note: Currently, only RSA public keys are supported (RSA-OAEP-256 for
encryption and RS256 for signing)Quick StartInstallationpip installdane-jwe-jwsEncrypt a message using a DANE-represented identityfromdane_jwe_jws.encryptionimportEncryptiontest_message="hello world!!"identity_name="dns.name.where.cert.lives.in.a.tlsa.record"encrypted=Encryption.encrypt(test_message,identity_name)print(encrypted)More examples
Package: daner
danerGithub-flavored Markdowninstallpipinstalldanerexamplesimple usagefromdanerimportdong# command# python manage.py hi --name milisp@dong()asyncdefhi(name:str):print(name)# command# python manage.py hi -u milisp# alias u=username@dong(alias={"u":"username"})asyncdefcreate(username:str):print(username)# command# python manage.py greet --name milisp# if you want to run a sync function@dong(sync=True)defgreet(name):print(name)create superuser# manage.py# createsuperuser# $ python manage.py createsuperuser -u username -e emailfromdanerimportdongimportdatabasesimportgetpassimportormarimportsqlalchemyimportuvicornDB_URI="sqlite:///fast.db"database=databases.Database(DB_URI)engine=sqlalchemy.create_engine(DB_URI)metadata=sqlalchemy.MetaData()metadata.create_all(engine)classUser(ormar.Model):classMeta(ormar.ModelMeta):metadata=metadatadatabase=databaseid=ormar.Integer(primary_key=True)username=ormar.String(max_length=60,unique=True)password=ormar.String(max_length=2048)email=ormar.String(max_length=80,nullable=True,unique=True)@dong(alias={"u":"username","e":"email"})asyncdefcreatesuperuser(username:str,email:str=None):awaitdatabase.connect()password=getpass.getpass("password: ")password="hashed"+passwordawaitUser(username=username,password=password,email=email,is_superuser=True,).save()awaitdatabase.disconnect()
Package: dangermode
🚨 DangermodeOverview🚨 Dangermode is a ChatGPT Plugin written with Python and FastAPI that allows  ChatGPT to execute code snippets in an IPython session, whether it's the console, the notebook, or a JupyterLab session.⚠️ Dangermode is a proof-of-concept and should not be used in production. It is not secure and should not be publicly exposed to the web. ⚠️🚨🚨🚨YOU ARE GIVING AI THE ABILITY TO EXECUTE CODE ASYOU.🚨🚨🚨Running this plugin on your machine is dangerous. Running this plugin on a public server isextremelydangerous. You have been warned.DemoFeaturesExecute code snippets in an IPython session through ChatGPT.Inspect variables and view results in real-time.Serve images and display data to ChatGPT.Get started, the (slightly) less dangerous waySince you don't want to unleash ChatGPT directly onto your literal machine (including your files) etc. Build the docker image and run it locally like this:docker build . -t dangermode
docker run -p 8000:8000 -i -t --rm dangermode(SCARY DANGER MODE) InstallationIf you're feeling brave , you can installdangermodedirectly viapip,conda, or clone the repository and install it locally. If youreallyaren't worried about security, go for it. You have been warned.Run Danger Modeimport dangermode
# You must set the host to bind to all addresses when using Docker.
# Since this is dangerous, I leave it as an exercise to the reader.
dangermode.activate_dangermode()Enabling on ChatGPTIn order to use this plugin, you have to haveChatGPT Plugin access.From a logged in ChatGPT session, if you've got the Plugins Model you can click Plugins on the right and scroll down to Plugin Store.Next, click "Develop your own plugin".Enterlocalhost:8000as the domain.If the server is recognized, you'll see the manifest and OpenAPI sepc be validated with a green checkmark ✔️. Click "Install localhost plugin" and start using it!API EndpointsGET /openapi.json: Retrieve the OpenAPI JSON configuration.GET /.well-known/ai-plugin.json: Retrieve the AI plugin JSON configuration.GET /images/{image_name}: Retrieve an image by its name.GET /api/variable/{variable_name}: Retrieve the value of a variable by its name.POST /api/run_cell: Execute a code cell and return the result.ContributingPlease do. I can't let Large Language Models write all of it.Please fork the repository, make your changes, and submit a pull request.LicenseDangermode is released under the BSD 3-Clause License. SeeLICENSEfor more information.ContactIf you have any questions or feedback, please feel free to reach out to the author, Kyle Kelley, on Twitter at@KyleRayKelleyor just open an issue on the repository.
Package: danger-noodle
Danger Noodle (dnd for short)Python module for handling npm modules available on unpkg.comInstallpipinstalldanger-noodleUsagednd install: Install all packages based on a snakeskin.txtdnd install <package-name>: Install a package that best matches the provided package name and appends the package and version to snakeskin.txtdnd uninstall <package-name>: Uninstall a package that best matches the provided package name and appends the package and version to snakeskin.txtExample commandsdnd install bootstrapdnd uninstall bootstrapdnd install jquery@3.3.0dnd uninstall jquery@3.3.0
Package: dangling-finder
dangling-finderFind dangling commits inside your GitHub repositories.IntroductionThis is an attempt to find ways to recover dangling commits inside a GitHub repository, to help you improve your use of repository secret scanning tools liketrufflehogorgitleaks.
For now, only one way is used:recover allforce-pushedevents in a pull request and list all former HEADs of the PR (most probably dangling-commits)TODO: get all available Push events from GitHub API (but only the X last events can be retrieved)TODO: add closed and not merged PR, in addition to their lost force-pushed commitsTODO: try with user specific events to get more dangling commitsLimitationsThis tool only focuses on potential dangling-commits sources. It doesn't list:current HEADS of pull requests (whether opened, merged or closed - TODO: check if closed PRs are already covered by popular tools)parent dangling-commits of the dangling HEADs found in a "force-pushed" event (git fetchcan be used to avoid thinking about this, see below inUsage)the content of the dangling-commits found: it would require to browse all commits from the dangling HEADs found (unecessary if you usegit fetch) and to have a way to get the content of each commit (the GitHub GraphQL API does not seem to provide a way to do so, and it would cost too much using the REST API -git fetchavoid us this trouble)Installationgitclonegit@github.com:MickaelFontes/dangling-finder.git&&cddangling-finder
poetryinstallUsageTo show the help, run:poetryrundangling-finder-hTo use the commands, you will need to provide a GitHub API token. Read the documentationhereto generate a token.Use the script to find the dangling heads and use the generated script with the dangling heads to add the dangling commits in the clone repo.poetryrundangling-finderownerrepo--github-token$GITHUB_TOKEN--git-script>owner-repo-dangling-scirpt.sh
gitclonegit@github.com:owner/repo.git&&cdowner/repo
chmod+x../owner-repo-dangling-scirpt.sh&&bash../owner-repo-dangling-scirpt.shThen scan the repo local for secrets with your favorite tool.
Package: dango
dangodangois an easy to use tokenizer for Japanese text, aimed at language learners and non-linguists.$echo"私は昨日映画を見ました"|dango私は昨日映画を見ましたIf used as a library it can also provide you with additional information such as:Dictionary form: For inflected words it can tell you the dictionary form for easier lookup.Part-of-speech tagging: It can tell you if a word is a verb, noun, adjective, etc.Reading in hiragana for words containing kanjiInstallation$pipinstalldangoOne of the dependencies isSudachiDict-core, which might take a while to download due to its size of ~70MB.UsageA simple CLI for tokenizing text is provided. Input is read fromstdinor from a file.$echo"私は昨日映画を見ました"|teeinput.txt|dango
私は昨日映画を見ました

$dangoinput.txt
私は昨日映画を見ましたUsage as a library:importdangowords=dango.tokenize('私は昨日映画を見ました')print([w.surfaceforwinwords])# => ['私', 'は', '昨日', '映画', 'を', '見ました']print(words[-1].part_of_speech)# => VERBprint(words[-1].surface)# => 見ましたprint(words[-1].surface_reading)# => みましたprint(words[-1].dictionary_form)# => 見るprint(words[-1].dictionary_form_reading)# => みるMotivation & Acknowledgementsdangowas created out of a need to extract vocabulary in bulk from Japanese
texts to serve as learning materials.While you can get quite far by using a morphological analyzer likeMeCabdirectly, there is the problem that it will segment text into much smaller
units than one would like if you are trying to learn the language.
For example見たwould be separated into見andたwhich is a bit like
separatingwatchedintowatchanded.dangousesSudachiPyfor tokenization/analysis and adds some processing
to aggregate the individual tokens into words and make the part-of-speech
information a bit easier to digest.dangotakes some inspiration fromVe, which provides the text parsing ofjisho.org.LicenseReleased under the BSD-3-Clause License
Package: daniel-cavazos-sdk
Daniel Cavazos SDKThedaniel-cavazos-sdkpackage provides a Python client library for interacting with The One API. This library provides a convenientTheOneClientclass that provides methods for interacting with The One API.InstallationYou can install thedaniel-cavazos-sdkpackage usingpip:pip install daniel-cavazos-sdkUsageFirst, import theTheOneClientclass from thedaniel_cavazos_sdkmodule:fromdaniel_cavazos_sdk.clientimportTheOneClientThen, create an instance of theTheOneClientclass with your API token:client=TheOneClient(token="your-api-token")Now, you can use the various methods available in theTheOneClientclass to interact with TheOneAPI:# get all moviesall_movies=client.get_movies()# get a specific movie by IDmovie=client.get_movie(id="123")# get all quotes for a specific movie by IDmovie_quotes=client.get_movie_quotes(id="123")# get all quotesall_quotes=client.get_quotes()# get a specific quote by IDquote=client.get_quote(id="123")Each method returns the parsed JSON response from the API. If the response has a non-2xx status code, arequests.exceptions.HTTPErroris raised.ContributingIf you find any bugs or issues with this package, please report them on theGitHub Issues page.If you would like to contribute to the development of this package, please fork the repository and submit a pull request with your changes.LicenseThis package is distributed under the MIT License. See theLICENSEfile for more information.