Saravanan666/pypi-org huggingface dataset:
Package: medium-crosspost
medium-crosspostA Python script to crosspost articles to Medium.Accompaniesthis blog post, which goes into depth on crossposting betweenGhostandMedium.Installation:pip install medium-crosspostBasic Usage:from medium_crosspost import MediumCrosspost

input_data = {
    "title": "Fantastic Article Name!",
    "canonicalUrl": "https://www.example.com/fantastic-article-name",
    "integrationToken": "super-secret-medium-integration-token",
    "content": "<html><head></head><body>Content is all about actual HTML-encoded article content.</body></html>",
    "tags": "can,be,a,list,or,comma,separated,string",
}

crosspost = MediumCrosspost(input_data)
result = crosspost.post()
Package: mediumMuncher
Medium MuncherMedium makes content readily available for machine consumption through theformat=jsonparam, and content feeds via thefeedspath, however the XML and JSON responses are not exactly plug-and-play for redisplaying content. Enter this package.Installationvia pip withpip3 install mediummuncherUsageGetting a story as stand-alone HTML (with head and body tags)from medium_muncher import MediumMuncher

muncher = MediumMuncher()
full_html=muncher.munch_story('https://medium.com/some-author/some-amazing-article-039525')
#returns "<!doctype html><head>... "Getting a story as an html snippet (no head or body)html_snippet=muncher.munch_story('https://medium.com/some-author/some-amazing-article-039525',snippet=True)
#returns "<p>article text!..."Using theverboseflag returns a tuple with the html and a dictionary of interesting article attributes such as title, published date etc.html_snippet=muncher.munch_story('https://medium.com/some-author/some-amazing-article-039525',snippet=True, verbose=True)
#returns tuple ("<p>article text!...", {"title":"this amazing article"...,)Getting all the story urls for a given authorstories=muncher.munch_author_feed('some-author')
#returns tuple ("https://medium.com/some-author/amazing-article-one-12902990",..,)Putting it all together to extract all stories for a given authorstories=list()
for url in muncher.munch_author_feed('ethan.m.knox'):
    stories.append(muncher.munch_story( url,
                                        snippet=True,
                                        verbose=True))
print(list)ContributingPlease feel free to fork and PR! Can always use another helping hand.
Package: mediumroast-py
IntroductionThis is the Python Software Development Kit (SDK) for the mediumroast.io.  Internal tooling from Mediumroast, Inc. uses this SDK, so it will always be a first class citizen. Specifically, we build tools requiring ETL (Extract, Transform and Load), Machine Learning and Natural Language Processing (NLP) with this SDK. As appropriate examples illustrating various capabilities of the SDK can be found in themediumroast_examples/directory of this package.Current statusThis SDK mirrors the capabilities of the mediumroast.io application server.  Therefore it is fully functional mapping to the capabilities aavailable from the server.  Specifically this means the SDK is capable of:Creating interaction, company, study and user objectsReporting on or listing interaction, company, study and user objectsTransforming raw inputs into respective objects.Ingesting metadata from S3 buckets, provided they match a file name specification, into raw inputs that can be transformed.
Many of these functionalities have example implementations that enable the developer to understand how to use the SDK.  Also the file name specification can be found in the githubmediumroast_examples directoryof the repository for mediumroast_py.The ExamplesTo illustrate how to interact programmatically with the mediumroast.io application several examples have been created to make it easier for developers to interact with the system.  The scope of the examples, over time, will include all aspects of the SDK to speed up 3rd party development processes.  This means that over time examples for all apsects of the SDK will be produced and made available under the Apache Software License.  As with anything in the SDK if you run into problems please make sure that you raise anissuein the repository.  More detail on the examples can be found within theexamples directoryof this repository.InstallationSince the package is under heavy development please check in often and upgrade frequently.InstallationThis package is available on PyPi and can be installed via pip use:pip install mediumroast_pyUpgradeYou can perform an upgrade using pip:pip install mediumroast_py --upgradeAvailability of mediumroast_examplesInstallation through pip fromPyPithe examples will be installed indist-packages/mediumroast_examples. For example if you're using Python 3.8 withdist-packageskept in/usr/localthen this would be the path for the examples:/usr/local/lib/python3.8/dist-packages/mediumroast_examples.Structure of the repositoryThe following structure is available for the Python SDK, as new SDK implementations are created additional top level directories will be created.mediumnroast_py/
      mediumroast_examples/
      mediumroast_py/
            api/
            extractors/
            transformers/
            helpers.py
      project.toml
      README.md
      LICENSE
Package: medium-speech
medium-to-speechMedium posts to Speech.A Python library for lazy people (like myself), who never finds time to read dailyMediumposts and prefer to listen to them instead. It convertmediumpost to markdown using aDocker container/imagethen usesgTTsto interface with Google Translate's text-to-speech API which converts text to spokenMP3files, thereafter plays the generatedmp3files withcvlcassuming it is installed.Apt RequirementsYou will need to install a few dependencies before you continue:sudoaptinstall-ydocker-cempg123python3.6python3.6-devvlcInstallationTo install run:python3-mpipinstall-Umedium-speechUsageAvailable arguments:play_medium_post.py-h

usage:play_medium_post.py[-h][--play][--cleanup][--speedN_SPEED][--loglevelLOG_LEVEL][--url-postMEDIUM_URL][--fileMARKDOWN_FILE]optionalarguments:-h,--helpshowthishelpmessageandexit--play,-pPlaygeneratedMP3files.--cleanup,-cCleanupgeneratedMP3files.--speedN_SPEED,-sN_SPEEDPlayeveryn'thframeonlyiePlayspeed.--loglevelLOG_LEVELlogleveltouse,default[INFO],options[INFO,DEBUG,ERROR]--url-postMEDIUM_URL,-uMEDIUM_URLMediumpostURL.--fileMARKDOWN_FILESpecifyaMarkdownfile.Listen to aMediumpost:play_medium_post.py-ps1-uhttps://medium.com/@mmphego/how-i-managed-to-harness-imposter-syndrome-391fdb754820Listen to Markdown file:play_medium_post.py-ps1--fileREADME.mdOh, Thanks!By the way... thank you! And if you'd like tosay thanks... :)✨🍰✨FeedbackFeel free to fork it or send me PR to improve it.
Package: medivisio
MediVisioMediVisio is a Python package designed to facilitate the visualization of 3D DICOM and NII (NIfTI) images. With its intuitive interface and extensive functionality, MediVisio allows users to explore, analyze, and interpret medical imaging data in a convenient and interactive manner.Features3D Visualization: Display DICOM and NII images in a three-dimensional space, enabling a comprehensive view of medical data from various angles and perspectives.InstallationYou can easily install MediVisio using pip, as shown below:pipinstallmedivisio## Getting StartedPleasenotethatMediVisiorequiresaPythonenvironmentwiththenecessarydependencies,suchasNumPy,Matplotlib,and
PyDICOM,Nibabel.TogetstartedwithMediVisio,youcanrefertotheprovidedexamplesanddocumentation.### UsecaseDatadirectorypath
Patients|--PatientID|--DICOM|--N2DSlices|--NII|--N2DSlicesor3DTensor'''python"""src_dir: Patientsimg_type: "DICOM" or "NII"img_label: "DICOM_Labels" or "NII_Labels""""import MediVisio#Deafault arguments#alpha = 0.99, applymask = Truemedivisio = MediVisio(src_dir, img_type, img_label)'''ReadsandPlots3DData.Youcanscrollslicesusingyourmouse.Thefirstsliceshownisthemiddleslice'''pythonmedivisio.read_data()'''
Package: medizum
Me Diz UmPacote feito para o trabalho final da disciplina Programação Orientada a Objetos II.Este pacote gera dados simples aleatóriamente.Para utilizar é necessário ter instalado as bibliotecas:pandaspillow.Categorias presentes:NomesIdadePesoAlturaPaisesEstados (do Brasil)Cidades (do Brasil)CoresImagemArquivoCPFRGProdutosSignosDias da SemanaMesesDatas de NascimentoExemplos de uso:from medizum import diz
diz = diz()
print(diz.nome())
print(diz.idade())
print(f'Oi, me chamo {diz.nome()} e tenho {diz.idade()}!')
Package: me-django-base
Base is a Django app to conduct Web-based base. For each
question, visitors can choose between a fixed number of answers.Detailed documentation is in the “docs” directory.Quick startAdd “base” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'base',
]Include the polls URLconf in your project urls.py like this:url(r'^base/', include('base.urls')),Runpython manage.py migrateto create the base models.Start the development server and visithttp://127.0.0.1:8000/admin/to create a base (you’ll need the Admin app enabled).Visithttp://127.0.0.1:8000/base/to participate in the base.
Package: me-django-util
Util is a Django app to conduct Web-based base. For each
question, visitors can choose between a fixed number of answers.Detailed documentation is in the “docs” directory.Quick startAdd “util” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...
    'util',
]Include the polls URLconf in your project urls.py like this:url(r'^util/', include('util.urls')),Runpython manage.py migrateto create the util models.Start the development server and visithttp://127.0.0.1:8000/admin/to create a util (you’ll need the Admin app enabled).Visithttp://127.0.0.1:8000/util/to participate in the base.
Package: medkit-learn
The Medkit-learn(ing) EnvironmentAlex J. Chan, Ioana Bica, Alihan Huyuk, Daniel Jarrett, and Mihaela van der SchaarThe Medkit-Learn(ing) Environment, or Medkit, is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.Primarily, Medkit is a tool that supports: (1) a variety of realistic environment models—learned from actual data, to reflect real medical settings), thus allowing simulation of (2) a variety of expressive and customisable policy models that represent complex human decision-behaviours; as well as (3) ensuring that the environment and policy components are disentangled—hence independently controllable.By fulfilling the above, Medkit seeks to enable advances in decision modelling to be validated more easily and robustly by enabling users to obtain batch datasets with known ground-truth policy parameterisations that simulate decision making behaviours with various degrees of Markovianity, bounded rationality, confounding, individual consistency and variation in practice.Medkit is pip installable - we recommend cloning it, optionally creating a virtual env, and installing it (this will automatically install dependencies):gitclonehttps://github.com/XanderJC/medkit-learn.gitcdmedkit-learn

pipinstall-e.Example usage:importmedkitasmksynthetic_dataset=mk.batch_generate(domain="Ward",environment="CRN",policy="LSTM",size=1000,test_size=200,max_length=10,scale=True)static_train,observations_train,actions_train=synthetic_dataset['training']static_test,observations_test,actions_test=synthetic_dataset['testing']While medical machine learning is by necessity almost always entirely offline, we also provide an interface through which you can interact online with the environment should you find that useful. For example, you could train a custom RL policy on this environment with a specified reward function, then you can test inference algorithms on their ability to represent the policy.env=mk.live_simulate(domain="ICU",environment="SVAE")static_obs,observation,info=env.reset()observation,reward,info,done=env.step(action)CitingIf you use this software please cite as follows:@misc{chan2021medkitlearning,title={The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation},author={Alex J. Chan and Ioana Bica and Alihan Huyuk and Daniel Jarrett and Mihaela van der Schaar},year={2021},eprint={2106.04240},archivePrefix={arXiv},primaryClass={cs.LG}}
Package: medkit-lib
medkitCIPackageProjectmedkitis a toolkit for a learning health system, developed by theHeKA research team.This python library aims at:Facilitating the manipulation of healthcare data of various modalities (e.g., structured, text, audio data)
for the extraction of relevant features.Developing supervised models from these various modalities for decision support in healthcare.InstallationTo installmedkitwith basic functionalities:pip install medkit-libTo installmedkitwith all its optional features:pip install medkit-lib[all]ExampleA basic named-entity recognition pipeline usingmedkit:# 1. Define individual operations.frommedkit.text.preprocessingimportCharReplacer,LIGATURE_RULES,SIGN_RULESfrommedkit.text.segmentationimportSentenceTokenizer,SyntagmaTokenizerfrommedkit.text.context.negation_detectorimportNegationDetectorfrommedkit.text.ner.hf_entity_matcherimportHFEntityMatcher# Preprocessingchar_replacer=CharReplacer(rules=LIGATURE_RULES+SIGN_RULES)# Segmentationsent_tokenizer=SentenceTokenizer(output_label="sentence")synt_tokenizer=SyntagmaTokenizer(output_label="syntagma")# Negation detectionneg_detector=NegationDetector(output_label="is_negated")# Entity recognitionentity_matcher=HFEntityMatcher(model="my-BERT-model",attrs_to_copy=["is_negated"])# 2. Combine operations into a pipeline.frommedkit.core.pipelineimportPipeline,PipelineStepner_pipeline=Pipeline(input_keys=["full_text"],output_keys=["entities"],steps=[PipelineStep(char_replacer,input_keys=["full_text"],output_keys=["clean_text"]),PipelineStep(sent_tokenizer,input_keys=["clean_text"],output_keys=["sentences"]),PipelineStep(synt_tokenizer,input_keys=["sentences"],output_keys=["syntagmas"]),PipelineStep(neg_detector,input_keys=["syntagmas"],output_keys=[]),PipelineStep(entity_matcher,input_keys=["syntagmas"],output_keys=["entities"]),],)# 3. Run the NER pipeline on a BRAT document.frommedkit.ioimportBratInputConverterdocs=BratInputConverter().load(path="/path/to/dataset/")entities=ner_pipeline.run([doc.raw_segmentfordocindocs])Getting startedTo get started withmedkit, please checkout ourdocumentation.This documentation also contains tutorials and examples showcasing the use ofmedkitfor different tasks.ContributingThank you for your interest into medkit !We'll be happy to get your inputs !If your problem has not been reported by another user, please open anissue, whether it's for:reporting a bug,discussing the current state of the code,submitting a fix,proposing new features,or contributing to documentation, ...If you want to propose a pull request, you can readCONTRIBUTING.md.ContactFeel free to contact us by sending an email tomedkit-maintainers@inria.fr.
Package: medlar
MedlarMaking sense out of weakly defined trees.License: MIT|Country of origin: Switzerland|Export control classification number(ECCN): EAR99Third party dependencies are documented in the folderthird-party.DocumentationUser and developerdocumentation of medlar.Bug TrackerAny feature requests or bug reports shall go to thetodos of medlar.Primary Source repositoryThe main source ofmedlaris on a mountain in central Switzerland.
We use distributed version control (git).
There is no central hub.
Every clone can become a new source for the benefit of all.
The preferred public clones ofmedlarare:on codeberg- a democratic community-driven, non-profit software development platform operated by Codeberg e.V.at sourcehut- a collection of tools useful for software development.ContributionsPlease do not submit "pull requests" (I found no way to disable that "feature" on GitHub).
If you like to share small changes under the repositories license please kindly do so by sending a patchset.
You can either send such a patchset per email usinggit send-emailor
if you are a sourcehut user by selecting "Prepare a patchset" on the summary page of your fork atsourcehut.StatusExperimental.Note: The default branch isdefault.
Package: medlemssys
Medlemssys is a membership register.  Primarily written for norwegian youth
organizations, but can and is used by other kinds of organizations.It doesn’t do anything very advanced, but provides a way to list the members,
export to CSV, can read OCR-files, send emails and some more.InstallationDoing a simple install, you can install “medlemssys” for a test version, or
“medlemssys[production]” to get a few required extras for a real setup:pip install medlemssys[production]You’ll use themedlemssyscommand for all commands.  It’s a small wrapper
around the django-admin command.  The first thing you’ll want to do is to
create a settings file:medlemssys initThis will give you amedlemssys_conf.pyfile.  Have a look at it, and change
what you need.  You should load in the data to the database and create yourself
a user:medlemssys migrate
medlemssys createsuperuserIf you chose the production setup, you can run gunicorn using a handy wrapper:medlemssys gunicorn --bind 0.0.0.0:8000You can of course also try the django development server to just test your
register:medlemssys runserver 0.0.0.0:8000
Package: medleydb-instruments
🎷 medleydb-instrumentsmedleydb-instrumentsis a tool used to seemlessly query MedleyDB annotations and instrument metadata.TheMedleyDB 1.0 and 2.0datasets don't provide annotations or metadata, only raw audio files. The authors providean official github repocontaining metadata, but it is painful to install and can be quite complex to use.InstallingInstall withpipor your favorite PyPI package manager.python -m pip install medleydb-instrumentsExampleimportmedleydb_instrumentsasmdbtrack=mdb.MultiTrack("AcDc_BackInBlack")# Get a list of instruments present in the trackinstrument_list=track.instruments# Determine if the recording has bleedhas_bleed=track.has_bleed# Get a dataframe of instrument activationsactivations_df=track.activations
Package: medlineplus
MedlinePlusMedlinePlus is a service of the National Library of Medicine (NLM), the world's largest medical library, which is part of the National Institutes of Health (NIH).MedlinePlus is the New Home ofGenetics Home ReferenceGenetics Home Referenceis now part ofMedlinePlus, an online health information resource from the National Library of Medicine (NLM).
Information from the Genetics Home Reference website is available in a new section calledMedlinePlus Genetics.DependenciesPython3.6+Installationpip3installmedlineplusUsagemedlineplus

medlineplus gene -l -s a
medlineplus gene -s a -o a.gene
medlineplus gene -s a -o a.gene -O xml

medlineplus condition -l -s b
medlineplus condition -s b -o b.condition
medlineplus condition -s b -o b.condition -O xmlExamplesGHR.gene.jlGHR.condition.jlz.gene.xmlz.condition.xml
Package: medmij-lists
medmij-pythonPython implementation of de MedMij ZAL, OCL, Whitelist and GNLInstallation$pipinstallmedmij-listsUsageWhitelistimporturllib.requestimportmedmij_listsWHITELIST_URL="https://afsprakenstelsel.medmij.nl/download/attachments/22348803/MedMij_Whitelist_example.xml"withurllib.request.urlopen(WHITELIST_URL)asu:whitelist_xml=u.read()whitelist=medmij_lists.Whitelist(xmldata=whitelist_xml)print('rcf-rso.nl'inwhitelist)print('example.com'inwhitelist)Runwhitelist.py:(env)$pythonwhitelist.py
True
FalseZALimporturllib.requestimportmedmij_listsZAL_URL="https://afsprakenstelsel.medmij.nl/download/attachments/22348803/MedMij_Zorgaanbiederslijst_example.xml"withurllib.request.urlopen(ZAL_URL)asu:zal_xml=u.read()zal=medmij_lists.ZAL(xmldata=zal_xml)za=zal["umcharderwijk@medmij"]print(za.gegevensdiensten["4"].authorization_endpoint_uri)Runzal.py:(env)$pythonzal.py
https://medmij.za982.xisbridge.net/oauth/authorizeOCLimporturllib.requestimportmedmij_listsOCL_URL="https://afsprakenstelsel.medmij.nl/download/attachments/22348803/MedMij_OAuthclientlist_example.xml"withurllib.request.urlopen(OCL_URL)asu:ocl_xml=u.read()ocl=medmij_lists.OAuthclientList(xmldata=ocl_xml)client=ocl["medmij.deenigeechtepgo.nl"]print(client.organisatienaam)Runocl.py:(env)$pythonocl.py
DeEnigeEchtePGOGNLimporturllib.requestimportmedmij_listsGNL_URL="https://afsprakenstelsel.medmij.nl/download/attachments/22348803/MedMij_Gegevensdienstnamenlijst_example.xml"withurllib.request.urlopen(GNL_URL)asu:gnl_xml=u.read()gnl=medmij_lists.GNL(xmldata=gnl_xml)gd=gnl["1"]print(gd.weergavenaam)Rungnl.py:(env)$pythongnl.py
BasisgegevensZorgVersion GuidanceThis library followsSemantic Versioning.
The versions of the Afsprakenset are mapped to the versions of the library as follows:Version AfsprakensetStatusVersion libraryAfsprakenset release 1.1Latest0.1.*
Package: medmij-oauth
Welcome to MedMij OAuth’s documentationThe medmij_oauth package assists in implementing an oauth server/client application conform the medmij oauth flow (described below). The module consists of 3 main submodules i.e.medmij_oauth.server,medmij_oauth.clientandmedmij_oauth.exceptions.
The client and server submodules are build for use with an async library likeaiohttp.Beside the package there are two example implementations available on thegithub repo, an oauth server and client implementation built using these modules (Only a reference, not for production use!).Read the full documentation onreadthedocs.Installation$pipinstallmedmij-oauthTests$pytest-vRequirementsModulesPython >=3.7Example implementationsaiohttp==3.3.2aiohttp-jinja2==1.0.0aiohttp-session==2.5.1cryptography==2.3SQLAlchemy==1.2.10Testspytest==3.7.1pytest-asyncio==0.9.0LicenseThis project is licensed under the AGPL-3.0 License - see the LICENSE file for detailsVersion GuidanceThis library followsSemantic Versioning.
The versions of the Afsprakenset are mapped to the versions of the library as follows:Version AfsprakensetStatusVersion libraryAfsprakenset 1.1Latest0.1.*