Saravanan666/pypi-org huggingface dataset:
Package: mediasorter
mediasorterA CLI tool to sort and copy images/videos to a defined directory structure. This will look at the metadata for each picture/video in the source directory and copy it to the target directory preserving the files metadata. The target directory is by default in the format %Y/%B/%Y_%m_%d.For example if there is an image under ~/phonepics/someimage.jpg with a create date of 2021-10-21. If I run the following:$ mediasorter --source ~/phonepics --target ~/imagesThe picture someimage.jpg will be saved under ~/images/2021/October/2021_10_21.Installationpip install mediasorterUsageUsage: mediasorter [OPTIONS]

The mediasorter Python project.

Options:
-s, --source PATH  Source directory of Images/Videos  [default: current directory]
-t, --target PATH  Target directory of Images/Videos  [required]
-f, --format TEXT  Directory format for how images/videos are saved to target  [default: %Y/%B/%Y_%m_%d]
-d, --debug
--version          Show the version and exit.
--help             Show this message and exit.
Package: mediasorter-server
Mediasorter (server)This is a straightforward web interface for themediasorterutility. Configured folders can be scanned, and detected media files can be selected individually for sorting, eliminating unwanted sorting mistakes compared to a CRON-based setup.Installpipinstallmediasorter-serverUsage# CLI toolmediasorter--help# use --setup to start with a default configuration filemediasorter--setup# start the all-in-one server (valid configuration is needed)mediasorter-server
Package: mediastackwrapper
This python library helps you use your mediastack API key to access data on the media stack API. No need to figure out how to paginate through the results this code does it for you.pip install mediastackwrapperimport mediastackwrapperTo use this library import mediastackwrapper and call the function news_dataframe(), put your between dates, the keyword you are searching the news for, your mediastack API key.import mediastackwrapper
import pandas as pddf = mediastackwrapper.news_dataframe(‘2022-1-1,2022-5-31’,’Keyword’,’your_api_key’)It will return the results as a pandas dataframe. You will be able to run analysis using the pandas library.Get Mediastack API keyhttps://mediastack.com?utm_source=FirstPromoter&utm_medium=Affiliate&fpr=christopher32Change Log0.0.1 (19/06/2022)First Release0.0.2 (19/06/2022)Second Release0.0.3 (19/06/2022)Third Release
Package: mediate
mediateMiddleware for every occasionInstallationmediatecan be installed fromPyPIpip install mediateUsage@middlewarefrommediateimportmiddlewaredefshout(call_next,name):returncall_next(name.upper())defexclaim(call_next,name):returncall_next(name+"!")@middleware(shout,exclaim)defhello(name):print(f"Hello,{name}")>>>hello("sam")Hello,SAM!MiddlewareMiddleware.bindimportmediatemiddleware=mediate.Middleware()@middlewaredefshout(call_next,name):returncall_next(name.upper())@middlewaredefexclaim(call_next,name):returncall_next(name+"!")@middleware.binddefhello(name):print(f"Hello,{name}")>>>hello("sam")Hello,SAM!Middleware.composeimportmediatemiddleware=mediate.Middleware()@middlewaredefshout(call_next,name):returncall_next(name.upper())@middlewaredefexclaim(call_next,name):returncall_next(name+"!")defhello(name):print(f"Hello,{name}")composed_hello=middleware.compose(hello)>>>hello("sam")Hello,sam>>>composed_hello("sam")Hello,SAM!
Package: mediathekview-change-downloads-to-http
Usage:mediathekview_change_downloads_to_http.pyThis little Python script tries to locate the MediathekView XML
file containing pending downloads, change their protocol from
https to http and re-writes the XML file accordingly.Readhttps://github.com/novoid/mediathekview_change_downloads_to_httpfor further information.copyright:by Karl Voitlicense:GPL v3 or any later versionURL:https://github.com/novoid/mediathekview_change_downloads_to_httpbugreports:via github or <tools@Karl-Voit.at>Options:-h,--helpshow this help message and exit-d,--dryrunenable dryrun mode: just simulate what would happen, do not
modify files-v,--verboseenable verbose mode-q,--quietenable quiet mode--versiondisplay version and exit
Package: mediathequeroubaix
Python CLI for the library of Roubaix (Médiathèque Roubaix)Releases:https://github.com/tomsquest/mediathequeroubaix.py/releasesSource Code:https://github.com/tomsquest/mediathequeroubaix.pyPyPI:https://pypi.org/project/mediathequeroubaix/Table of ContentsFeaturesDisplay your loansRenew your loansUsageInstallCreate an initial, sample configurationDisplay the current configurationList the loansRenew the loansWhy I am doing thisChangelogInstallationDevelopmentReleasingCreditsFeaturesMédiathèqueRoubaix.py is a client for thelibray of Roubaix,mediathequederoubaix.fr.Display your loansRunningmediathequeroubaix loans listwill:Get thelist of your loansand their due date...formany cardholdersand check thenext return datefor each of your cardRenew your loansRunningmediathequeroubaix loans renewwill:Renewautomaticallyall loans...formany cardholdersand print thenew due dateof the loansUsageInstallpipinstallmediathequederoubaixCreate an initial, sample configurationconfig createmakes a sample configuration in$HOME/.config/mediathequederoubaix/config.jsonand display the content of the file.The configuration is initialized with a sample but fake user.mediathequeroubaixconfigcreateDisplay the current configurationconfig showdisplays the current configuration.mediathequeroubaixconfigshowList the loansloans listshow the list of loans for the users.mediathequeroubaixloanslistRenew the loansloans renewrenew the list of loans for the users and display the new loans.mediathequeroubaixloansrenewWhy I am doing thisI created this project to:LearnFunctional ProgramingLearntypedandmodernPythonBe able to quickly list and renew my loans (especially when you have many cards)ChangelogSeeCHANGELOG.mdInstallationpipinstallmediathequeroubaixDevelopmentClone this repositoryRequirements:PoetryPython 3.10Create a virtual environment and install the dependenciespoetryinstallActivate the virtual environmentpoetryshellInstall Pre-commitpre-commitinstallTestpytestCheck everything in one gopre-commitrun--all-filesReleasingTrigger theDraft release workflow(pressRun workflow). This will update the changelog & version and create a GitHub release which is inDraftstate.Find the draft release from theGitHub releasesand publish it. When
a release is published, it'll triggerreleaseworkflow which creates PyPI
release.CreditsBackground and color fromPrettySnapPython project bootstrapped usingWolt templateFunctional library isReturns from DRY-PythonTables look great thanks toTextualize's RichCLI screenshot pimped withShowCode.app
Package: mediatpy
IntroductionThis library is a port ofMediatrin Python.For more information and usage instructions, see thedocumentation.Usagepip install mediatpyimportasynciofrommediatpyimportRequest,RequestHandler,MediatorclassMyResponse:passclassMyRequest(Request[MyResponse]):passmediator=Mediator()@mediator.request_handlerclassMyRequestHandler(RequestHandler[MyRequest,MyResponse]):asyncdefhandle(self,request:MyRequest)->MyResponse:returnMyResponse()asyncdefmain():request=MyRequest()response=awaitmediator.send(request)assertisinstance(response,MyResponse)if__name__=='__main__':asyncio.run(main())
Package: media-transporter
Manages the transportation of TV/Movie files to a mountable media share.Contributers:Steve Coward (stevesugarstack.io)media-transporter is a project that arose out of laziness and boredom,
mainly. There are plenty of similar or fully-featured alternatives like
SickBeard and others, but I already had an existing workflow for:Polling RSS feeds and downloading torrent files
(Otomatic).Automatic watching of a folder for torrents and auto-adding to a
torrent client (Transmission).The last piece involved the need to move completed downloads to my NAS.
Entermedia-transporter. Here’s how it works:Createconfig.pyin a directory of your choosing. Useconfig_example.pyas a template. The main options aremedia_shares,download_path, andunrar_path. Adjust these
to match the path to your media share, where you have finished
downloads, and theabsolutepath to theunrarbinary. Theshare_[tv|movie]_*config options point to the path on your media
share where TV and Movies are stored.media-transporterrequires one command line argument.-cshould be theabsolutepath to yourconfig.pyfile.When executed (preferably automatically after a torrent is completed
(Transmission is built to support this, for example)),media-transporterdetermines if your configured media share is
within an acceptable range for storage capacity (configured inconfig.py).If it is,media-transporterchecks your Downloads folder for
media files and folders, and intelligently moves each of them to
their appropriate destination on your media share.Logs are written to the value oflog fileto record all actions
taken during processing. Useful for debugging.The idea is thatmedia-transporterwill be able to be used on
multiple platforms, including Windows. At the moment,media-transporterhas only been tested on OS X and Linux, but it
should be feasible to support Windows very easily.RequirementsPython 2.*unrarvia either apt-get/yum (Linux), homebrew (OS X), WinRAR
(Windows)Installation$pipinstalltransporter$pythonsetup.pyinstallThings to doCode cleanup. Plenty of stuff to do. This wasn’t originally meant for
public distribution.Support and test script on Windows.
Package: mediatumfsm
Declarative finite state machine library inspired by [akka FSM](http://doc.akka.io/docs/akka/snapshot/scala/fsm.html)
and [erlang gen_fsm](http://www.erlang.org/doc/design_principles/fsm.html).
This library works with Python 2.7 and Python 3.2+InstallationWithpipfrom source:pip install git+git://mediatumdev.ub.tum.de/mediatum-fsmThis installs themediatumfsmpackage and its dependencypydot2, if not already installed.Running TestsFrom the base directory, for example:py.test
Package: mediatype
mediatypeMedia Type (aka MIME Type) parsing and creationInstallationpip install mediatypeUsage>>>importmediatypeParsing>>>media_type=mediatype.parse('application/manifest+json')>>>>>>media_typeMediaType(type='application',subtype='manifest',suffix='json',parameters=None)>>>>>>str(media_type)'application/manifest+json'Creation>>>media_type=mediatype.MediaType(type='application',subtype='manifest',suffix='json',parameters=None)>>>>>>str(media_type)'application/manifest+json'ReferencesIANA - Media TypesRFC-6838 - Media Type Specifications and Registration ProceduresMozilla - MIME types (IANA media types)Wikipedia - Media type
Package: mediatypes
mediatypesmediatypes isa very simple but fully featured media type (also known as MIME type) mapper for Python.written in Python Standard Librarymediatypes supports tomap filenames to media typesInstallationpipinstallmediatypesQuickstartimportmediatypesLicensemediatypes has a BSD-3-Clause license, as found in theLICENSEfile.ContributingChangelog
Package: mediawatcher
# MediaWatcherWatch directories and move media files accordingly.```λ mediawatcher -husage: mediawatcher [-h] -c CONFIGWatch directories and move media files accordingly.optional arguments:-h, --help  show this help message and exit-c CONFIG   path to the config file in YAML```### Requirements* Python 3.5* Some folders to watch ;)### InstallationYou can install MediaWatcher using pip:```pip install mediawatcher```### RunningFirst, you'll need a config file, here's an example created in `~/.mediawatcher`:```watch:- /data/downloadsoutput_anime: /data/media/animeoutput_tv: /data/media/tvoutput_movies: /data/media/movies```And then, run mediawatcher:```mediawatcher -c ~/.mediawatcher[2016-05-07 16:27:15.165334] INFO: MediaWatcher: MediaWatcher ready. Performing initial scan.[2016-05-07 16:27:15.165466] INFO: MediaWatcher: Scanning /data/downloads...[2016-05-07 16:27:15.874268] INFO: MediaWatcher: Done scanning.[2016-05-07 16:27:15.874309] INFO: MediaWatcher: Initial scan done, watching 1 folder.```### LicenseMIT LicenseCopyright (c) [2016] [@adrien-f]Permission is hereby granted, free of charge, to any person obtaining a copyof this software and associated documentation files (the "Software"), to dealin the Software without restriction, including without limitation the rightsto use, copy, modify, merge, publish, distribute, sublicense, and/or sellcopies of the Software, and to permit persons to whom the Software isfurnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in allcopies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS ORIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THEAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHERLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THESOFTWARE.
Package: mediawikiapi
MediaWikiAPIMediaWikiAPIis a Python library that makes it easy to access and parse
data from Wikipedia.Search Wikipedia, get article summaries, get data like links and images
from a page, and more. Wikipedia wraps theMediaWiki APIso you can focus on using
Wikipedia data, not getting it.>>>frommediawikiapiimportMediaWikiAPI>>>mediawikiapi=MediaWikiAPI()>>>print(mediawikiapi.summary("Wikipedia"))# Wikipedia (/ˌwɪkɨˈpiːdiə/ or /ˌwɪkiˈpiːdiə/ WIK-i-PEE-dee-ə) is a collaboratively edited, multilingual, free Internet encyclopedia supported by the non-profit Wikimedia Foundation...>>>mediawikiapi.search("Barack")# [u'Barak (given name)', u'Barack Obama', u'Barack (brandy)', u'Presidency of Barack Obama', u'Family of Barack Obama', u'First inauguration of Barack Obama', u'Barack Obama presidential campaign, 2008', u'Barack Obama, Sr.', u'Barack Obama citizenship conspiracy theories', u'Presidential transition of Barack Obama']>>>ny=mediawikiapi.page("New York (state)")>>>ny.title# u'New York (state)'>>>ny.url# u'http://en.wikipedia.org/wiki/New_York_(state)'>>>ny.content# u'New York is a state in the northeastern United States. New York was one of the original thir'...>>>ny.links[0]# u'1790 United States Census'>>>mediawikiapi.config.language="fr">>>mediawikiapi.summary("Facebook",sentences=1)# Facebook est un service de réseautage social en ligne sur Internet permettant d'y publier des informations (photographies, liens, textes, etc.) en contrôlant leur visibilité par différentes catégories de personnes.InstallationTo install MediaWikiAPI, simply run:pipinstallmediawikiapiMediaWikiAPI is compatible with Python 3 and Python 2.7.ChangelogChangelogcould be find in the documentation.DocumentationThe documentation is availablehereTo run tests, clone therepository on GitHub, then run:poetryinstallpoetrybuild
poetryrunpytest--junitxml=pytest.xml--cov-report=term-missing:skip-covered--cov=mediawikiapiin the root project directory.To build the documentation yourself, after installing requirements.txt, run:pipinstallsphinxcddocs/
makehtmlTo run formatter and mypy run:poetry run mypy --strict .
poetry run flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
poetry run black --diff --check .LicenseMIT licensed. See theLICENSE filefor
full details.Credits@goldsmith for making such a fantastic library to fork
Package: mediawiki-api-wrapper
MediaWiki API rapperIt is an unofficial Python wrapper over available API.It is a Python wrapper for a tiny part of mediawiki API.It was written to interact with OSM Wiki and some things may be assuming it too much - but PRs changing this are welcome.Examplesimport mediawiki_api_wrapper

???TODO (please open an issue if you planned to use that repository)DevelopmentContributions are welcome to cover larger part of API.Run testspython3 -m unittest
Package: mediawiki-langcodes
MediaWiki langcodesConvert MediaWiki language names and language codes.Build dependenciesphpwgetBuild$ python -m venv .venv
$ source .venv/bin/activate.fish
$ python -m pip install -U pip build
$ makeUpdate extra language namesExtract language names from Lua modules in the Wiktionary dump file,
use code fromhttps://github.com/tatuylonen/wiktextract/blob/master/languages/get_data.py$ python languages/get_data.py en --db-file ../en_20231220.db
$ python languages/get_data.py zh --db-file ../zh_20231220.dbthen in this project folder:// download files
$ make lang_files
// remove old extra files
$ rm build_script/extra_names/*.json
// create a db file without extra languages
$ python build_script/main.py
$ python build_script/add_extra.py en path_to_en_extra_json
// add extra languages from the English Wiktionary
$ python build_script/main.py
$ python build_script/add_extra.py zh path_to_zh_extra_jsonUsagefrommediawiki_langcodesimportcode_to_name,name_to_codecode_to_name("fr")# return "français"code_to_name("fr","en")# return "French"name_to_code("français")# return "fr"name_to_code("français","fr")# return "fr"
Package: mediawiki-utilities
MediaWiki Utilities is an open source (MIT Licensed) library developed by Aaron Halfaker for extracting and processing data from MediaWiki installations, slave databases and xml dumps.Install with pip:pip installmediawiki-utilitiesNote:Use of this library requires Python 3 or later.Documentation:http://pythonhosted.org/mediawiki-utilities/About the authorname:Aaron Halfakeremail:aaron.halfaker@gmail.comwebsite:http://halfaker.info–http://en.wikipedia.org/wiki/User:EpochFailContributorsNone yet.  Seehttp://github.com/halfak/mediawiki-utilities.  Pull requests are encouraged.
Package: medicalai
Medical-AI is a AI framework for rapid protyping for Medical ApplicationsDocumentation:https://aibharata.github.io/medicalAI/Source Code:https://github.com/aibharata/medicalaiYoutube Tutorial:https://www.youtube.com/V4nCX-kLACgMedical-AI is a AI framework  for rapid prototyping of AI for Medical Applications.Installationpipinstallmedicalai## Requirements
Python Version : 3.5-3.7 (Doesn't Work on 3.8 Since Tensorflow does not support 3.8 yet.Dependencies: Numpy, Tensorflow, Seaborn, Matplotlib, PandasNOTE: Dependency libraries are automatically installed. No need for user to install them manually.UsageGetting Started Tutorial: Google ColabGoogle Colab Notebook LinkImporting the LibraryimportmedicalaiasaiUsing TemplatesYou can use the following templates to perform specific TasksLoad Dataset From FolderSet the path of the dataset and set the target dimension of image that will be input to AI network.trainSet,testSet,labelNames=ai.datasetFromFolder(datasetFolderPath,targetDim=(96,96)).load_dataset()- trainSet contains 'data' and 'labels' accessible by trainSet.data and trainSet.labels
- testSet contains 'data' and 'labels' accessible by testSet.data and testSet.labels
- labelNames contains class names/labelsCheck Loaded Dataset Sizeprint(trainSet.data.shape)print(trainSet.labels.shape)Run Training and Save Modeltrainer=ai.TRAIN_ENGINE()trainer.train_and_save_model(AI_NAME='tinyMedNet',MODEL_SAVE_NAME='PATH_WHERE_MODEL_IS_SAVED_TO',trainSet,testSet,OUTPUT_CLASSES,RETRAIN_MODEL=True,BATCH_SIZE=32,EPOCHS=10,LEARNING_RATE=0.001)Plot Training Loss and Accuracytrainer.plot_train_acc_loss()Generate a comprehensive evaluation PDF reporttrainer.generate_evaluation_report()PDF report will be generated with model sensitivity, specificity, accuracy, confidence intervals,
ROC Curve Plot, Precision Recall Curve Plot, and Confusion Matrix Plot for each class.
This function can be used when evaluating a model with Test or Validation Data Set.Explain the Model on a sampletrainer.explain(testSet.data[0:1],layer_to_explain='CNN3')Loading Model for PredictioninfEngine=ai.INFERENCE_ENGINE(modelName='PATH_WHERE_MODEL_IS_SAVED_TO')Predict With LabelsinfEngine.predict_with_labels(testSet.data[0:2],top_preds=3)Get Just Values of Prediction without postprocessinginfEngine.predict(testSet.data[0:2])Alternatively, use a faster prediction method in productioninfEngine.predict_pipeline(testSet.data[0:1])Advanced UsageCode snippet for Training Using Medical-AI## Setup AI Model Manager with required AI.model=ai.modelManager(AI_NAME=AI_NAME,modelName=MODEL_SAVE_NAME,x_train=train_data,OUTPUT_CLASSES=OUTPUT_CLASSES,RETRAIN_MODEL=RETRAIN_MODEL)# Start Trainingresult=ai.train(model,train_data,train_labels,BATCH_SIZE,EPOCHS,LEARNING_RATE,validation_data=(test_data,test_labels),callbacks=['tensorboard'])# Evaluate Trained Model on Test Datamodel.evaluate(test_data,test_labels)# Plot Accuracy vs Loss for Trainingai.plot_training_metrics(result)#Save the Trained Modelai.save_model_and_weights(model,outputName=MODEL_SAVE_NAME)Automated TestsTo Check the testspytestTo See Output of Print Statementspytest -s
Package: medical-data
Medical DataThis repository contains utilities for handling data in medical image analysis that are not specific to certain tasks.So far it consists of 2 major parts:
An abstract Dataset API built on top oftorch.utils.data.Datasetandtorchio.data.Subjectas well as general transforms for this kind of data.To use the dataset classes, you basically only need to implement theparse_subjectsmethod to return a list of samples and everything else will work automatically.
You will automatically get image statistics such as median spacing or median shape. For label statistics, you either need to subclass theAbstractDiscreteLabelDatasetor implement theget_single_label_statsandaggregate_label_statsmethods.All transforms work ontorchio.data.Subjectsand can be passed to the datasets as optional parameters. You can also pass"default"as a parameter to use the default transforms.Pull requests for other common utilities are highly welcomed.InstallationThis project can be installed either from PyPI or by cloning the repository from GitHub.For an install of published packages, use the commandpipinstallmedical-dataTo install from the (cloned) repository, use the commandpipinstallPATH/TO/medical-dataYou can also add-eto the command to make an editable install in case you want to modify the code.You can also install the package directly from GitHub by runningpipinstallgit+https://github.com/justusschock/medical-data.git
Package: medical-dl-utils
Medical Deep Learning UtilitiesThis repository contains utilities for training and evaluating deep learning models in medical image analysis that are not specific to certain tasks.So far it consists of 2 major parts:
An abstract Dataset API built on top oftorch.utils.data.Datasetandtorchio.data.Subjectas well as general transforms for this kind of data.To use the dataset classes, you basically only need to implement theparse_subjectsmethod to return a list of samples and everything else will work automatically.
You will automatically get image statistics such as median spacing or median shape. For label statistics, you either need to subclass theAbstractDiscreteLabelDatasetor implement theget_single_label_statsandaggregate_label_statsmethods.All transforms work ontorchio.data.Subjectsand can be passed to the datasets as optional parameters. You can also pass"default"as a parameter to use the default transforms.Pull requests for other common utilities are highly welcomed.InstallationThis project can be installed either from PyPI or by cloning the repository from GitHub.For an install of published packages, use the commandpipinstallmedical-dl-utilsTo install from the (cloned) repository, use the commandpipinstallPATH/TO/medical-dl-utilsYou can also add-eto the command to make an editable install in case you want to modify the code.You can also install the package directly from GitHub by runningpipinstallgit+https://github.com/justusschock/medical-dl-utils.gitDocker ImagesWe provide a docker image for easy usage of the package and as a base image for other projects.The file for this image can be found atdockers/Dockerfile. We provide both, a CPU-only and a CUDA-enabled image based on the NVIDIA NGC PyTorch image.
These images can be found onDockerHub.