Saravanan666/pypi-org huggingface dataset:
Package: yarsaw
YARSAWYARSAW (Yet Another Random Stuff API Wrapper) is an Async, Object Oriented and Modern Python API Wrapper for the Random Stuff API. This module makes it simpler for you to interact with the API and is easy to implement into your application.Make sure to get Random Stuff API Key fromhereand a RapidAPI Application Key after registering and subscribing to the APIhere.FeaturesTons of things you can doGet AI Responses (useful for chatbots!)Get Anime GIFs and Waifu ImagesGet Animal imagesGet memes from various subreddits, and even create memes with pre-built templates!Get jokes and fun factsAnd so much more!Object-oriented design. YARSAW takes inspiration fromrandomstuff.pyand sends you the data from the API in form of objects instead of plain JSON.Async-ready. YARSAW provides an async client for async enviorments using aiohttp.Easy to use. YARSAW is well-documented, and has utility features to help you with your requests.Saves your time. i) No need to learn about the API itself and its complicated design. ii) No need to enter the credentials again and again. Enter it once, and get coding. YARSAW will take care of the boring stuff for you. iii) No need to handle the unhelpful errors given by requests/aiohttp/RapidAPI. Get human-headable, descriptive and helpful error messages. iv) Common errors and issues are prevented and declared before runtime.Examplesimportyarsawimportasyncioclient=yarsaw.Client("RSA Key","RapidAPI Key")asyncdefjoke():joke=awaitclient.get_joke()# get the jokeformatted_joke=yarsaw.format_joke(joke)# format the joke (optional)print(formatted_joke)# print the jokeasyncio.get_event_loop().run_until_complete(joke())# run the joke() functionInstallationpython3-mpipinstallyarsawLinksDocumentationRandom Stuff APIRandom Stuff API Discord
Package: yartsu
YARTSUyartsu, another rich terminal screenshot utilityNOTE: this is a currently in a beta release and the API is subject to change (feedback welcome)Inspired by recent commits in the wonderful libraryrichI decided to writeyartsu.I needed to programmatically generate screenshots for documentation purposes. The newexport_svgmethods inrichwere a godsend.
So I wanted to try to make this feature a little more generalizable to program output outside ofrich/python.
Hopefully, you find it useful.If you come across anything unexpected please submit an issue.InstallpipinstallyartsuThere is a standalone binary available for linux in thereleases.Optionally install witheget:egetdaylinmorgan/yartsuOtherwise you can download an extract manually to somewhere on your path.UsageGetting a properly formatted output from a terminal screenshot is challenging.There are three options for generating a screenshot.If one of the below option causes you any headaches consider first trying a different option.Option 1: PipesMany tools that color output (i.e.greporls) additionally allow
you to force ANSI color codes to be included even when piping output.In these cases you can simply pipe the output directly intoyartsuls--color=always|yartsu-w50-oassets/ls_color.svgOption 2: Subprocess/PtyWith this optionyartsuwill deploy asubprocessandptyto run your command for you in an attempt to preserve ANSI.Note with this option you need to separate
the command you want to run withyartsuargs using--.yartsu-w50-oassets/ls_color.svg--ls--color/Option 3: Text FileFinally, if you neither of the above options work and you can
manage to preserve the codes in a plain text file you can pass this as input toyartsu.ls--color>ls.txt
yartsu-w50-ils.txt-oassets/ls_color.svgBy default svgs will be saved at./capture.svg.Additionally, for options 1 and 3 you may want to define your own title with-t/--title.
For option 2 the title will by default be the cmd ran byyartsu.Themes:There are a number of themes you can use to style output.
Useyartsu --list-themesto see the available options.
Then you can specify the theme you want with--theme, i.e.yartsu --theme rich_default.
You may also use the environment variableYARTSU_THEME.Seeherea preview of the available themesSupported CharactersCurrentlyyartsuloads thenerd fontpatched FiraCode font.
This should result in general support for emoji's andnerd fonticons.Note: github won't load the font's when displaying on the README. Click on thesvgto see thenerd fonticons.Differences fromRichFor both practical and stylistic reasons the underlying code used to generate the SVG is slightly different thanrich's defaultsave_svgmethod. Seeherefor the current deviation between the latest releases of each respective release.TODO (for stable release)add support for nerd-fontsaddoptionalshadowadd unit testssetup CI for release/testing
Package: yarub
YarubThis is a head start to Modern standard arabic training dataset library which provides Morphological, Named Entity recognization, Sentiment Analysis, Word Embedding, Dialect Identification,Part of speech and so on training dataset.Downloading the library with specific version using pip command.pip installyarub==0.1.0Basic InstructionYou need to first install dependent libraries before download likerequests,zipfile,os,ioandstruct.After successful installation of dependent library you can installYarublibrary with pip command.Example ofYarubimport yarub  # Load Library package

from yarub import load_sentiment,load_ner,load_dialect,load_word_embedding,load_morphology,load_posOR==from yarub import *    # Second method to call library and load entire function.Load the dataset in your local system.df = load_sentiment()   # This function wil load `Sentiment analysis` training dataset



df1 = load_ner()        # This function will load `Named entity recognization` training dataset.



df2 = load_dialect()     # This function will load modern standard arabic `Dialect identification` training dataset.



df3 = load_word_embedding()   # This function will load `Word embedding` training dataset.



df4 = load_morphology()       # This function will load  `Morphological analysis` training dataset.



df5 = load_pos()             # This function will load `Part of speech` training dataset.
Package: yas3
YAS3 - Yet Another S3 ClientYas3 is a simple client for managing buckets/storage in S3 (and S3 compatible endpoints) for people who don't want the complexity of learning boto3.Uploading ObjectsYou can upload from Object paths:withyas3.Connection(access_key=ACCESS_KEY,secret_key=SECRET_KEY,endpoint=ENDPOINT)asconn:conn.upload('test.txt','Object.txt',bucket='bucket')Or as bytes:withyas3.Connection(access_key=ACCESS_KEY,secret_key=SECRET_KEY,endpoint=ENDPOINT)asconn:conn.upload(Object_bytes,'Object.txt',bucket='bucket',type='application/octet-stream')The Object type is guessed using mimetypes, but it can be easily specified usingmimetype='text/json'.Downloading ObjectsYou can download the Object locally:withyas3.Connection(access_key=ACCESS_KEY,secret_key=SECRET_KEY,endpoint=ENDPOINT)asconn:conn.download('Object.txt','test.txt',bucket='bucket')Or you can get the response directly:withyas3.Connection(access_key=ACCESS_KEY,secret_key=SECRET_KEY,endpoint=ENDPOINT)asconn:bucket_data=conn.get('Object.txt',bucket='bucket')print(bucket_data)Object OperationsMoving Objects:withyas3.Connection(access_key=ACCESS_KEY,secret_key=SECRET_KEY,endpoint=ENDPOINT)asconn:conn.move('Object.txt','new_Object.txt',source_bucket='bucket',target_bucket='bucket')Copying Objects:withyas3.Connection(access_key=ACCESS_KEY,secret_key=SECRET_KEY,endpoint=ENDPOINT)asconn:conn.copy('Object.txt','new_Object.txt',source_bucket='bucket',target_bucket='bucket')Deleting Objects:withyas3.Connection(access_key=ACCESS_KEY,secret_key=SECRET_KEY,endpoint=ENDPOINT)asconn:conn.delete('Object.txt',bucket='bucket')Listing Objects in a bucketwithyas3.Connection(access_key=ACCESS_KEY,secret_key=SECRET_KEY,endpoint=ENDPOINT)asconn:conn.list_Objects(bucket='bucket',prefix=None)Using it as an objectIf you want to create an object, feel free:conn=yas3.Connection(...)...conn.close()Full DocsSee the full API documentation here.
Package: yasal
DescriptionConvert Windows Shortcut File*.urlto UNIX Desktop file*.desktopyasal is mostly inspirated by the firefox pluginSaveLink.desktopfile are named with the given url page title, the output
file will be placed in the same folder if not specifiedUsageusage: yasal [-h] [-f FILE | -r RECURSIVE] [-o OUT] [-i]

optional arguments:
  -h, --help            show this help message and exit
  -f FILE, --file FILE  specify file FILE to be convert
  -r RECURSIVE, --recursive RECURSIVE
                        recursive mode, will convert all .url file in
                        directory RECURSIVE
  -o OUT, --out OUT     specify output directory OUT for .desktop files
  -i, --icontexthtml    set the .desktop icon field to 'text-html' default is
                        'firefox'Examplesconvert a single file, result is stored in the input file same
directory:yasal -f /path/to/file.urlconvert all files in a given folder and store result in another given
folder:yasal -r /path/to/folder -o /path/to/result/folder
Package: yaschedule
yascheduleLib for getting schedule data from Yandex.Rasp API:https://yandex.ru/dev/rasp/docQuick StartInitfromyaschedule.coreimportYaSchedule# TO GET TOKEN - https://yandex.ru/dev/rasp/doc/concepts/access.htmlTOKEN='some string'yaschedule=YaSchedule(TOKEN)[optional] requests cachingSee more atrequests_cachedocsprint(yaschedule.session.settings.expire_after)# get particular cache settingyaschedule.session.settings.expire_after=600# set particular cache settingprint(yaschedule.session.settings)# get all cache settings as object of <class 'requests_cache.policy.settings.CacheSettings'>print({a:getattr(yaschedule.session.settings,a)foraindir(yaschedule.session.settings)ifnota.startswith('_')})# get all cache settings as dictUsage# get all stations in jsonyaschedule.get_all_stations()# !!! The size of the returned data is about 40 MB# cities and stations codes from yaschedule.get_all_stations()city_1='c213'# Moscowcity_2='c2'# Saint-Petersburgstation_1='s9600366'# Pulkovostation_2='s9600213'# Sheremetevo# get station scheduleyaschedule.get_station_schedule(station=station_1)# get schedule between two stationsyaschedule.get_schedule(from_station=station_1,to_station=station_2)# get schedule between two citiesyaschedule.get_schedule(from_station=city_1,to_station=city_2)# get schedule between city and stationyaschedule.get_schedule(from_station=city_1,to_station=station_1)# u can also specify other request params of request# transport_type by default get all transport typesyaschedule.get_schedule(from_station=city_1,to_station=city_2,transport_types='train')yaschedule.get_schedule(from_station=city_1,to_station=city_2,transport_types='plane')# transfers by default: Falseyaschedule.get_schedule(from_station=city_1,to_station=city_2,transfers=True)
Package: yascikit-learn
yascikit-learnYet another scikit-learnInstallationpip install yascikit-learnUSAGENaive BayesNegation Naive Bayesfromyasklearn.naive_bayesimportNegationNBfromsklearnimportdatasetsdataset=datasets.load_iris()X=dataset.datay=dataset.targetnnb=NegationNB().fit(X,y)nnb.predict(X)Selective Naive Bayesfromyasklearn.naive_bayesimportSelectiveNBfromsklearnimportdatasetsdataset=datasets.load_iris()X=dataset.datay=dataset.targetsnb=SelectiveNB().fit(X,y)snb.predict(X)Universal Set Naive Bayesfromyasklearn.naive_bayesimportUniversalSetNBfromsklearnimportdatasetsdataset=datasets.load_iris()X=dataset.datay=dataset.targetunb=UniversalSetNB().fit(X,y)unb.predict(X)FTRLProximalfromyasklearn.ftrl_proximalimportFTRLProximalClassifierfromsklearnimportdatasetsdataset=datasets.load_iris()X=dataset.datay=dataset.targetftrlc=FTRLProximalClassifier().fit(X,y)ftrlc.predict(X)Topic modelingPLSAfromyasklearn.decompositionimportPLSAfromsklearnimportdatasetsdataset=datasets.load_iris()X=dataset.dataplsa=PLSA(n_components=3,random_state=1).fit(X)plsa.predict(X)PLSVNote that PLSV has not implemented predict method.fromyasklearn.decompositionimportPLSVfromsklearn.datasetsimportfetch_20newsgroupsnewsgroups=fetch_20newsgroups(subset='train')X=list(map(lambdax:x.split(),newsgroups.data))plsv=PLSV(n_components=20,n_dimension=2,random_state=1)plsv.fit_transform(X)ClusteringXMeansfromyasklearn.clusterimportXMeansfromsklearnimportdatasetsdataset=datasets.load_iris()X=dataset.dataxm=XMeans(n_clusters=3,random_state=1)xm.fit_predict(X)KMedoidsfromyasklearn.clusterimportKMedoidsfromsklearnimportdatasetsdataset=datasets.load_iris()X=dataset.datakm=KMedoids(n_clusters=3,random_state=1)km.fit_predict(X)XMedoidsfromyasklearn.clusterimportXMedoidsfromsklearnimportdatasetsdataset=datasets.load_iris()X=dataset.dataxm=XMedoids(n_clusters=3,random_state=1)xm.fit_predict(X)Utilityfromyasklearn.model_selectionimporttrain_dev_test_splitimportnumpyasnpX=np.arange(10).reshape((5,2))y=range(5)X_train,X_dev,X_test,y_train,y_dev,y_test=train_dev_test_split(X,y,dev_size=0.33,random_state=1)
Package: yasfb
This Sphinx extension is derived Fergus Doyle’ssphinxfeedwhich is in turn derived from Dan
Mackinlay’ssphinxcontrib.feedpackage.UsageInstallyasfbusingeasy_install/pip/python setup.py installAddyasfbto the list of extensions in yourconf.py:extensions = [..., 'yasfb']Customise the necessary configuration options to correctly generate the
feed:feed_base_url = 'http://YOUR_HOST_URL'
feed_author = 'YOUR NAME'Publishing DatesPublish dates for feed entries come from either reST metadata embedded
in the file, or the last git commit date for a file.
Package: yasfpy
Yet Another Scattering FrameworkYASF is a T-Matrix implementation in Python based on the Matlab frameworkCELESdeveloped byEgel et al..InstallpipinstallyasfpySadlyyasfwas already taken, so the package is calledyasfpyfor the Python version and can be found onpypi.ExamplesSmalldashboarddisplaying various parameters calculated using YASFDevelopmentThis repository is still under development!DocumentationThe code is documented usingMkDocs. If you discover mistakes, feel free to create a pull request or open up an issue.TODOThepywigxjpfpackage is not following PEP 517 and PEP 518 standards, so it may happen, that it won't install properly as a dependency of YASF. Please install it manually if that happens usingpip install pywigxjpf(before that, runpip install pycparseras stated in theirpypi page).
One could convert thesetup.pyfile to apyproject.tomlfile. Providingpycparseras a dependency could also solve the known issue of having to preinstall it.
Package: yasg-util
yasg-utilInjecting parameters through annotations based on drf-yasgRequirementsPython 3.6+drf-yasgInstallation$pipinstallyasg-utilStartsettings.pyINSTALLED_APPS=[...'django.contrib.staticfiles',# required for serving swagger ui's css/js files'drf_yasg',...]urls.pyfromdjango.urlsimportpath,re_pathfromdrf_yasgimportopenapifromrest_frameworkimportpermissionsfromdrf_yasg.viewsimportget_schema_viewfromrest_framework.fieldsimportIntegerField,CharField,DictFieldfromrest_framework.serializersimportSerializerfromrest_framework.viewsimportAPIViewfromyasg_utilimportrestapi,Locationschema_view=get_schema_view(openapi.Info(title="Snippets API",default_version='v1',description="Test description",terms_of_service="https://www.google.com/policies/terms/",contact=openapi.Contact(email="contact@snippets.local"),license=openapi.License(name="BSD License"),),public=True,permission_classes=(permissions.AllowAny,),)classDemoSerializer(Serializer):name=CharField(required=True,min_length=2,max_length=20,help_text='name')age=IntegerField(required=True,min_value=0,max_value=150,help_text='ahe')classMeta:location=Location.BODYclassResponseSerializer(Serializer):code=IntegerField(default=200,help_text='status code')msg=CharField(default='success',help_text='response message')data=DictField(help_text='response data')classDemoAPIView(APIView):@restapi(response_model=ResponseSerializer)defpost(self,request,pk:int,serializer:DemoSerializer):return{'code':0,'msg':'success','data':{"pk":pk,"info":serializer.data}}urlpatterns=[re_path(r'^swagger(?P<format>\.json|\.yaml)$',schema_view.without_ui(cache_timeout=0),name='schema-json'),path('doc/',schema_view.with_ui('swagger',cache_timeout=0),name='schema-swagger-ui'),path('redoc/',schema_view.with_ui('redoc',cache_timeout=0),name='schema-redoc'),path('demo/<int:pk>',DemoAPIView.as_view()),]go tohttp://127.0.0.1:8000/doc
Package: yash
Sometimes I make reminds me how old I am, yash makes me feel like a baby again.Getting startedInstall withpip install yash.The yash command will print out the jobs it find. Pipe it to bash if you want
to execute what it generates:yash# lists jobsyashjobname# print a job in bashyashjobname|bash-eux# run a job in a local bash shellIt will look for jobs in ayash.ymlfile that you could write as such:---env:someglobal:foo---name:examplescript:-myvar=x-some--super--long=$myvarlineThen you could see your generated bash withyaml exampleand execute it withyaml example | bash -euxor something.
Package: yash-ai
$yashi/\_/\(o.o)/|\Yashi: Yet Another SHell aI Command-Line CompanionAre you a person who had trouble remembering shell commands and flags for all shell commands? This is yet another helper that
can make the shell more human-friendly. Just say what to do, not remember. Simply input what you want to do in natural language, andyashiwill suggest single-line commands that achieve your intent.UsageYou can install Shell-AI directly from PyPI using pip:pip install yash-aiAfter installation, you can useyashicli.KeysThis tool based onCohereLLM service. To enable CLI working you should exposeapi keyas env variable and enjoy.export YASHI_COHERE_KEY='gYjIU2+....'
Package: yashiro
yashirois just a thin wrapper around jinja.In a nutshellInstallationThe easiest way is to usepoetryto manage your dependencies and addyashiroto them.[tool.poetry.dependencies]yashiro="*"Usageyashiro, once installed offers a single command,yashiro, that parses the templated based on a JSON file.yashirofollows the GNU recommendations for command line interfaces, and offers:-hor--helpto print help, and-Vor--versionto print the versionYou can use yashiro to parse a template.usage: yashiro [-h] [-V] [-m MAPPING] [-s] -t TEMPLATE

optional arguments:
    -h, --help             Show this help message and exit
    -m/--mappings MAPPINGS The path to the file that contains the mappings
    -t/--template TEMPLATE The path to the template
    -V/--version           Print the version and exitLinksDocumentationChangelog
Package: yasik
Yasik: Optimization Pipeline Integration for Racecar SimulationOverviewYasik is a domain-specific language (DSL) designed to seamlessly connect optimization pipelines with racecar simulations. It provides a powerful and flexible interface to streamline the integration of optimization algorithms into the simulation environment, enabling efficient tuning and enhancement of racecar performance.FeaturesOptimization Pipeline Integration: Yasik serves as a bridge between optimization algorithms and racecar simulations, facilitating the exchange of data and control commands.Parameter Configuration: Easily define and configure the parameters to be optimized in the racecar simulation. Yasik allows users to specify the variables subject to optimization and their respective ranges.Real-time Communication: The language supports real-time communication between the optimization pipeline and the racecar simulation, enabling dynamic adjustments and rapid iterations during the optimization process.Customizable Fitness Functions: Users can define and implement custom fitness functions tailored to their specific optimization goals. This allows for a wide range of optimization scenarios, from speed improvement to energy efficiency.
Package: yasimavr
Yet another simulator for Microchip AVR microcontrollers, inspired from simavr_yasimavr_ is a simulator for AVR 8-bits microcontrollers.
It is mainly aimed at the Mega0 and Mega1 series (ATmega80x-160x-320x-480x and others)
with a possibility to work with the “classic” series. (ATMega48/88/168/328 and others)It is composed of 2 layers:a C++ layer for the core API and the various peripheral simulation modelsa Python layer to handle the configuration, utilities, data recording, and external componentsInstallationPrerequisites:Python (version >=3.7) and PIPFor Ubuntu, libelf is required:sudoapt-getinstalllibelf-devInstall:execute:pip install yasimavrThe python bindings for the C++ librairies are built with the SIP tool from RiverbankComputing
(https://www.riverbankcomputing.com)ThanksQuite a few ideas in this software - and even big chunks of code - originate from simavr.
(https://github.com/buserror/simavr)
Big thanks to the simavr authors for this great tool !Supported IOsGPIOSPITWIUSARTSupported CoresThe package includes a predefined set of MCU models:ATMegaxx8 series (ATMega48/88/168/328)ATMega 0-series (ATMega808/809/1608/1609/3208/3209/4808/4809)Other device models can be easily simulated by creating a YAML config file.
A template is provided, and the example _atgiga4809_ shows how to load and use a customised device configuration.
New simulation models for peripherals can be created in Python or C++ using the provided API.FeaturesReal-time/Fast mode : yasimavr can try to sync the simulated time with system time or run as fast as possibleAVR-GDB integration : yasimavr can acts as a GDB backend stub, with support for breakpoints and watchpointsVCD export : yasimavr can export traces of pin states, GPIO ports, interrupt vectors, memory locations or generic signals in Value Change Dump (VCD) filesMCU dump : at any point of the simulation, yasimavr can create a snapshot of the state of the MCU model, including all registers and memories and save it in a text file.“Zombie” mode : yasimavr can directly interact with simulated peripherals by acting as the CPU. This is useful to verify customised peripheral models or a test script.Probing : yasimavr can read/write CPU registers or memories on-the-fly. This is useful to force the firmware into certain branches for example, improving test coverage.How to use_yasimavr_ can be used as a Python package to run a prepared simulation script.
(See the examples for how it looks like)It also supports direct command line use:python -m yasimavr [options] [firmware]For the list of command line options, execute python -m yasimavr -hSome simple script examples are available here:https://github.com/clesav/yasimavr/tree/main/examplesDocumentationThe documentation is still a work in progress but will be progressively completed.
The online version, including an API reference, can be read on the Read the Docs:[Development documentation]http://yasimavr.readthedocs.io/en/latest/[Stable documentation]http://yasimavr.readthedocs.io/en/stable/