Saravanan666/pypi-org huggingface dataset:
Package: vaderSentiment-fr
VADER-Sentiment-Analysis-FRThis is a French version of VADER (Valence Aware Dictionary and Sentiment Reasoner). Please visit <https://github.com/cjhutto/vaderSentiment> to see the original version. Vader_FR possesses a manually translated french lexicon.
VADER is a lexicon and rule-based sentiment analysis tool that isspecifically attuned to sentiments expressed in social media. It is fully open-sourced under the[MIT License](the original VADER sincerely appreciate all attributions and readily accept most contributions, but please don’t them and us liable).7LieuesTechnologies aims to support your company, from IT to accounting, by building a virtual assistant which provides multi-technologies and multi-trade help.Install Vader-frpip install vaderSentiment-frHOW TO USE Vader-FRfrom vaderSentiment_fr.vaderSentiment import SentimentIntensityAnalyzerSIA = SentimentIntensityAnalyzer()phrase = “Une phrase très cool à analyser”score = SIA.polarity_scores(phrase)# Note : You can use polarity_scores_max instead of polarity_scores. polarity_scores_max uses fuzzywuzzy to get the most similar words with your inputs. For example “connar” won’t be detected with polarity_scores but with polarity_scores_max
Package: vadivelu
A Python library for accessing theVadivelu API.InstallationMust have Python 3.7 or higherpipinstallvadiveluAPI Referencevadivelu.get_available_formats(code)Get the available formats for a HTTP code. The formats can be gif, jpg, or both.Parameterscode:intThe HTTP code to useReturnsTuple[str, str]A tuple containing the avaiable formatsRaisesValueErrorif the code provided is not supported by the APIpick_available_format(code, priority)Pick a format (gif or jpg) that the API supports for a specific HTTP code.Parameterscode:intThe HTTP code to usepriority:strThe format to pick in the case both gif and jpg are available, defaults to gifReturnsstrThe chosen format (gif or jpg)RaisesValueErrorif the code provided is not supported by the API or ifpriorityis notgiforjpgget_media_url(code, media_format)Get the media URL for the specified code in the specified format.Parameterscode:intThe HTTP code to usemedia_format:strThe media format to get (gif or jpg)ReturnsstrTheurl for the mediaRaisesValueErrormedia_formatis not available for the given code, usepick_available_formatto avoid this errorExampleimportrequestsimportvadivelucode=404url=vadivelu.get_media_url(code,vadivelu.pick_available_format(code,'jpg'))# save jpg to 404.jpg filewithopen('404.jpg','wb')asf:f.write(requests.get(url).content)
Package: vaede
VAEDEVariational Auto Encoder for distribution estimationFastAPI-Project-TemplateThe base to start an openapi project featuring: SQLModel, Typer, FastAPI, JWT Token Auth, Interactive Shell, Management Commands.Install it from PyPIpipinstallvaedeUsagefromvaedeimportBaseClassfromvaedeimportbase_functionBaseClass().base_method()base_function()$python-mvaede#or$vaedeDevelopmentRead theCONTRIBUTING.mdfile.
Package: vaers
safety_vaccineThis is under review.The latest data of VAERS (Vaccine Adverse Event Reporting System) dataset is
available at:https://vaers.hhs.gov/eSubDownload/index.jsp?fn=2021VAERSData.zipset operationsvaers.py shows a good example of intersection operation.
The following figure shows what is intersection operation.How to install vaersOn WSL on Windows, MacOS, or Linux operating systems:$ pip install vaersOn Windows 11 or 10:$ pip install vaers --force-reinstall --no-cache-dir --no-binary :all:How to run vaers$ vaersor$ vaers 2022How to run vaers.py0. Download 2021VAERSData.zip
1. Unzip 2021VAERSData.zip file
2. Run vaers.py program
$ python vaers.py
total instances:  677514
total deaths 8926
NOVIDs instances:  1202
NOVIDs deaths:  4
NOV death per instance 0.003328
MODERNA+PFIZER: 905
MODERNA+PFIZER death: 4
MODERNA+PFIZER death per instance: 0.00442
MODERNAIDs instances:  299195
MODERNA deaths 3648
MODERNA 0.012193
PFIZERIDs instances:  283061
PFIZER deaths 3969
PFIZER 0.014022How to run pfizerAge.py to generate a pfizer.csv file.$ python pfizerAge.pyHow to run modernaAge.py to generate a moderna.csv file.$ python modernaAge.pyHow to calculate safety thresholds of PFIZER and MODERNA vaccines by age$ python deathperinstance.pySubtraction operationset(A).difference(B)Union operationset(A).union(B)Symmetric difference (ExclusiveOR)set(A).symmetric_difference(B)
Package: vaers-downloader
VAERS DownloaderThis project downloads VAERS Data fromhttps://vaers.hhs.gov/data.htmlit depends on tensorflow to automatically trained models to solve the captcha and download data intoVAERS/directory.Installpipinstallvaers_downloaderUsagevaers_downloader[--start=2019][--end=2019][--directory=/tmp]This will create a new directory namedVAERSin the currect directory where you ran the above command.Pass a parameter or two for the start and end year as below to download data for those years only.Default: basically will download all data from 2019 to whatever the year it is when you run it.python-mvaers_downloader.download-s2000python-mvaers_downloader.download--start1990-e1999-d/tmpDevelopmentpython3-mvenv.venvsource.venv/bin/activate
pip3install-rrequirements.txtimportosimportsyssys.path.insert(0,'./vaers_downloader')fromVAERSFileDownloaderimportupdateVAERSFilesupdateVAERSFiles(needsUpdate=True,years=[2019,2020,2021,2022],workingDirectory=os.getcwd())Thanks to Frank Knoll for the original code...https://github.com/KnollFrank/HowBadIsMyBatchTestspythonsetup.pytestLicenseSeeLICENSEAuthorsFrank Knoll (https://github.com/KnollFrank)Alex Goretoy (https://github.com/McFlat)
Package: vaex
What is Vaex?Vaex is a high performance Python library for lazyOut-of-Core DataFrames(similar to Pandas), to visualize and explore big tabular datasets. It
calculatesstatisticssuch as mean, sum, count, standard deviation etc, on anN-dimensional gridfor more thana billion(10^9) samples/rowsper
second. Visualization is done usinghistograms,density plotsand3d
volume rendering, allowing interactive exploration of big data. Vaex uses
memory mapping, zero memory copy policy and lazy computations for best
performance (no memory wasted).InstallingWith pip:$ pip install vaexOr conda:$ conda install -c conda-forge vaexFor more details, see the documentationKey featuresInstant opening of Huge data files (memory mapping)HDF5andApache Arrowsupported.Read the documentation on how to efficiently convert your datafrom CSV files, Pandas DataFrames, or other sources.Lazy streaming from S3 supported in combination with memory mapping.Expression systemDon't waste memory or time with feature engineering, we (lazily) transform your data when needed.Out-of-core DataFrameFiltering and evaluating expressions will not waste memory by making copies; the data is kept untouched on disk, and will be streamed only when needed. Delay the time before you need a cluster.Fast groupby / aggregationsVaex implements parallelized, highly performantgroupbyoperations, especially when using categories (>1 billion/second).Fast and efficient joinVaex doesn't copy/materialize the 'right' table when joining, saving gigabytes of memory. With subsecond joining on a billion rows, it's pretty fast!More featuresRemote DataFrames (documentation coming soon)Integration intoJupyter and Voila for interactive notebooks and dashboardsMachine Learning without (explicit) pipelinesContributingSeecontributingpage.SlackJoin the discussion in ourSlackchannel!Learn more about VaexArticlesBeyond Pandas: Spark, Dask, Vaex and other big data technologies battling head to head(includes benchmarks)7 reasons why I love Vaex for data science(tips and trics)ML impossible: Train 1 billion samples in 5 minutes on your laptop using Vaex and Scikit-LearnHow to analyse 100 GB of data on your laptop with PythonFlying high with Vaex: analysis of over 30 years of flight data in PythonVaex: A DataFrame with super strings - Speed up your text processing up to a 1000xVaex: Out of Core Dataframes for Python and Fast Visualization - 1 billion row datasets on your laptopFollow our tutorialsWatch our more recent talks:PyData London 2019SciPy 2019Contact us for data science solutions, training, or enterprise support athttps://vaex.io/
Package: vag
vagvag is a command line utility tool for vagrant, docker andbuilderDocumentationread the docsInstallation$pipinstallvagList of top level commands$vag
Usage:vag[OPTIONS]COMMAND[ARGS]...

Options:--helpShowthismessageandexit.

Commands:buildBuildsvagrantboxcleanCleansupvagrantbuild,terminatesvagrantinstanceetcdockerDockerautomationinitCreatesanewVagrantfileinstanceVagrantInstanceAutomationpushPublishesvagrantboxtotargetenvironmentsshSSHtovagranttestVagrantinstancetestStartatestVagrantinstanceversionPrintsversionDevelopmentlocal vag installation$rm-rfdist/*
$poetryinstall
$poetrybuild
$pipuninstall-yvag
$pipinstalldist/*.whledit and run$poetryrunvagvagrantlist
Package: vagalume
# python-vagalumeA simple Python client for Vagalume API. Also a command-line utility to searchsong lyrics and translations.## Install$ pip install vagalume## UsageYou can use it from the **command line**:$ vagalume lyric 'radiohead' 'there, there'With the -t flag, you can find translations (by default to pt-br):$ vagalume lyric 'radiohead' 'there, there' -tBut is possible to define another language supported by API ('fr', 'en', 'pt-br', 'pt', 'nl', 'de', 'jp', 'it' or 'es'):$ vagalume lyric 'radiohead' 'there, there' -t esAlso, you can use the **module** on your own code:```pythonfrom vagalume import lyricsartist_name = 'radiohead'song_name = 'there, there'result = lyrics.find(artist_name, song_name)if result.is_not_found():print 'Song not sound'else:print result.song.nameprint result.artist.nameprintprint result.song.lyricprinttranslation = result.get_translation_to('pt-br')if not translation:print 'Translation not found'else:print translation.nameprint translation.lyric```
Package: vagas-cfs
Relatório de Acompanhamento de vagas em câmpus fora de sedeIndica a quantidade de vagas ocupadas pelos estudantes em cada disciplina dos Campus Fora de Sede (CFS).Recebe como entrada o seguintes relatório extraído do sistema Prime:Relatório de Carga Horária por turma / disciplina.Não contem a informação de salas nem de capacidadeUtilizado diariamente durante o período de Ajuste Acadêmico
Package: vagquery
vagqueryA python library for generating and executing queries for the VAG public transport systemstart.vag.deThis enables users with disabilities, hackers and you to receive information from the start.vag website.General usageTwo classes are usually used:import vagquery
    # Query for stations beginning with 'Schwe'
    stations = vagquery.StationQuery("Schwe").query()
    for station in stations:
        print(station)


    # Query for next departures of the main railway station (id: 510)
    departures = vagquery.DepartureQuery(510).query()
    for departure in departures:
        print(departure)Specialized usageFor repeated queries, the query object can be created and run again and again:dquery = vagquery.DepartureQuery(510)
    departures = dquery.query()
    # ...
    # much later
    # ...
    departures = dquery.query()For a custom formating of the departures, the properties of a departure object can be used:departures = vagquery.DepartureQuery(510).query()
    for departure in departures:
        print(departure.product + " " + str(departure.latitude) + str(departure.longitude))Licensevagqueryis licensed under the MIT license.
Package: vagrant-debian
Vagrant-Debian is a program to create vagrant boxes from Debian repositoriesIn order to run, you need:a Debian linux system (maybe it works on Ubuntu)Virtualbox (if not install Vagrant-Debian will install it for you)Vagrant (Same, it's installed if not detected)a working -- and fast -- internet connection (about 500 MB will be downloaded)Enough diskspace on your current directory (around 3 Gbytes).Enough memory for your PC: 4GbytesTo run, just type on command line:vagrant_debian create --add --clean --headlessThis will silently download the Official Debian media, launch a virtualbox instance with the media, install the debian system, add necessary packages and create a runnable and minimal vagrant box.The script is compatible with Jessie, Stretch, Bullseye and unstable Debian versions and maybe with older (not tested) or future ones. The version is choosen with a command line option.
Warning: with Buster, you have to install virtualbox by yourself as it's not part of standard Debian packages.The size of created box is around 300MB large.Please use:vagrant_debian --helpTo display command help
or:vagrant_debian create --helpTo display help oncreatesubcommand.Have fun !
Package: vagrant-environment
vagrant-environmentis a Python wrapar for theVagrantcommand-line toolvagrant.Requirementsvagrant-environmentrequires:Vagrant(tested with version 1.6.3 under 64-bit Linux).Python 2.7.InstallationOnce you have installed VirtualBox and Vagrant, do the usual:$ pip install vagrant-environmentThat will installvagrant-environmentand its Python dependencies. You
might want to do that within a virtualenv.
Package: vagrant-metadata
vagrant-metadataCreates and updates Vagrant box metadata files (metadata.json)An utility program which creates and updates Vagrantbox metadata files. This program is meant for
small-scale deployment and versioning of Vagrant base boxes to private servers. For large scale
deployment it always better to just useAtlas.UsageInstallationThis program requires Python 2.7 and can be installed via PIP:pip install vagrant-metadataPublishing Your First BoxNow, let's suppose you have a base box calledprecise64in two flavors: one for Virtualbox and
the other for VMWare.vagrant-metadatafollows the "convention over configuration" principle and
it expects a certain directory layout to do its magic. Let's start by creating the required
directory structure:mkdir -p precise64/1.0.0/virtualbox
mkdir -p precise64/1.0.0/vmware_desktopAt this point you should have a directory tree like this:.
└── precise64
    └── 1.0.0
        ├── virtualbox
        └── vmware_desktopNow you should copy your boxes to their appropriate directory and end up with something like this:.
└── precise64
    └── 1.0.0
        ├── virtualbox
        │   └── precise64.box
        └── vmware_desktop
            └── precise64.boxNow let's go into theprecise64directory:cd precise64Since this is the first time we runvagrant-metadatawe'll have to tell it something about the
base box such as its URL, name and description:vagrant-metadata --name="lvillani/precise64" --description="Ubuntu 12.04 64-bit" \
    --baseurl="http://lorenzo.villani.me/vagrant/precise64"After a while, whenvagrant-metadataexits, you will find a freshmetadata.jsonfile which
contains all the box metadata. At this point you can publish the wholeprecise64directory
somewhere.Updating a BoxUpdating a box is easier than preparing a new one for publication. Let's suppose we want to
publish version2.0.0of theprecise64box described before: create a new2.0.0directory
with the same layout as before and copy your boxes there ending up with something like:.
└── precise64
    ├── 1.0.0
    │   ├── virtualbox
    │   │   └── precise64.box
    │   └── vmware_desktop
    │       └── precise64.box
    ├── 2.0.0
    │   ├── virtualbox
    │   │   └── precise64.box
    │   └── vmware_desktop
    │       └── precise64.box
    └── metadata.jsonAt this point go to theprecise64directory and runvagrant-metadata:cd precise64
vagrant-metadataNotice that we don't even have to specify the box name, description or base URL.Sync this directory with your remote server and you are done.If you don't have all box files downloaded locally, and want to add new version
to existing metadata.json simply add--append(-a) option to only add new
version box to existing metadata
Package: vagrant-playbook
Declarative cluster definition for vagrant, to be used with vagrant-compose plugin.vagrant-compose is a vagrant plugin, that can be used for creating complex multi-machine scenarios including several set of nodes/virtual machines, each one with different characteristic, software stacks and configuration.The DSL used by vagrant-compose requires some ruby knowledge, because the Vagrantfile itself is based on ruby, and this fact sometimes is and obstacle for people with limited programming background.By using vagrant-playbook this issue is mostly addressed because the definition of the cluster is done in yaml, and the ruby programming part within the Vagrantfile is reduced to the minimum.see github:https://github.com/fabriziopandini/vagrant-playbookfor detailed documentation.
Package: vagranttoansible
A simple library to convertvagrantssh-configto an inventory file
for Ansible.UsageInside your vagrant directory, run the scriptvagrant2ansibleThis
will create a filed namedhosts(your Ansible inventory) in the
current directory along with a file named.vagrant-ssh-config(the
output ofvagrantssh-config).The script has been tested withVagrant 2.0but it should work with
earlier versions of Vagrant too.Installation$pipinstallvagranttoansibleExample$vagranttoansibleThis configuration:Host machine1HostName 127.0.0.1User vagrantPort 2222UserKnownHostsFile /dev/nullStrictHostKeyChecking noPasswordAuthentication noIdentityFile /home/mha-dw/Projets/ansible/.vagrant/machines/machine1/virtualbox/private_keyIdentitiesOnly yesLogLevel FATALHost machine2HostName 127.0.0.1User vagrantPort 2200UserKnownHostsFile /dev/nullStrictHostKeyChecking noPasswordAuthentication noIdentityFile /home/mha-dw/Projets/ansible/.vagrant/machines/machine2/virtualbox/private_keyIdentitiesOnly yesLogLevel FATALwill bemachine1 ansible_host=127.0.0.1 ansible_user=vagrant ansible_ssh_common_args='-o StrictHostKeyChecking=no' ansible_ssh_private_key_file=/home/mha-dw/Projets/ansible/.vagrant/machines/machine1/virtualbox/private_key ansible_port=2222machine2 ansible_host=127.0.0.1 ansible_user=vagrant ansible_ssh_common_args='-o StrictHostKeyChecking=no' ansible_ssh_private_key_file=/home/mha-dw/Projets/ansible/.vagrant/machines/machine2/virtualbox/private_key ansible_port=2200$vagranttoansible--helpusage:vagranttoansible[-h][-V][-v][-oOUTPUT_FILE_NAME]Simplescripttotransform'vagrant ssh-config'outputtoaninventoryhostsforAnsible.Thisscriptmustberuninyourvagrantfolder.optionalarguments:-h,--helpshowthishelpmessageandexit-V,--versionPrintversionandexits-v,--verbosePrintmoreinformation-oOUTPUT_FILE_NAME,--output-file-nameOUTPUT_FILE_NAMETheinventoryfilenametowritehoststo.Default:stdoutTODOMaybe remove the stormssh dependencyTest with different Vagrant environmentsMore options will be added. Feel free to contribute.CreditsMohamed El Mouctar HAIDARA (elmhaidara@gmail.com)
Package: vahfka
vahfkaThe vahfka library simplifies Kafka interaction by providing convenient abstractions for both producing and consuming messages. It seamlessly integrates with the kafka-python library and offers additional features for asynchronous message sending and context management.InstallationYou can install the vahfka library using pip:pipinstallvahfkaUsageInitializing the Kafka Producer:fromvahfkaimportKafkaProducerWithCallbacksCreate a KafkaProducerWithCallbacks instancekafka_producer=KafkaProducerWithCallbacks(bootstrap_servers='localhost:9092')Sending Messages:Synchronous Message Sending:# Send a message synchronouslykafka_producer.send_message(topic='example_topic',message={'key':'value'})Asynchronous Message Sending:# Send a message asynchronouslykafka_producer.send_message_async(topic='example_topic',message={'key':'value'})KafkaConsumerWrapper:fromvahfkaimportKafkaConsumerWrapper# Create a KafkaConsumerWrapper instancekafka_consumer=KafkaConsumerWrapper(bootstrap_servers='localhost:9092',group_id='my-group',topics=['example_topic'])ConsumingMessages:# Consume messages from Kafka topicmessages=kafka_consumer.consume_messages()print(messages)ContextManagement:# Use the KafkaProducerWithCallbacks as a context managerwithKafkaProducerWithCallbacks(bootstrap_servers='localhost:9092')asproducer:producer.send_message(topic='example_topic',message={'key':'value'})ExamplefromvahfkaimportKafkaProducerWithCallbacks,KafkaConsumerWrapper# Initialize KafkaProducerWithCallbacks and KafkaConsumerWrapper instanceskafka_producer=KafkaProducerWithCallbacks(bootstrap_servers='localhost:9092')kafka_consumer=KafkaConsumerWrapper(bootstrap_servers='localhost:9092',group_id='my-group',topics=['example_topic'])# Send a messagekafka_producer.send_message(topic='example_topic',message={'key':'value'})# Consume messagesmessages=kafka_consumer.consume_messages()print(messages)For questions, issues, or contributions, please visit thehttps://gitlab.com/magmam/egov
Package: vai-jobspec-tools
vai-jobspec-toolsUtilities for VoxelAI jobspec containersInstallationpipinstallvai-jobspec-toolsAPIfrom__future__importannotationsimportcontextlibimportloggingfrompathlibimportPathfromtypingimportIteratordefconfigure_logger(logger:logging.Logger,*,verbosity:int=0,level:int|None=None,):"""Configure a logger with a level and custom stream handler."""...@contextlib.contextmanagerdefoptional_temporary_directory(workdir:str|Path|None=None)->Iterator[Path]:"""Create a temporary directory if one is not provided."""...defprepare_dst_uri(uri:str,subject_id:str,session_id:str,pipeline_name:str,pipeline_version:str,job_id:str,*,create:bool=True,)->str:"""Generate a URI scoped to a particular subject, session, pipeline, version and job"""...deflowercase_alnum(s:str)->str:"""Transform a string so that it contains only lowercase alphanumeric characters."""...
Package: vais
VAIS runs simulations of agents and players across multiple worldscreate a random world# planet parameters are: num_seeds, width, height, wind, rain, sun, lavap=planet.Planet('Test Planet',5,60,45,0.2,0.20,0.2,0.5)p.evolve(100)print(p)View the worldfldr=os.getcwd()+os.sep+'data'+os.sep+'worlds'view_world.display_map(fldr+os.sep+'ExamplePlanet.txt')create a character manuallystats={'Health':20,'max_health':20,'INT':8,'STA':5,'STR':2,'AGI':5}c1=character.Character('Jim','Orc','Mage',stats,['cast'],'Test',['bag'])print(c1)CHARACTER=JimRace=OrcClass=MageSTATS=STA:5AGI:5INT:8Health:20max_health:20STR:2Story=ExamplecharSKILLS=castINVENTORY=bagCreate a 2nd random character and battle themtraits=character.CharacterCollection(character.fldr)rules=battle.BattleRules('battle.rules')c2=traits.generate_random_character()b=battle.Battle(c1,c2,traits,rules,print_console='Yes')print(b.status+' Wins')Jim[100%]hitsCrador[100%]for4Crador[80%]hitsJim[100%]for4Jim[80%]missCrador[80%]Crador[80%]hitsJim[80%]for4Jim[60%]CRITCrador[80%]for12Crador[20%]hitsJim[60%]for4Jim[40%]hitsCrador[20%]for4JimWinsSimulate 10,000 fightssim=battle.BattleSimulator(c1,c2,traits,rules,10000)print(sim)After10000fightsJimwins!Jim=9158(92%)Crador=842(8%)