Saravanan666/pypi-org huggingface dataset:
Package: vanqc
vanqcVariant Annotator and QC Checker for Human Genome SequencingInstallation$pipinstall-Uhttps://github.com/dceoy/vanqc/archive/main.tar.gzDependent commands:pigzbgziptabixbcftools(andplot-vcfstats)javasnpEff(java -jar snpEff.jar)gatkvepDocker imagePull the image fromDocker Hub.$dockerimagepulldceoy/vanqcUsageNormalize VCF files using Bcftools$vanqcnormalize/path/to/reference.fa/path/to/variants.vcf.gzAnnotate variants using SnpEff$vanqcdownload--snpeff--dest-dir=/path/to/resource
$vanqcsnpeff\/path/to/resource/snpeff_data/GRCh38.86/path/to/reference.fa\/path/to/variants.vcf.gzAnnotate variants using GATK Funcotator$vanqcdownload--funcotator--dest-dir=/path/to/resource
$vanqcfuncotator\/path/to/resource/funcotator_dataSources.v1.7.20200521\/path/to/reference.fa\/path/to/variants.vcf.gzAnnotate variants using Ensembl VEP$vanqcdownload--vep--dest-dir=/path/to/resource
$vanqcvep\/path/to/resource/vep_cache/homo_sapiens\/path/to/reference.fa\/path/to/variants.vcf.gzCollect VCF stats using Bcftools$vanqcstats/path/to/reference.fa/path/to/variants.vcf.gzCollect variant calling metrics using GATK (Picard)$vanqcmetrics\/path/to/reference.fa\/path/to/dbsnp.vcf.gz\/path/to/variants.vcf.gzRunvanqc --helpfor more information.
Package: van.reposync
Mirror a pypi-style egg repository from a debian APT repositoryDisclaimerThis package depends on an as-yet-unreleased version of python-apt (0.8) for
which it seems the only download location is debian’s experimental
distribution. Currently the version there is 0.7.91.While this code is tested for simple cases, a lot of the different failure
modes have not been tested. So if it breaks for you, please add your failure to
the automated tests and submit a patch!UseDoctest setup (so we can test the documentation):>>> import tempfile, os
>>> tmp_dir = tempfile.mkdtemp()>>> from van.reposync import tests
>>> tests_dir = os.path.dirname(tests.__file__)
>>> sources1_list = os.path.join(tmp_dir, 'sources1.list')
>>> open(sources1_list, 'w').write(open(os.path.join(tests_dir, 'sources1.list'), 'r').read() % {'tests_dir': tests_dir})We provide one binaryvan-reposyncwhich can be run to perform the
synchronization. The first time it’s run, it requires an –apt-sources
parameter pointing at an apt sources.list file containing the apt-repositories
you wish to mirror. It is important that the .list file contain matching deb
and deb-src lines (mirroring needs both binary and source packages):>>> tests.runit('van-reposync sync --root %s --apt-sources %s' % (tmp_dir, sources1_list))After it’s done, you will see that the directory you pointed the tool at
contains an apt configuration, buildout configuration and tarballs linked into
a pypi-style repository.The next run does not require the apt-sources command, and should be a lot faster:>>> tests.runit('van-reposync sync --root %s' % tmp_dir)TearDown>>> import shutil
>>> shutil.rmtree(tmp_dir)Changes1.0.1 (2009-06-15)Fix buildout versions file syntax.Use a very simple layout of pypi repository. All the tarballs in one directory.1.0.0 (2009-06-15)Initial Release.
Package: van.resumelb
Pool for zc.resumelb to divide workers by versionThis module contains a pool class which uses the classifier to route
requests to multiple sub-pools of workers. Each sub-pool works like a
normal zc.resumelb pool but only on workers of a single version.The classifier will decide which of the sub-pools will serve the request
by returning a tuple (version, classifier). This request will be served
by a sub-pool containing only workers of a matching version.UsageAt the current time, the only way is to monkey patch the zc.resumelb.lb.Pool class
Like this:>>> from van.resumelb import Pool
>>> import zc.resumelb.lb
>>> zc.resumelb.lb.Pool = PoolWARNING: you must pass –single-version to the loadbalancer startup
script.  If you do not, all the workers will have a version of None.In the future it should be possible to set the Pool class on the command
line. The code to enable command-line overriding of the pool is awaiting
merging at:http://zope3.pov.lt/trac/browser/zc.resumelb/branches/jinty-external-pool/
Package: vantage
IntroductionThe growing complexity of cancer diagnosis and treatment requires data sets that are larger than currently available in a single hospital or even in cancer registries. However, sharing patient data is difficult due to patient privacy and data protection needs. Privacy preserving distributed learning technology has the potential to overcome these limitations. In this setting organizations can collaborate by exchanging aggregated data and/or statistics while keeping the underlying data safely on site and undisclosed. This repository contains software (and instructions) to setup a distributed learning infrastructure.For an overview of the architecture and information on how to use the infrastructure, please seehttps://distributedlearning.ai. For documentation please seehttps://distributedlearning.readme.io.Hardware and software requirementsServerRunning the central server requires a (virtual) machine that:is accessible from the internethas Python 3.6+ and the vantage package installed (pip install vantage)NodeRunning a node/site requires a (virtual) machine that has:Python 3.6+ and the vantage package installed (pip install vantage)Docker CE installed (the user running the node software needs to have the proper permissions to perform docker commands)Access to a local data storeAccess to the internet and/or central serverInstallationSee thereadme.iofor detailed instructions on how to install the server and nodes.
Package: vantetider-scraper
This is a scraper for statistical data fromhttp://www.vantetider.sebuilt on top of theStatscraper package <https://github.com/jplusplus/statscraper>.Installpip install -r requirements.txtThe scraper has to do a lot of requests and usesrequests-cache <https://pypi.python.org/pypi/requests-cache>to store queries.Example usagefromvantetiderimportVantetiderScraperscraper=VantetiderScraper()scraper.items# List _implemeted_ datasets# [<VantetiderDataset: VantatKortareAn60Dagar (Väntat kortare än 60 dagar )>, <VantetiderDataset: Overbelaggning (Överbeläggningar)>, <VantetiderDataset: PrimarvardTelefon (Telefontillgänglighet)>, <VantetiderDataset: PrimarvardBesok (Läkarbesök)>, <VantetiderDataset: SpecialiseradBesok (Förstabesök)>, <VantetiderDataset: SpecialiseradOperation (Operation/åtgärd)>]dataset=scraper.get("Overbelaggning")# Get a specific dataset# List all available dimensionsprintdataset.dimensionsprintdatatset.regions# List available regionprintdatatset.years# List available years# Make a query, you have to explicitly define all dimension values you want# to query. By default the scraper will fetch default values.res=dataset.fetch({"region":"Blekinge","year":"2016","period":"Februari",# Currenty we can only query by id of dimension value"type_of_overbelaggning":["0","1"],# "Somatik" and "Psykiatri"})# Do something with the resultdf=res.pandasPractical application, using dataset.py for storege.fromvantetiderimportVantetiderScraperfromvantetider.allowed_valuesimportTYPE_OF_OVERBELAGGNING,PERIODSimportdatasetdb=dataset.connect('sqlite:///vantetider.db')TOPIC="Overbelaggning"# Set up local dbtable=db.create_table(TOPIC)scraper=VantetiderScraper()dataset=scraper.get(TOPIC)# Get all available regions and years for queryyears=[x.valueforxindataset.years]regions=[x.valueforxindataset.regions]# Query in chunks to be able to store to database on the runforregioninregions:foryearinyears:res=dataset.fetch({"year":year,"type_of_overbelaggning":[x[0]forxinTYPE_OF_OVERBELAGGNING],"period":PERIODS,"region":region,})df=res.pandasdata=res.list_of_dictstable.insert_many(data)TODOImplement scraping of “Aterbesok”, “Undersokningar”, “BUPdetalj”, “BUP”.Enable querying on label names on all dimensionsAdd more allowed values tovantetider/allowed_values.pyMake requests-cache optional.DevlopRun tests:make tests
Package: vantiqservicesdk
Repository ContentsThis repository contains an SDK for building Vantiq Service Connectors in Python. Service connectors allow some of all
of a Vantiq service to be implemented outside the Vantiq platform.  The typical use case for this is to leverage a language other than VAIl (in this case Python).  One major use case for Python is to take advantage of its broad support for ML/Generative AI libraries.Overview of a Service ConnectorTBD...Building a Service ConnectorRunning this code required Python 3.8 or later.To use the SDK, install it into your Python environment.  Usingpip,pip install vantiqservicesdkOther Python environment tools may use different commands.Once it is installed, you can import it.fromvantiqservicesdkimportBaseVantiqServiceConnectorMore details TBD...The MIT License (MIT)Copyright (c) 2023 Vantiq, Inc.Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, 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 THE
SOFTWARE.
Package: vanty
Vanty CLICLI for managing projects from advantch.com including:The Starter KitThe Starter Kit is the fastest way to launch new SaaS & AI products.Visithttps://www.advantch.com/for documentation and more informationAdvantch Cloud (closed alpha)Deploy databases and storage for your appsVanty.ai (closed alpha)Vanty.ai is Agent platform for businesses and professionals. Quickly spin up entire documentation site, manage content, research, customer support with agents.InstallationWe recommend using poetry to install vanty:poetryinstallvantyUsageVerify your license:vantyauthverify<your-license-token>(closedalpha)Download the project to the current directory:vantyprojectdownload--project<project-id>(closedalpha)Get started:cd<project-name>
vantydevinitRun the project:vantydevstartTemplate GENZip the template filecd./scaffold/template/&&zip-rXapp_template.zipapp_template-x".*"-x"__MACOSX"Copy to bucketrclone copy app_template.zip advantch-prod:template/app_template.zipDocsAdvantch DocsLocal CLI DocsIssues & Support:Advantch users can report issues on the slack issue channel.https://www.advantch.com/support/Building for pypipoetrybuild
poetrypublishPRs and Contributions:Please note that whilst this is open source, it is not intended to be a community project.Advantch users can submit PRs for extensions etc that maybe helpful to the core project or other users.Otherwise, please fork and use this as a base for your own projects.2023 · Advantch.
Package: vape
# Ｖ　Ａ　Ｐ　ＥA command-line aesthetic text generator. Perfect for your new Vaporwave band.Might also be useful for getting around text filters, who knows.## Ｉｎｓｔａｌｌａｔｉｏｎ$ pip install vape## Ｕｓａｇｅ$ vape I want to be AESTHETIC
Ｉ　ｗａｎｔ　ｔｏ　ｂｅ　ＡＥＳＴＨＥＴＩＣYou can copy the result to the clipboard with-c.You can also force upper case text with-u.And that’s all it does!## See Also
* [Emo](https://github.com/Miserlou/Emo)
* [Zalgo](https://github.com/Miserlou/Zalgo)Ｒｉｃｈ　Ｊｏｎｅｓ　２０１７
Package: vaping
Vapingvaping is a healthy alternative to smokeping!*(This statement has not been evaluated by the Food and Drug Administration)IntroductionVaping provides the following features:Real-time latency graphing viewable in the browserLine and smokestack graphsContainerized and easy to setup and configureSupport for time-series databasesPlugin-based design to allow integration with other servicesSupports distributed setups through message queueVaping is a Python daemon which polls for input and sends its output through plugins.It has a standalone mode to directly serve realtime graphs in a browser, or can use ZeroMQ to distribute messages.InstallationpipinstallvapingYou will need a compiler and Python development libraries for some components, which you can obtain with thegccandpython-develpackages for your operating system.Alternatively, you can use theDocker image, which includes all requirements.Quick StartTo use Vaping, you need first a configuration file that defines which hosts to target and where to send the output. You can have a look atthe examples in this repositoryand adapt them to your needs.Then, start thevapingprogram from the command line, specifying the path to the configuration file.A quick start example isavailable here. It shows you how to ping multiple hosts and display the resulting graphs using a local web server.UsageVaping has a command-line interface with the following usage:Usage: vaping [OPTIONS] COMMAND [ARGS]...

  Vaping

Options:
  --version    Show the version and exit.
  --quiet      no output at all
  --verbose    enable more verbose output
  --home TEXT  specify the home directory, by default will check in order:
               $VAPING_HOME, ./.vaping, ~/.config/vaping
  --debug      enable extra debug output
  --help       Show this message and exit.

Commands:
  start    start a vaping process
  stop     stop a vaping process
  restart  restart a vaping processstartStarts a vaping process, by default will fork into the background unless--debugor--no-forkis passed.It adds options:-d, --no-fork  do not fork into backgroundstopStops a vaping process identified by$VAPING_HOME/vaping.pidDocumentationDocumentation is created with mkdocs and available here:stable:http://vaping.readthedocs.io/en/stable/latest:http://vaping.readthedocs.io/en/latest/ChangesThe current change log is available athttps://github.com/20c/vaping/blob/master/CHANGELOG.mdLicenseCopyright 2016-2021 20C, LLCLicensed under the Apache License, Version 2.0 (the "License");
you may not use this software except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: vapi-python
Vapi Python SDKThis package lets you start Vapi calls directly from Python.InstallationYou can install the package via pip:pipinstallvapi_pythonUsageFirst, import the Vapi class from the package:fromvapi_pythonimportVapiThen, create a new instance of the Vapi class, passing your API Key as a parameter to the constructor:vapi=Vapi(api_key='your-api-key')You can start a new call by calling thestartmethod and passing anassistantobject orassistantId. You can find the available options here:docs.vapi.ai <https://docs.vapi.ai/api-reference/assistants/create-assistant>vapi.start(assistant_id='your-assistant-id')orassistant={'firstMessage':'Hey, how are you?','context':'You are a shopping assistant...','model':'gpt-3.5-turbo','voice':'jennifer-playht',"recordingEnabled":True,"interruptionsEnabled":False,"functions":[{"name":"setColor","description":"Used to set the color","parameters":{"type":"object","properties":{"color":{"type":"string"}}}}]}vapi.start(assistant=assistant)Thestartmethod will initiate a new call.You can stop the session by calling thestopmethod:vapi.stop()This will stop the recording and close the connection.LicenseMIT LicenseCopyright (c) 2023 Vapi Labs Inc.Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, 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 THE
SOFTWARE.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2023-11-17)First release on PyPI.
Package: vapordmods
DisclaimersThis project is a way to learn and improve my knowledge of Python. The code and how it's implemented is far from perfect.Keep in mind that this module is new and bugs are possiblesFeaturesThis module allows to update mods (Thunderstore,NexusmodsandSteam Workshop) automatically. You configure the filevapordmods.ymlwith the mods, the version and the folder you want.Everytime the code run, a comparaison is made with thevapordmods.manifestfile to compare the version of the installed mods with the latest version if no specific version is identified.If an update is required, or it's not installed, the mod is downloaded and installed int the directory you have specified.The mods are not removed automatically and you need to remove the mods manually.Management ofThunderstoremodsManagement ofNexusmodsmodsYou need to be premium to use this feature. You cannot have the download link without it.Management ofSteam WorkshopmodsYou can only update to the latest version at the moment.InstallExamplesConfiguration filesconfig:
  default_mods_dir: /opt/mods/`

mods:
  - provider: thunderstore
    app: ishid4
    mods: BetterArchery
    version: 1.7.5
    
  - provider: thunderstore
    app: OdinPlus
    mods: OdinsTraps
    version:
    
  - provider: thunderstore
    app: OdinPlus
    mods: ShrinkMe
    version: 
  
  - provider: workshop
    app: 896660
    mods: 1025487Codes
Package: vaporetto
🐍 python-vaporetto 🛥Vaporettois a fast and lightweight pointwise prediction based tokenizer.
This is a Python wrapper for Vaporetto.InstallationInstall pre-built package from PyPIRun the following command:$ pip install vaporettoBuild from sourceYou need to install the Rust compiler followingthe documentationbeforehand.
daachorse usespyproject.toml, so you also need to upgrade pip to version 19 or later.$ pip install --upgrade pipAfter setting up the environment, you can install daachorse as follows:$ pip install git+https://github.com/daac-tools/python-vaporettoExample Usagepython-vaporetto does not contain model files.
To perform tokenization, followthe document of Vaporettoto download distribution models or train your own models beforehand.Check the version number as shown below to use compatible models:>>>importvaporetto>>>vaporetto.VAPORETTO_VERSION'0.6.3'Examples:# Import vaporetto module>>>importvaporetto# Load the model file>>>withopen('tests/data/vaporetto.model','rb')asfp:...model=fp.read()# Create an instance of the Vaporetto>>>tokenizer=vaporetto.Vaporetto(model,predict_tags=True)# Tokenize>>>tokenizer.tokenize_to_string('まぁ社長は火星猫だ')'まぁ/名詞/マー 社長/名詞/シャチョー は/助詞/ワ 火星/名詞/カセー 猫/名詞/ネコ だ/助動詞/ダ'>>>tokens=tokenizer.tokenize('まぁ社長は火星猫だ')>>>len(tokens)6>>>tokens[0].surface()'まぁ'>>>tokens[0].tag(0)'名詞'>>>tokens[0].tag(1)'マー'>>>[token.surface()fortokenintokens]['まぁ','社長','は','火星','猫','だ']Note for distributed modelsThe distributed models are compressed in zstd format. If you want to load these compressed models,
you must decompress them outside the API.>>>importvaporetto>>>importzstandard# zstandard package in PyPI>>>dctx=zstandard.ZstdDecompressor()>>>withopen('tests/data/vaporetto.model.zst','rb')asfp:...withdctx.stream_reader(fp)asdict_reader:...tokenizer=vaporetto.Vaporetto(dict_reader.read(),predict_tags=True)Note for KyTea's modelsYou can also use KyTea's models as follows:>>>withopen('path/to/jp-0.4.7-5.mod','rb')asfp:# doctest: +SKIP...tokenizer=vaporetto.Vaporetto.create_from_kytea_model(fp.read())Note: Vaporetto does not support tag prediction with KyTea's models.Speed ComparisonLicenseLicensed under either ofApache License, Version 2.0
(LICENSE-APACHEorhttp://www.apache.org/licenses/LICENSE-2.0)MIT license
(LICENSE-MITorhttp://opensource.org/licenses/MIT)at your option.ContributionSeethe guidelines.
Package: vaporize
Vaporize is a clean and consistent library for accessing theRackspace Cloud API.Another One?There are already a number of good Python options out there for accessing the
Rackspace Cloud API. Unfortunately they have one thing in common: none of them
use the same pattern. This means a lot of wasted time looking things up in
the documentation. The other problem is that there is a library for each group
of sendpoints on the Rackspace cloud API. There’s a libarry for CloudServers,
one for CloudFiles, one for CloudDNS and one for CloudLoadBalancers. Which menas
there’s a lot of redundant code, requiring four imports and four instantiations
and it’s up to you to smooth over incompatibilities between them.So Vaporize was born. One library for one API.InstallationTo install Vaporize using PIP:pip install vaporizeUsageVaporize is designed to be simple to use. Connect to your Rackspace Cloud in
just two lines of code:>>> import vaporize
>>> vaporize.connect('username', 'apikey')DocumentationAPI Documentation and a User Guide (in the works) availablehere.ContributingVaporize is currently still in development. Contributors are welcome. Just fork
this repo and start making pull requests.FeaturesSupport for the following Rackspace Cloud services:CloudServersCloudServers - Next Gen.CloudLoadBalancersCloudDNSCloudDatabasesCloudBlockStoragePlanned FeaturesSupport for the following Rackspace Cloud services:CloudFilesCloudMonitoring