Saravanan666/pypi-org huggingface dataset:
Package: voo
📦 setup.py (for humans)This repo exists to providean example setup.pyfile, that can be used
to bootstrap your next Python project. It includes some advanced
patterns and best practices forsetup.py, as well as some
commented–out nice–to–haves.For example, thissetup.pyprovides a$ python setup.py uploadcommand, which creates auniversal wheel(andsdist) and uploads
your package toPyPiusingTwine, without the need for an annoyingsetup.cfgfile. It also creates/uploads a new git tag, automatically.In short,setup.pyfiles can be daunting to approach, when first
starting out — even Guido has been heard saying, "everyone cargo cults
thems". It's true — so, I want this repo to be the best place to
copy–paste from :)Check out the example!Installationcdyour_project# Download the setup.py file:#  download with wgetwgethttps://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.py-Osetup.py#  download with curlcurl-Ohttps://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.pyTo DoTests via$ setup.py test(if it's concise).Pull requests are encouraged!More ResourcesWhat is setup.py?on Stack OverflowOfficial Python Packaging User GuideThe Hitchhiker's Guide to PackagingCookiecutter template for a Python packageLicenseThis is free and unencumbered software released into the public domain.Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any means.
Package: voodoo-api
#Voodoo API## Examplefrom voodooapi import APIshipping_info = {"name": "Oliver Ortlieb","street1": "320 2nd Ave","street2": "#5","city": "New York","state": "NY","zip": "10003","country": "USA"}api_key = "your-api-key-goes-here"api = API(api_key)file_url = "https://mydomain.com/somefile.stl"qty = 1materials = api.get_materials()khaki = [m["color"] for m in materials if m["color"] == "Khaki"]material_id = khaki["id"]quote = api.upload_and_quote_model(file_url, qty, material_id, shipping_info)print "Price is $%.2f." % (quote["quote"]["total"])purchase = (raw_input("Would you like to complete the purchase? (y/n)").lower() == 'y')if purchase:order = api.confirm_order(quote["quote_id"])print orderprint "Purchase completed."## Classes## API###API(api_key)Creates an API instance that uses the given api key. You should provide a correct API key or the API will not work.###API.get_materials()Returns a list of available materials for printing.Example response:# Truncated example showing the structure of the response[{u'color': u'True Red', u'type': u'PLA', u'id': 1},{u'color': u'True Brown', u'type': u'PLA', u'id': 2},...]# End of response###API.create_model(file_url):Creates a model on the Voodoo servers representing the model provided by file_url. The Voodoo servers will download the file before providing a response.Example response:{u'user_id': 1,u'deletedAt': None,u'file_uri':u'3c911686-4bbe-4fb7-b08e-354e88f1274b.stl',u'volume': 263703.684920517,u'updatedAt':u'2015-06-15T15:23:21.122Z',u'y': 63.9124984741211,u'x': 155.777496337891,u'z': 110.86499786377,u'id': 25,u'createdAt': u'2015-06-15T15:23:21.122Z'}###API.create_order(order_items, shipping_info):Creates an order request on the server and returns a quote for that order, along with a quote id used to confirm the order. The purchase is not completed until the confirm_order method is executed with the appropriate quote_id.Example usage:# Order items is a list of dictionaries with the keys:# { "material": material_id, "id": model_id, "qty": number }order_items = {'material': 34, 'id': 25, 'qty': 1# Shipping info is a dictionary with the keys:shipping_info = {'city': 'Test city,'name': 'Test name','zip': '12345','street1': '123 Test Rd','street2': '#1', # optional!'state': 'AK','country': 'USA'}print API.create_order(order_items, shipping_info)# Response{u'quote':{u'items': 263814.55,u'total': 290196.01,u'tax': 26381.46,u'shipping': 0},u'quote_id': u'123456',u'shipping':{u'city': u'New York',u'name': u'Oliver Ortlieb',u'zip': u'10003',u'street1': u'320 2nd Ave',u'street2': u'#5',u'state': u'NY',u'country': u'USA'},u'order_items': [{u'material': 34, u'id': 25, u'qty': 1}]}###API.confirm_order(quote_id):Confirms the order given by quote_id. Prior to calling this method, no order will be printed and no money will be charged.Response is the same as create_order.###API.upload_and_quote_model(file_url, qty, material, shipping_info):Creates a model for file_url and quotes it for the given quantity, material, and target shipping address. Returns the same response as create_order.
Package: voodoo-cli
Odoo dockerized.> For beginners: get an odoo instance without effort> For developers: reproductible odoo environmentsHow it worksVoodoo leverage Docker-compose andAnybox’s buildoutfor odoo.In yourhostrunvoodoocommands to bootstrap the project and launch docker.Then in yourguest(container), runakcommands to update odoo dependencies (odoo modules), trigger update scripts and restart odoo server.Automatic DNS resolutionVoodoo includ an automatic dns resolution by integratingresolvable from gliderlabsWhen you run a project in a directory called “my_project” a dns entry will be automatically created and be accessible from the host with my_project.vdGetting StartedREAD the documentation:Voodoo documentation
Package: voodoonet
VoodooNetPredicting liquid droplets in mixed-phase clouds beyond lidar attenuation using artificial neural nets and Doppler cloud radar spectraVOODOO is a machine learning approach based convolutional neural networks (CNN) to relate Doppler spectra morphologies to the presence of (supercooled) liquid cloud droplets in mixed-phase clouds.InstallationPrerequisitesVoodooNet requires Python 3.10.Before installing VoodooNet, install PyTorchaccording to your infrastructure. For example on a Linux machine without GPU you might run:pip3installtorch--extra-index-urlhttps://download.pytorch.org/whl/cpuFrom PyPIpip3installvoodoonetLocally for developmentpip3install-e.[dev]UsageMake predictions using the default model and settingsimportglobimportvoodoonetrpg_files=glob.glob('/path/to/rpg/files/*.LV0')probability_liquid=voodoonet.infer(rpg_files)Generate a training data setDownload some RPG-FMCW-94 raw files and corresponding classification files from theCloudnet data portalAPI. For example, forLeipzig LIMbetween 2021-01-10 and 2021-01-15:curl"https://cloudnet.fmi.fi/api/raw-files?dateFrom=2021-01-10&dateTo=2021-01-15&site=leipzig-lim&instrument=rpg-fmcw-94"|jq'.[]["downloadUrl"]'|xargs-n1curl-O
curl"https://cloudnet.fmi.fi/api/files?dateFrom=2021-01-10&dateTo=2021-01-15&site=leipzig-lim&product=classification"|jq'.[]["downloadUrl"]'|xargs-n1curl-Oimportglobimportvoodoonetrpg_files=glob.glob('*.LV0')classification_files=glob.glob('*classification.nc')voodoonet.generate_training_data(rpg_files,classification_files,'training-data-set.pt')Alternatively, just use N random days:importvoodoonetvoodoonet.generate_training_data_for_cloudnet('leipzig-lim','training-data-set.pt',n_days=5)Train a VoodooNet modelimportvoodoonetpre_computed_training_data_set='training-data-set.pt'voodoonet.train(pre_computed_training_data_set,'trained-model.pt')Make predictions using the new modelimportglobimportvoodoonetfromvoodoonet.utilsimportVoodooOptionsrpg_files=glob.glob('/path/to/rpg/files/*.LV0')options=VoodooOptions(trained_model='new_model.pt')probability_liquid=voodoonet.infer(rpg_files,options=options)
Package: voolu
Voolu CLISimplify terminal access to remote machines using thevoolu network.Create an accountWhile in beta accounts are totally free and unlimited. (Within reason...)vacccreate[?]Chooseausername:username[?]Chooseapassword:********[?]Re-enterpassword:********LoginUse the login command to authenticate your current machine.vacclogin[?]Username:username[?]Password:********

Loggedinandsavedtokenat~/.voolu/tokenConnect hostUse thevhostcommand to register the current machine as a host with a given 'tag'.vhostmy-hostConnect clientsThevshcommand creates an ssh-like session with a given host tag.vshmy-hostContributingThe goal is to build out v___ commands to cover all desired use cases. Modules folder is designed to support an ecosystem of top level commands.SupportFor support, please create an issue or emailsupport@voolu.io.
Package: voprov
A library for IVOA Provenance Data Model supporting PROV-N, PROV-XML, PROV-JSON exportFree software: MIT license (https://opensource.org/licenses/MIT)Documentation: (incoming).FeaturesAn implementation of theIVOA Provenance Data Modelin Python.Serialization support:PROV-N,PROV-XMLandPROV-JSON.Exporting VOPROV documents into various graphical formats (e.g. PDF, PNG, SVG).Convert a VOPROV document to aProv Document.UsesA short tutorial for using this package is available here:https://github.com/mservillat/voprov/blob/master/tutorials/voprov_tutorial.ipynb
Package: vops
VopsThis python project uses data from yahoo finance to graph option profit-loss diagrams.Note: any examples used here are outdated because the option contracts have expired.InstallingInstalling with pip:pipinstallvopsUsageSimple program graphing a long call option:fromvopsimportscrapingfromvopsimportgraphingoptionObj=scraping.scrapeCallOptions('AMD')graphing.graphLongCall(optionObj,'AMD201231C00060000')Graphing both short and long positions on a call option:optionObj=scraping.scrapeCallOptions('AMD')graphing.graphCalls(optionObj,'AMD201231C00060000')Exporting graphs to a png:graphing.graphCalls(optionObj,'AMD201231C00060000',export=True)Output:TodoAdd axis labels to all graphsCreate method for graphing long and short put options simultaneouslyMerge call/put options chainsAllow option chains from different chains to be scraped
Package: vor
What is this?Vör is a set of services for gathering metrics by polling systems and
delivering them to graphite.Currently there is support for polling metrics from Elasticsearch and Redis.RequirementsPython 2.7 or pypy equivalentTwisted 16.0.0 or laterincremental 16.9.0 or latertreq 16.20.0 or later for Elasticsearch supporttxredis for Redis supportpybeanstalk for Beanstalk supportCopyright and WarrantyThe code in this distribution started as an internal tool at Mochi Media and
is made available under the MIT License. See the includedLICENSEfile for details.ContributorsChristopher ZornZack DeverDana PowersAuthorRalph Meijer
<mailto:ralphm@ik.nu>
<xmpp:ralphm@ik.nu>NameIn Norse mythology, Vör is a goddess associated with wisdom. She is described
as “wise and inquiring, so that nothing can be concealed from her”.
Package: vor12
Copyright (c) 2015 Jeremie DECOCK (http://www.jdhp.org)Web site:http://www.jdhp.org/projects_en.html#vor12Online documentation:http://vor12.readthedocs.orgSource code:https://github.com/volab/vor12Issue tracker:https://github.com/volab/vor12/issuesVoR12 on PyPI:https://pypi.python.org/pypi/vor12In collaboration withhttp://www.vorobotics.comDescriptionThe VoR12 project is a mobile video camera.
It uses twoDynamixel AX-12actuators and theOpenCVcomputer vision
library for object tracking experiments.Note:This project is still in beta stage, so the API is not finalized yet.DemosDependenciesPython 3.4OpenCVversions 3.0.0 or abovePyAX-12NumPyNote:If you usepipto install VoR12, PyAX-12 and Numpy will be
automatically downloaded and installed (see the followinginstallsection).OpenCV cannot be installed withpip, thus you have to install it
manually.InstallationGnu/LinuxYou can install, upgrade, uninstall VoR12 with these commands (in a
terminal):pip install --pre vor12
pip install --upgrade vor12
pip uninstall vor12Or, if you have downloaded the VoR12 source code:python3 setup.py installWindowsYou can install, upgrade, uninstall VoR12 with these commands (in acommand prompt):py -m pip install --pre vor12
py -m pip install --upgrade vor12
py -m pip uninstall vor12Or, if you have downloaded the VoR12 source code:py setup.py installMacOSXYou can install, upgrade, uninstall VoR12 with these commands (in a
terminal):pip install --pre vor12
pip install --upgrade vor12
pip uninstall vor12Or, if you have downloaded the VoR12 source code:python3 setup.py installBug reportsTo search for bugs or report them, please use the VoR12 Bug Tracker at:https://github.com/volab/vor12/issuesLicenseTheVoR12library is provided under the terms and conditions of theMIT License.
Package: vorlauf
VorlaufWhat?Vorlauf is a very minimal tool that helps you to create process pipelines (in
the shell sense). It also helps separate out the definition of a Process from
the running of a that process with a given stdin, stdout, and stderr.Why?Because the subprocess api for chaining processes together is cumbersome and
not very well documented.How?This library does basically nothing - it’s implemented in fewer than 100 lines.
There are two classes available,ProcessandPipeline.ProcessTheProcessclass is passed args, cwd, and env, and is executed by callingProcess.runwith optionalstdin,stdout, andstderrparameters.By removing thestdout,stderr, andstdinfrom the creation of theProcessclass, we can create reusableProcessdefinitions:critical_grepper = Process('grep', 'CRITICAL')

syslog = open('/var/log/syslog', 'r')
apachelog = open('/var/log/httpd/error.log', 'r')

filtered = open('critical.log', 'w')

for logfile in (syslog, apachelog):
  critical_grepper.run(stdin=logfile, stdout=filtered)PipelineThePipelineclass stores a list ofProcessclasses which, when run
withPipeline.runwith optionalstdinandstdout, pipes the
processes together. If present,stdinis passed to the first process, and
if present,stdoutis passed to the last process.Examplefrom vorlauf import Pipeline, Process

pipeline = Pipeline()
pipeline.add(Process('cat', 'foo.txt'))
pipeline.add(Process('grep', 'something'))
pipeline.add(Process('uniq'))

with open('new.txt', 'wb') as fd:
    pipeline.run(stdout=fd)And because of operator overloading built into theProcessandPipelineclasses, this can be simplified as:from vorlauf import Process as P

pipeline = P('cat', 'foo.txt') | P('grep', 'something') | P('uniq')
with open('new.txt', 'wb') as fd:
    pipeline.run(stdout=fd)Finally, you could use theProcessclass to create reusable components:from vorlauf import Process

class GPG(Process):

    def __init__(self, passphrase):
        super(GPG, self).__init__('gpg', '-c', '--passphrase', passphrase, '-')


class MySQLDump(Process):

    def __init__(self, password, dbname, **kwargs):
        super(MySQLDump, self).__init__(
            'mysqldump', '-u', 'root', '-p{}'.format(password), dbname
        )


with open('mysql.dump', 'wb') as fd:
    pipeline = MySQLDump('loldongs', 'foo') | GPG('supersekrit')
    pipeline.run(stdout=fd)TestsRun:virtualenv venv
venv/bin/pip install -e .
venv/bin/python test.pyChangelog1.0.0 (2015-09-22)Initial release
Package: voronoi
voronoiIn what followspythonis an alias forpython3.7orpypy3.7or any later version (python3.8,pypy3.8and so on).InstallationInstall the latestpip&setuptoolspackages versionspython-mpipinstall--upgradepipsetuptoolsUserDownload and install the latest stable version fromPyPIrepositorypython-mpipinstall--upgradevoronoiDeveloperDownload the latest version fromGitHubrepositorygitclonehttps://github.com/lycantropos/voronoi.gitcdvoronoiInstallpythonsetup.pyinstallUsage>>>fromvoronoi.diagramimportDiagram>>>diagram=Diagram()>>>diagram.construct([],[])>>>diagram.cells==diagram.edges==diagram.vertices==[]True>>>fromvoronoi.pointimportPoint>>>diagram=Diagram()>>>diagram.construct([Point(0,0),Point(4,0),Point(4,4),Point(0,4)],[])>>>fromvoronoi.facesimportVertex>>>diagram.vertices==[Vertex(2,2)]True>>>fromvoronoi.segmentimportSegment>>>diagram.construct([],[Segment(Point(0,0),Point(4,0)),...Segment(Point(4,4),Point(0,4))])>>>diagram.vertices==[Vertex(2,2),Vertex(0,2),Vertex(4,2)]TrueforCPythonoriginal C++ implementation can be invoked by importing from_voronoimodule instead.DevelopmentBumping versionPreparationInstallbump2version.Pre-releaseChoose which version number category to bump followingsemver
specification.Test bumping versionbump2version--dry-run--verbose$CATEGORYwhere$CATEGORYis the target version number category name, possible
values arepatch/minor/major.Bump versionbump2version--verbose$CATEGORYThis will set version tomajor.minor.patch-alpha.ReleaseTest bumping versionbump2version--dry-run--verbosereleaseBump versionbump2version--verbosereleaseThis will set version tomajor.minor.patch.Running testsInstall dependenciespython-mpipinstall--force-reinstall-rrequirements-tests.txtPlainpytestInsideDockercontainer:withCPythondocker-compose--filedocker-compose.cpython.ymlupwithPyPydocker-compose--filedocker-compose.pypy.ymlupBashscript (e.g. can be used inGithooks):withCPython./run-tests.shor./run-tests.shcpythonwithPyPy./run-tests.shpypyPowerShellscript (e.g. can be used inGithooks):withCPython.\run-tests.ps1or.\run-tests.ps1cpythonwithPyPy.\run-tests.ps1pypy
Package: vorta
Vorta Backup ClientVorta is a backup client for macOS and Linux desktops. It integrates the mightyBorgBackupwith your desktop environment to protect your data from disk failure, ransomware and theft.https://github.com/m3nu/vorta/assets/3916435/a622a148-5373-4ae0-87bc-4ca1d6f6202eWhy is this great? 🤩Encrypted, deduplicated and compressed backupsusingBorgas backend.No vendor lock-in– back up to local drives, your own server orBorgBase, a hosting service for Borg backups.Open source– free to use, modify, improve and audit.Flexible profilesto group source folders, backup destinations and schedules.One placeto view all point-in-time archives and restore individual files.Learn more onVorta's website.InstallationVorta should work on all platforms that support Qt and Borg. This includes macOS, Ubuntu, Debian, Fedora, Arch Linux and many others. Windows is currently not supported by Borg, but this may change in the future.See our website fordownload links and install instructions.Connect and ContributeTo discuss everything around using, improving, packaging and translating Vorta, join thediscussion on Github.Report bugs by opening a newGithub issue.Want to contribute to Vorta? Great! See ourcontributor guideon how to help out with coding, translation and packaging.We currently have students from the Google Summer Of Code 2023 Program contributing to this project.License and CreditsSeeCONTRIBUTORS.mdto see who programmed and translated Vorta.Licensed underGPLv3. © 2018-2023 Manuel Riel and Vorta contributorsBased onPyQtandQt.Icons byFork Awesome(licensed underSIL Open Font License, Version 1.1) and Material Design icons by Google (licensed underApache License 2.0). See thesrc/vorta/assets/iconsfolder for a copy of applicable licenses.
Package: vortex
This is an API to interface with an idea appraisal tool. This statisitcally predicts the quality of an idea that you may pursue.The API usage is as follows:from vortex.app import predict


print(predict("A hackathon application that uses the Twilio API in a multiplayer fashion and plays Pokemon."))You will recieve a scalar number from this function.Feel free to e-mail me atmatkallada@gmail.com.
Package: vortex-api
Vortex API Python ClientInstallationpip install vortex-apiApi Usagefromvortex_apiimportAsthaTradeVortexAPIfromvortex_apiimportConstantsasVcclient=AsthaTradeVortexAPI("your api secret","your application id")#For client login using TOTPclient.login("client code","client password","totp")# Place orderclient.place_order(exchange=Vc.ExchangeTypes.NSE_EQUITY,token=22,transaction_type=Vc.TransactionSides.BUY,product=Vc.ProductTypes.DELIVERY,variety=Vc.VarietyTypes.REGULAR_LIMIT_ORDER,quantity=1,price=1700.0,trigger_price=0.0,disclosed_quantity=0,validity=Vc.ValidityTypes.FULL_DAY)#Get order bookclient.orders(limit=20,offset=1)Connecting to websocketUsing the feed, you can listen to both price quote changes and order/trade updates. You need to define your own callbacks foron_price_updateandon_order_update. The packet structure foron_order_updateis the same as that received in postbacks and is availableherefromvortex_apiimportVortexFeedfromvortex_apiimportConstantsasVcimporttimedefmain():# Get access token from any of the login methodswire=VortexFeed(access_token)wire.on_price_update=on_price_updatewire.on_order_update=on_order_updatewire.on_connect=on_connectwire.connect(threaded=True)# If you make threaded = False, anything after this line will not executetime.sleep(10)wire.unsubscribe(Vc.ExchangeTypes.NSE_EQUITY,26000)wire.unsubscribe(Vc.ExchangeTypes.NSE_EQUITY,26009)wire.unsubscribe(Vc.ExchangeTypes.NSE_EQUITY,2885)defon_price_update(ws,data):print(data)defon_order_update(ws,data):print(data)defon_connect(ws,response):ws.subscribe(Vc.ExchangeTypes.NSE_EQUITY,26000,Vc.QuoteModes.LTP)#Subscribe to NIFTYws.subscribe(Vc.ExchangeTypes.NSE_EQUITY,26009,Vc.QuoteModes.OHLCV)# Subscribe to BANKNIFTYws.subscribe(Vc.ExchangeTypes.NSE_EQUITY,2885,Vc.QuoteModes.FULL)# Subscribe to RELIANCEif__name__=="__main__":main()Refer to thepython documentfor all methods and features
Package: vortexasdk
VortexaSDKWelcome to Vortexa's Python Software Development Kit (SDK)! We built the SDK to
provide fast, interactive, programmatic exploration of our data. The tool lets
Data Scientists, Analysts and Developers efficiently explore the world’s
waterborne oil movements, and to build custom models & reports with minimum
setup cost.The SDK sits as a thin python wrapper aroundVortexa's API, giving you immediate access to pandas
DataFrames.ExampleIn an interactive Python console, run:>>>fromdatetimeimportdatetime>>>fromvortexasdkimportCargoMovements>>>df=CargoMovements()\.search(filter_activity='loading_state',filter_time_min=datetime(2017,8,2),filter_time_max=datetime(2017,8,3))\.to_df()returns:quantityvessels.0.nameproduct.group.labelproduct.grade.labelevents.cargo_port_load_event.0.end_timestampevents.cargo_port_unload_event.0.start_timestamp01998ALSIA SWANClean productsLube Oils2017-08-01T06:10:45+00002017-08-27T14:38:15+0000116559IVERDirty productsnan2017-08-02T17:20:51+00002017-09-07T07:52:20+00002522288BLUE SUNCrudeGharib2017-08-02T04:22:09+00002017-08-13T10:32:09+0000Quick StartTry me out in your browser:Installation$pipinstallvortexasdkor$pip3installvortexasdkThe SDK requires Python versions between 3.7 and 3.10. SeeSetup FAQfor more details.To install the SDK on an Apple ARM-based machine, use Python versions between 3.7 and 3.10 and use the latest version of pip. This is supported in the SDK versions 0.41.0 or higher.AuthenticationSet yourVORTEXA_API_KEYenvironment variable, that's all. Alternatively, the
SDK prompts to you enter your API Key when running a script interactively.To get an API key and experiment with Vortexa's data, you canrequest a demo here.Check SetupTo check the SDK is setup correctly, run the following in a bash console:$python-mvortexasdk.check_setupA successful setup looks like this:Next StepsLearn how to callEndpointsGlossaryThe Glossary can be found atVortexa API Documentation. The Glossary outlines key
terms, functions and assumptions aimed at helping to extract powerful findings
from our data.DocumentationRead the documentation atVortexaSDK DocsContributingWe welcome contributions! Please read ourContributing Guidefor ways to offer feedback and contributions.Thanks goes to these wonderful contributors
(emoji key):Kit Burgess🎨💻tinovs💻👀David Andrew Starkey💻📖💡syed👀Jakub Korzeniowski🤔Edward Wright📓Patrick Roddy📓Romain📓🤔Natday💼🤔📓ArthurD1📓Chloe Connor📓Achilleas Sfakianakis📓Sean Barry💻📖Konrad Moskal💻Pawel Pietruszka💻Olly De Sousa💻This project follows theall-contributorsspecification. Contributions of any kind welcome!
Package: vortexfitting
VortexFittingTools for detection, identification and fitting of vortices based on the
non-linear fitting of the Lamb-Oseen vortex model.Documentationhttps://guilindner.github.io/VortexFitting/This project was started by Guilherme Lindner as a Master 1 project of theInternational Master in Turbulence, at Ecole Centrale de Lille, under the supervision of Jean-Philippe Laval, Jean Marc Foucaut and Ilkay Solak.
The code was validated and improved by Dr. Yann Devaux at Institute Pprime, Poitiers, France.