Saravanan666/pypi-org huggingface dataset:
Package: vancalculator
vancalculator Library PackageThis project is developed to fullfil the requirement of Udacity AWS Machine Learning Foundations Course.This is an Object Oriented Programming Project developed in Python build for the calculation of mode, median, Gaussian and binomial distribution.It is able to be downloaded at PyPi: vancalculatorSearch in Link:PyPi WebsiteExact Package Link:vancalculator LibraryOrpip install vancalculatorGithub Link:vancalculator-PackageThe package is able to calculate:GeneralmodemedianVersion: 0.0.2.1Gaussianmeanstandard deviationGaussian Graph VisualizationPDF Graph viewAdd two Gaussians togetherDisplay characteristics of GaussianBinomialmeanstandard deviationBinomial Graph VisualizationPDF Graph viewAdd two Binomials togetherDisplay characteristics of BinomialUsage:importvancalculator
Package: vancouver-food
Vancouver food LibraryVancouver is quite a food city, with an abundance of Asian-inspired flavors, seafood of the Pacific Northwest, and Canadian specialties. If you're not sure what to eat, let us choose for you!You can find usage examples for the library inAPI referenceInstallationTo start, ensure you have Python 3.7.1 or newer. If you just
want to use the package, run:pip3install--upgradevancouver-foodAfter you have installed the package, import it at the top of a file:importvancouver_foodprint(vancouver_food.random_pick())
Package: vander
# vanderRun a command, and live-graph the first integer in its output, right in your terminal.## Usagevander [-t <how often command should run>] [-c <color>] commandHow often the command should run can be either an integer or a float, e.g., 3 and 0.7 are both valid. See the standard foreground/background colors [here](http://urwid.org/manual/displayattributes.html) for the valid arguments for the color.## Examplesvander cat /sys/devices/system/cpu/cpu0/cpufreq/scaling_cur_freqvander python -c “import random; print random.randint(0, 100)”## TodoNothing?
Package: vandermonde
vandermondevandermonde is a module with a handful of tools for working withVandermonde
matrices.
In particular, theBjörck-Pereyra algorithmfor solving systems with the Vandermonde matrix or its transposed is
implemented.Example:importnumpyasnpimportvandermondex=np.linspace(0.0,1.0,14)b=np.random.rand(len(x))sol=vandermonde.solve(x,b)Installationvandermonde isavailable from the Python Package
Index, so
simply dopip install -U vandermondeto install/update.Licensevandermonde is published under theMIT license.
Package: vanellope
vanellope is a publishing engine (just a blog software) named after the adorable Disney characterVanellope von Schweetz. It it is designed to be deployed with a minimal effort.DEMOpipinstallvanellopevanellope--port=8000There it is ! A brand new app is running on port 8000. You can run on port 80 or put it behind a reverse proxy like Nginx. In either way you work here is done.REMEMBER, after vanellope started, it will look for acontentdirectory in the current location. If the directory not exist it will create one and put every thing it generated in it.And one more thing, a newly created vanellope instance is completely vulnerable like every new born baby. You should visit/welcomepage signup as administrator immediately after the instance start running. The/welcomepage can be used only once.Section title.
Package: vangap-meliora1
About the packagemeliorais a Python package that provides a set of statistical tests and tools to assess the performance of the credit risk models. All tests are covered with unit tests and algorithms have been replicated in other tools like R, MATLAB and SAS to avoid errors. Whenever possible, the definition of the test was retrieved from the authoritive source like the EBA, the ECB or the Basel Committee.The main contributors started building their first statistical credit models back in 2003. Over the years, we have impemented similar set of tests in several different financial institutions.This package is standing on the shoulders of giants as it makes heavy use of the Python
ecosystem and especially Scikit-learn, Scipy and Statsmodels. Several functions are straightforward
wrappers using these resources and are provided to the user for convenience purposes. The authors
have taken great care to ensure that no part of this package contains proprietary code.Main aimThe aim of the package is to provide all common tests used by today's modellers when developing, maintaining and validating their PD, LGD, EAD and prepayment models. The aim of this package is to provide credit risk practioners with the tools to develop their credit risk models without reinventing the wheel.Main Featurestests cover both IFRS 9 and IRB models as well as non-regulatory modelsthe tool contains more than 30 testsall test have been covered with unit teststhe tests have been documented in detailcommonly accepted tresholds have been provided for convenience purposesFor the list of all tests, see Overview > List of testsTests that are currently included in the package#NameAreaEstimate1Binomial testCalibrationPD2Chi-Square test (Hoshmer-Lemeshow test)CalibrationPD3Normal testCalibrationPD4Traffic lights approachCalibrationPD5Spiegehalter testCalibrationPD6Redelmeier testCalibrationPD7Herfhindahl index / Concentration of rating gradesConcentrationPD8Brier scoreDiscriminationPD9Receiver Operating CharacteristicDiscriminationPD10Accuracy RatioDiscriminationPD11Kendall’s τDiscriminationPD12Somers’ DDiscriminationPD13The Pietra IndexDiscriminationPD14Conditional Information Entropy RatioDiscriminationPD15Kullback-Leibler distanceDiscriminationPD16Information valueDiscriminationPD17Bayesian error rateDiscriminationPD18Cumulative LGD accuracy ratioDiscriminationLGD19Loss Capture RatioDiscriminationLGD20Kolmogorov-Smirnov testDiscriminationPD21Spearman’s rank correlationDiscriminationLGD22Jeffrey's testDiscriminationPD23ELBE back-test using a t-testDiscriminationLGD24Migration matrices testDiscriminationPD25Loss ShortfallPredictive powerLGD26Mean Absolute DeviationPredictive powerLGD27Bucket testPredictive powerLGD28Transition matrix testPredictive powerLGD29Population Stability IndexStabilityPD30Stability of transition matricesStabilityPDFull list of dependenciesNumPy (https://www.numpy.org)Pandas (https://pandas.pydata.org/)Statsmodels (https://www.statsmodels.org/)Scikit-learn (https://scikit-learn.org/)Scipy (https://scipy.org/)Getting HelpFor usage questions, send an email toanton.treialt@aistat.comLicenseMIT License
Package: vanguard
Vanguard is a simple API authentication library for Django REST FrameworkQuick start-----------0. Install `vanguard` using `pip``pip install vanguard`1. Add "vanguard" to your INSTALLED_APPS setting like this::```INSTALLED_APPS = [...'rest_framework','vanguard',...]```2. Include the vanguard URLconf in your project urls.py like this::```from django.conf.urls import include:url(r'^vanguard/', include('vanguard.urls')),```3. Run `python manage.py migrate` to create the vanguard models.4. Available vanguard endpoints```Signup [vanguard/, vanguard/signup]Login [vanguard/login]Password Retieval [vanguard/forgotpassword]Logout [vanguard/logout]```5. Use @validate_token as the inner most annotation for any API end point methodyou wanna authenticate```from rest_framework.decorators import api_view, renderer_classesfrom rest_framework.renderers import JSONRenderer, BrowsableAPIRenderer, statusfrom rest_framework.response import Response:from vanguard.utils import validate_token:.@api_view(['GET', 'POST'])@renderer_classes((JSONRenderer, BrowsableAPIRenderer,))@validate_tokendef my_api(request):#On successful authentication, HTTP_TOKEN's value will be replaced by#authenticated user's emailuser_email=request.META['HTTP_TOKEN']:return Response({'Authenticated User': request.META['HTTP_TOKEN']}, status=status.HTTP_200_OK)```
Package: vanidl
VaniDLVaniDL is an tool for analyzing I/O patterns and behavior with Deep Learning Applications. It analyzes Darshan Extended traces to extract various I/O patterns with Deep Learning applications. The tool at it core uses vanidl which converts Darshan Profiler’s trace into knowledge for analysis. It is designed to provide low-level I/O behavior details to tensorflow applications in HPC.VaniDL features include:Easy-to-use and understand high-level API for extracting I/O behavior of the applications.Fast prototyping through highly modular data representation through pandas for easy plotting of graphs.Full transparency over profiling data with access to internal data structures such as timeline of applications, aggregation functions, and drill up/down data views.Powerful helper functions to build a visual understanding of how applications perform I/O such as request distributions, file access pattern, and extracting file specific summaries.Easy to use File Summary and Job Summary extractors for understanding the data consumed by Deep Learning ApplicationsOverview#Initialize classprofile=DLProfile()#Load darshan filestatus=profile.Load("./run1.darshan")#Get Job Summarysummary=profile.GetSummary()#Application Timeline of data operationstl=profile.CreateIOTimeline()plt.figure(figsize=(20,4))plt.grid()plt.plot(tl['time_step'],tl['operation_count']);More examples arehereInstallationRequirementsnumpy==1.18.5pandas==1.0.4h5py==2.10.0tensorflow~=2.2.0VaniDL InstallationTo install VaniDL, the easiest way is to runFor the bleeding edge version (recommended):pipinstallgit+https://github.com/hariharan-devarajan/vanidl.gitFor the latest stable version:pipinstallvanidlOtherwise, you can also install from source by running (from source folder):pythonsetup.pyinstallOn ThetamoduleloadVaniDLGetting StartedSeeGetting Started with VaniDLto learn about VaniDL basic functionalities or start browsingTFLearn APIs.ExamplesThere are many examples of analysis available, seeExamples.ContributionsThis is the first release of VaniDL, if you find any bug, please report it in the GitHub issues section.Improvements and requests for new features are more than welcome! Do not hesitate to twist and tweak VaniDL, and send pull-requests.For more info:Contribute to VaniDL.LicenseMIT License
Package: vanilla
If Go and ZeroMQ had a baby, and that baby grew up and started dating PyPy,
and they had a baby, it might look like Vanilla.OverviewVanilla allows you to build concurrent software in Python. Vanilla programs are
structured around independent coroutines (greenlets) which communicate with
each other via Pipes. Pipes are similar to channels in Go programming.There’s no callback crazyness and no monkey patching. Vanilla strives to be as
explict and straightforward as possible.DocumentationRead the DocsHere’s how it looks:You spawn coroutines:h=vanilla.Hub()defbeat(message):whileTrue:print(message)h.sleep(1000)h.spawn(beat,'Tick')h.spawn_later(500,beat,'Tock')# Tick / Tock / Tick / TockCoroutines communicate via Pipes:h=vanilla.Hub()sender,recver=h.pipe()h.spawn(sender.send,'Hello World')recver.recv()# 'Hello World'Pipe-fu; inspired by reactive functional patterns, Pipes can be chained:h=vanilla.Hub()p=h.pipe().map(lambdax:x*2)h.spawn(p.send,4)p.recv()# 8In Vanilla, everything is a Pipe. Here’s how TCP looks:h=vanilla.Hub()server=h.tcp.listen(port=9000)# server is a Recver which dispenses new TCP connectionsconn=server.recv()# conn is a Pipe you can recv and send onmessage=conn.recv()conn.send("Echo: "+message)InstallationVanilla works with Python 2.6 - 2.9 and PyPy.pip install vanillaStatus
Package: vanilla.bean
Obligatory Sinatra style (but concurrent!) micro-web framework forVanilla.Exampleh=vanilla.Hub()b=h.bean(port=8000)@b.get('/')defindex(request):response=request.response()response.send('Hello ')h.sleep(1000)response.send('World\n')Websocketsh=vanilla.Hub()b=h.bean(port=8000)@b.websocket('/echo')defecho(ws):whileTrue:ws.send(ws.recv())Installationpip install vanilla.bean
Package: vanilla.consul
This is an adaptor/plugin forVanillabased on thepython-consullibrary.Usage is the same as thestandard APIexcept that all API calls return a
Vanilla pipe, which can berecv’ed on to receive the Consul response.Exampleh=vanilla.Hub()c=h.consul()classConfig(object):passconfig=Config()@h.spawndefmonitor():# register our servicec.agent.service.register('foo',service_id='foo:1',ttl='10s').recv()@h.spawndefkeepalive():whileTrue:# ping our service's health check every 5sc.health.check.ttl_pass('service:foo:1').recv()h.sleep(5000)# maintain our internal configuration state with all available nodes# providing the foo serviceindex=NonewhileTrue:index,nodes=c.health.service('foo',index=index,passing=True).recv()config.nodes=[node['Service']['ID']fornodeinnodes]# make use of config.nodesInstallationpip install vanilla.consul
Package: vanilla-option-pricing
Vanilla Option PricingA Python package implementing stochastic models to price financial options.The theoretical background and a comprehensive explanation of models and their parameters
can be found is the paperFast calibration of two-factor models for energy option pricingby Emanuele Fabbiani, Andrea Marziali and Giuseppe De Nicolao, freely available on arXiv.InstallingThe preferred way to install the package is using pip,
but you can also download the code and install from sourceTo install the package using pip:pipinstallvanilla_option_pricingQuickstartLet's create a call option.fromdatetimeimportdatetime,timedeltafromvanilla_option_pricing.optionimportVanillaOptionoption=VanillaOption(spot=100,strike=101,dividend=0,date=datetime.today(),maturity=datetime.today()+timedelta(days=30),option_type='c',price=1,instrument='TTF')We can compute the implied volatility and create a Geometric Brownian Motion
model with it. Of course, if now we ask price the option using the Black framework,
we'll get back the initial price.fromvanilla_option_pricing.modelsimportGeometricBrownianMotionvolatility=option.implied_volatility_of_undiscounted_pricegbm_model=GeometricBrownianMotion(volatility)gbm_price=gbm_model.price_option_black(option)print(f'Actual price:{option.price}, model price:{gbm_price}')But, if we adopt a different model, say a Log-spot price mean reverting to
generalised Wiener process model (MLR-GW), we will get a different price.importnumpyasnpfromvanilla_option_pricing.modelsimportLogMeanRevertingToGeneralisedWienerProcessp_0=np.eye(2)model=LogMeanRevertingToGeneralisedWienerProcess(p_0,1,1,1)lmrgw_price=model.price_option_black(option)print(f'Actual price:{option.price}, model price:{lmrgw_price}')In the previous snippet, the parameters of the LMR-GW model were chosen
at random. We can also calibrate the parameters of a model against
listed options.fromdatetimeimportdatefromvanilla_option_pricing.optionimportVanillaOptionfromvanilla_option_pricing.modelsimportOrnsteinUhlenbeck,GeometricBrownianMotionfromvanilla_option_pricing.calibrationimportModelCalibrationdata_set=[VanillaOption('TTF','c',date(2018,1,1),2,101,100,date(2018,2,1)),VanillaOption('TTF','p',date(2018,1,1),2,98,100,date(2018,2,1)),VanillaOption('TTF','c',date(2018,1,1),5,101,100,date(2018,5,31))]models=[GeometricBrownianMotion(0.2),OrnsteinUhlenbeck(p_0=0,l=100,s=2)]calibration=ModelCalibration(data_set)print(f'Implied volatilities:{[o.implied_volatility_of_undiscounted_priceforoindata_set]}\n')formodelinmodels:result,trained_model=calibration.calibrate_model(model)print('Optimization results:')print(result)print(f'Calibrated parameters:{trained_model.parameters}\n\n')
Package: vanilla-pkg
Vanilla packagePyPIThis is a simple class which takes borrows heavily from the examples inthe docspackage, built process outlinedpython'sofficialpackaging project tutorial.TestingUnit tests reside in thetestsubdirectory of the vanilla_pkg directory.Tests can be run with:pytest -v --cov=vanilla_pkgAnaconda packageAfterbuilding a conda package, it can be published by following theuser guide for uploading packages.
The user guide refers to a meta.yaml file but provides no examples. Fortunately, there is anexample package in the conda demo.
Package: vanilla-roll
vanilla-rollvanilla-roll is volume renderer using array-api as backend.Why vanilla-roll ?VTKis one of the most excellent volume renderers in the world.
It has good performance and many features.
But I think that the installation process is not easy.
So vanilla-roll is motivated to solve this problem.FeaturesIOMRANIFTIDICOMNRRDRendering AlgorithmSamplingShear-WarpRaycastRendering ModeMIPMinPAverageVolumeRenderingAmbientShadingBackendnumpypytorchcupyjaxnumbdaInstallation$pipinstallvanilla-rollvanilla-roll supports following extrastorchdicommhaniftiExampleCodeimporturllib.requestfrompathlibimportPathfromtempfileimportTemporaryDirectoryimportnumpyasnpimportskimage.ioimportvanilla_rollasvr# from A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie# https://www.openfmri.org/dataset/ds000113/MRA_FILE_URL="https://s3.amazonaws.com/openneuro/ds000113/ds000113_unrevisioned/uncompressed/sub003/angio/angio001.nii.gz"# noqa: E501deffetch_mra_volume()->vr.volume.Volume:withTemporaryDirectory()astmpdir:mra_file=Path(tmpdir)/"mra.nii.gz"urllib.request.urlretrieve(MRA_FILE_URL,mra_file)returnvr.io.read_nifti(mra_file)defsave_result(ret:vr.rendering.types.RenderingResult,path:str):img_array=vr.rendering.convert_image_to_array(ret.image)skimage.io.imsave(path,np.from_dlpack(img_array))# type: ignoredefmain():volume=fetch_mra_volume()ret=vr.render(volume,mode=vr.rendering.mode.MIP())save_result(ret,f"result.png")if__name__=="__main__":main()OutputIf you need more exmplaes, please check theexamples.For developmentInstall Poery plugins$poetryselfadd'poethepoet[poetry_plugin]'Install all extra packages$poetrypoeinstall-all-extrasRun tests$poetrypoetestRun linter and formatter$poetrypoecheckSee AlsoLicenseApache-2.0
Package: vanilla-transformer-jax
Vanilla TransformerJAX/Flax implimentation of 'Attention Is All You Need' by Vaswani et al. (https://arxiv.org/abs/1706.03762)InstallationUse the package managerpipto install the package in the following way:pipinstallvanilla-transformer-jaxUsageTo use the entire Transformer model (encoder and decoder), you can use the following way:fromjaximportrandomfromvtransformerimportTransformer# imports Transformer classmodel=Transformer()# model hyperparameters can be tuned, otherwise defualts mentioned in paper shall be usedprng=random.PRNGKey(42)example_input_src=jax.random.randint(prng,(3,4),minval=0,maxval=10000)example_input_trg=jax.random.randint(prng,(3,5),minval=0,maxval=10000)mask=jax.array([1,1,1,0,0])init=model.init(prng,example_input_src,example_input_trg,mask)#initializing the params of modeloutput=model.apply(init,example_input_src,example_input_trg,mask)# getting outputTo use Encoder and Decoder seperately, you can do so in the following way:encoding=model.encoder(init,example_input_src)#using only the encoderdecoding=model.decoder(init,example_input_trg,encoding,mask)#using only the decoderContributingThis library is not perfect and can be improved in quite a few factors.Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: vanity-cosmos
vanity-cosmosCLI tool for creatingCosmosvanity addressesFeaturesGenerate Cosmos bech32 vanity addressesSpecify a substring that the addresses muststart withend withcontainsSet minimum amount of letters (a-z) or digits (0-9) required in an addressSet the number of addresses to generateUses all CPU coresHow to useLook for an address that starts with "00000" (e.g. cosmos100000v3fpv4qg2a9ea6sj70gykxpt63wgjen2p)python -m vanitycosmos --startswith 00000Look for an address that ends with "8888" (e.g. cosmos134dck5uddzjure8pyprmmqat96k3jlypn28888)python -m vanitycosmos --endswith 8888Look for an address containing the substring "mystring" (e.g. cosmos1z39wmystringah22s5a3pyswtnjkx2w0hvn3rv)python -m vanitycosmos --contains mystringLook for an address consisting of letters only (e.g. cosmos1rfqkejeaxlxwtjxucnrathlzgnvgcgldzmuxxe)python -m vanitycosmos --letters 38Look for an address with at least 26 digits (e.g. cosmos1r573c4086585u084926726x535y3k2ktxpr88l)python -m vanitycosmos --digits 26Generate 5 addresses (the default is 1)python -m vanitycosmos --n 5Combine flags introduced abovepython -m vanitycosmos --startswith 00000 --endswith 8888 --n 5
Package: vank
Vank一个基于Python的Web框架特性高度个性化: 你可以构建许多自定义的组件添加到配置当中、在恰当的时机使用他们。例如自定义路由转换器、错误处理器等。低学习成本: 这是一个非常简单的框架，你可以在仅仅几个小时内学习完并熟练使用它。完备的文档: 具有非常详细的文档供开发者们学习，并且了解框架是如何运行的。快速开始安装Vankpipinstallvank初始化项目python-mvankinit-nmysite运行项目cdmysite
pythonmain.py技术文档
Package: vankrupt-workshop-client
Vankrupt workshop clientsHere you'll find reference implementations of clients
for interacting with vankrupt workshop APIs.The library comes with a relatively thin API and CLI clients for
both uploading mods, and downloading/upgrading them.API libraryAPI client/libraryreadmewill be most helpful for you, if you are looking for references
on how to implement uploader/downloader clients.
The documentation on how exactly to process downloads, updates,
perform uploads is there as well.CLI clientCLI clientreadmeis a good reference on how exactly to use API clients and tie them up
to a concrete environment.General info / statusIt works against the environment you choose, both cli clients can be configured
to work withdev / prod / localenvironments.Currently it works against dev by default. When prod gets pulished, will
work with that.InstallationThe library is published to PyPi under the namevankrupt-workshop-client.Thus, to use it locally all you'd need to do is install it.
I'd recommend to do it the following way:Create python virtual env:python3-mvenvvenvActivate it:sourcevenv/bin/activateInstall:pipinstallvankrupt-workshop-clientUse however you'd like, i.e.:vankrupt_downloadfilter-mods--page1--platformps5For usage details, refer to
CLI clientreadme.Quickstart: runDownloadvankrupt_download--helpUploadvankrupt_upload--help
Package: van.potomo
ContentsPurposeUsageWhyCaveatsChanges2.1 (2013-08-23)2.0 (2013-08-15)1.0.0 (2009-03-10)PurposeThis package integrates pure-python gettext translation compilation (msgfmt)
into the setup.py “build” and “develop” commands.These commands are extended so that every “data file” identified by setuptools
with the .po extension is compiled to a .mo file in the same directory as the
.po file.This integrates very well if you use z3c.recipe.i18n (and hopefully others as
well) to manage .po files.UsageYou need to override the “build” and “develop” commands in your setup.py, for example:from setuptools import setup, find_packages
from van.potomo import develop, build

setup(
    name = "HelloWorld",
    cmdclass={'build': build,
              'develop': develop},
    setup_requires=["van.potomo"],
    version = "0.1",
    packages = find_packages(),
)WhyThis package exists because the author believes that:Compiled translations should not be stored under revision control, they are not sourceCompiled translations should not be distributed in tarballs, they are not sourceCompiled translations should not be “lazily compiled” unless you take into account that
filesystems on production machines are often read-only.The process of compiling translations should not require an extra step/options during
the build/install or build/develop cycle.The options available at the time offered all of this:Babel: Not integrated into the “build” distutils command.cc.gettext: Depends on buildout, not useful for deployments by other means.zope.i18n lazy compilation: Very nice for development, but not on some
production environments.Caveatsvan.potomo makes your setup.py depend on van.potomo. But outside tools cannot
tell this because they need to run the setup.py to figure out the dependencies.Basically this means your developers/users will need to install van.potomo
manually before anything else. If anyone knows a robust/good way to get around
this limitation, please let the author know.Changes2.1 (2013-08-23)run egg_info before develop to make sure SOURCES.txt exists and is up
to date2.0 (2013-08-15)Conform to Zope repository policy.Support Python 3Fix issue where recently deleted files could cause an exception if
they were still in SOURCES.txt1.0.0 (2009-03-10)Initial Release