Saravanan666/pypi-org huggingface dataset:
Package: probaforms
Welcome to probaformsProbaformsis a python library of conditional Generative Adversarial Networks, Normalizing Flows, Variational Autoencoders and other generative models for tabular data. All models have a sklearn-like interface to enable rapid use in a variety of science and engineering applications.Implemented conditional modelsVariational Autoencoder (CVAE)Wasserstein GAN (WGAN)Real NVPInstallationpip install probaformsorgitclonehttps://github.com/HSE-LAMBDA/probaforms.gitcdprobaformspipinstall-e.orpoetry installBasic usage(See more examples in thedocumentation.)The following code snippet generates a noisy synthetic data, fits a conditional generative model, sample new objects, and displays the results.fromsklearn.datasetsimportmake_moonsimportmatplotlib.pyplotaspltfromprobaforms.modelsimportRealNVP# generate sample X with conditions CX,y=make_moons(n_samples=1000,noise=0.1)C=y.reshape(-1,1)# fit nomalizing flow modelmodel=RealNVP(lr=0.01,n_epochs=100)model.fit(X,C)# sample new objectsX_gen=model.sample(C)# display the resultsplt.scatter(X_gen[y==0,0],X_gen[y==0,1])plt.scatter(X_gen[y==1,0],X_gen[y==1,1])plt.show()SupportHome:https://github.com/HSE-LAMBDA/probaformsDocumentation:https://hse-lambda.github.io/probaformsFor any usage questions, suggestions and bugs use theissue page, please.Thanks to all our contributors
Package: probart
# Probart
Probart is a helper library I use in my generative art experiments.It sits on top of PyCairo and NumPy. Nothing beneficial, only a
draft. Right now, you could draw just multi-line paths with it:from probart.canvas import EntropyCanvas

width = 1920
height = 1080
background_color = (0, 0, 0, 0)
canvas = EntropyCanvas(width, height, background_color)

path = [(0, 0), (1, 1)]
start_color = (0.7, 0, 1, 0.1)
end_color = (1, 1, 1, 0.1)
num_clones = 100
line_width = 1 / width
main_deviation = 0.01
sub_deviation = 0.005
canvas.multiline(path, start_color, end_color, num_clones, line_width, main_deviation, sub_deviation)
Package: probat
ProBatBattery life saver and reminderParaphernalia:Terminator,Oh-my-zshwithbullet-train theme,zsh-syntax-highlightingandzsh-autosuggestionsGetting Started:Current requirements are:python-termcolor: colorizing the shell on unixInstall:# Via PIP:
$ pip install probat


# From Arch AUR repo:
$ ... -S python-probatRun:$ probatPro tip:# Add this line to your .bashrc so you will see the battery status at login:
...
# Run `probat` if it's found in $PATH
command -v probat &>/dev/null && probat
...ContributingThis project welcomes with open arms any intent to contribute in any way :)Please leave feedback and ideas on theissuespageEasily set up the project locally for development/testing purposes:$ git clone https://github.com/codeswhite/probat
$ cd ./probat
$ pipenv installNote that installing the python requirements can bealternativelydone via basicpip:$ pip install -r requirements.txt
Package: probator
ProbatorA security and compliance tool for validating your infrastructure. For full documentation please head over tohttps://probator.gitlab.io/DockerUsing the providedDockerfileit is trivial to build a container that contains everyting
necessary to run a the backend, scheduler and worker processes.Build ArgumentsThe Dockerfile currently supports 3 arguments as--build-argvariablesNameDescriptionpip_flagsUsed to pass any extra configuration arguments to the pip install command. An example of this could be to pass in the--extra-index-urlargument for loading extra packages from a private PyPi repositoryextra_packagesAny extra python packages you would like to install, such as custom pluginstimezoneThe timezone to set in the container, used for the schedulers. Defaults toUTCLimitationsThe container does not come with a database, that will need to be provided externally. You
will also need to map in a volume containing the configuration filesExamplesBuild with custom plugin from private repositoryThis example uses thepip_flagsandextra_packagesto install a custom pluginprobator-auth-openiddocker build . -t probator:latest --build-arg pip_flags="--extra-index-url https://pypi.company.tld/simple/" --build-arg extra_packages="probator-auth-openid"Example executionTo run any part of the system you will need to pass in the configuration folder with the required files (config.json,logging.jsonandssl/private.key) present to/usr/local/etc/probatorinside the container.The example below will execute the schedulerdocker run --rm -v probator-config:/usr/local/etc/probator probator:latest probator schedulerThe next example shows how to run the API server with a port mapping for a production API serverdocker run --rm -p 5000:5000 -v probator-config:/usr/local/etc/probator probator:latest gunicorn -w 6 -b 0.0.0.0:5000 -k gthread -t 60 "probator.wsgi:run()"CreditsBased on the work by Riot Games'Cloud Inquisitor
Package: probator-auditor-cloudtrail
probator-auditor-cloudtrailPlease open issues in theProbatorrepositoryDescriptionThis auditor ensures that CloudTrail:Is enabled globally on multi-regionLogs to a central locationHas SNS/SQS notifications enabled and being sent to the correct queuesRegional trails (of our chosen name) are not enabledConfiguration OptionsOption nameDefault ValueTypeDescriptionbucket_nameNonestringName of the S3 bucket to send CloudTrail logs tobucket_regionus-west-2stringRegion to create S3 bucket incloudtrail_regionus-west-2stringRegion to create CloudTrail inenabledFalseboolEnable the CloudTrail auditorinterval60intRun frequency in minutesresource_tagsNonelistList of tags, inkey=valueformat. Empty value disabled tag managements3_archive_days31intDays after which files go to cold storage. Empty or0value to disables3_kms_key_idNonestringKMS Key ID for S3 SSE encryption. If empty, uses the default AWS KMS Keysns_topic_nameNonestringSNS topic name for CloudTrail log deliverysqs_queue_arnNonestringARN of the SQS queue receiving log notificationstrail_nameus-west-2stringName of the trail to createBased on the work by Riot Games'Cloud Inquisitor
Package: probator-auditor-domain-hijacking
probator-auditor-domain-hijackingPlease open issues in theProbatorrepositoryDescriptionThis auditor Checks DNS resource records for any pointers to non-existing
assets in AWS (S3 buckets, Elastic Beanstalks, etc) and alerts to indicate
there is a vulnerability.Configuration OptionsOption nameDefault ValueTypeDescriptionenabledFalseboolEnable the Domain Hijacking auditorinterval30intHow often the auditor runs, in minutesemail_recipients['changeme@domain.tld']arrayList of emails to receive alertshijack_subjectPotential hijack detectedstringEmail subject for domain hijack notifications
Package: probator-auditor-ebs
probator-auditor-ebsPlease open issues in theProbatorrepositoryDescriptionThis auditor validates that EBS volumes are tagged and can be configured to take corrective action, if required.Configuration OptionsOption nameDefault ValueTypeDescriptionenabledFalseboolEnable the EBS auditorinterval1440intHow often the auditor runs, in minutesemail_subjectUnattached EBS VolumesstringNotification email subjectignore_tagsprobator:ignorearrayA list of tags that will cause the auditor to ignore the volumeThis project is based on the work forCloud Inquisitorby Riot Games
Package: probator-auditor-iam
probator-auditor-iamPlease open issues in theProbatorrepositoryDescriptionThis auditor validates and applies IAM policies for AWS Accounts.Configuration OptionsOption nameDefault ValueTypeDescriptionenabledFalseboolEnable the IAM roles and auditorinterval30intHow often the auditor executes, in minutesmanage_rolesTrueboolEnable management of IAM rolesrolesTruestringJSON document with roles to push to accounts. See below for exampledelete_inline_policiesFalseboolDelete inline policies from existing roleshostnameNonestringGit server hostnamerepositoryNonestringPath of the Git repositoryauthentication_typeoauth-tokenstringAuthentication typeoauth_tokenNonestringOAuth2 token. Required ifauthentication_typeisoauth-tokenusernameNonestringGit username. Required ifauthentication_typeisusername-passwordpasswordNonestringGit password./Required ifauthentication_typeisusername-passwordmax_session_duration8stringIAM Assume Role MaxSessionDuration (in hours)disable_ssl_verifyFalseboolDisable SSL certificate validationrolesconfigurationTherolessetting allows you to configure roles to create and manage on all accounts enabled in Probator. The JSON document is structured as
a dictionary, with the top-level key being the name of the role, and the dictionary value has two keys;trustandpoliciestrustThetrustsetting must be a valid IAM Assume Role Policy Document. If thetrustkey is an empty object ({}), null or not set, the default trust
document is provided.policiesThepolicieskey contains a list of IAM policy names to attach to the role. These policies must exist within the account before running the auditor,
ideally being created by the auditor as well.Example documentThe example below shows how one can manage the role probator assumes for monitoring and auditing in your accounts{"probator_role":{"trust":{"Version":"2012-10-17","Statement":[{"Sid":"","Effect":"Allow","Principal":{"AWS":"arn:aws:iam::123456789012:role/probator-instance-role","Service":"ec2.amazonaws.com"},"Action":"sts:AssumeRole"}]},"policies":["ProbatorAccess"]}}This project is based on the work forCloud Inquisitorby Riot Games
Package: probator-auditor-vpc-flowlogs
probator-auditor-vpc-flowlogsPlease open issues in theProbatorrepositoryDescriptionEnabling this plugin will ensure that all your VPC's have VPC Flow Logs turned on. You can configure the logs to either be delivered to an
S3 bucket you provide, or to auto-generated CloudWatch Logs groups.If using CloudWatch Logs, a new log group will be created in the same region as the VPC, with the VPC Id as the log group name. S3 buckets
will not be created automaticallyConfiguration OptionsOption nameDefault ValueTypeDescriptionenabledFalseboolEnable the VPC Flow Logs auditorinterval60intRun frequency in minutesrole_nameVpcFlowLogsRolestringName of IAM Role used for VPC Flow Logsdelivery_methods3stringLog delivery methodtraffic_typeALLstringTraffic type to logbucket_nameNonestringBucket name to deliver logs toThis project is based on the work forCloud Inquisitorby Riot Games
Package: probator-auth-onelogin-saml
probator-auth-onelogin-samlPlease open issues in theProbatorrepositoryDescriptionThis code enables the OneLogin service for SAML based SSO authentication for the Probator web interface.Configuration OptionsOption nameDefault ValueTypeDescriptionstrictTrueboolStrict validation of SAML responsesdebugFalseboolService Provider Entity IDsp_entity_idService Provider Entity IDstringService Provider Entity IDsp_acsNonestringAssertion Consumer endpointsp_slsNonestringSingle Logout Service endpointidp_entity_idNonestringIdentity Provider Entity IDidp_ssosNonestringSingle Sign-On Service endpointidp_slsNonestringSingle Logout Service endpointidp_x509certNonestringBase64 encoded x509 certificate for SAML validationThis project is based on the work forCloud Inquisitorby Riot Games
Package: probator-auth-saml
probator-auth-osamlPlease open issues in theProbatorrepositoryDescriptionThis code enables SAML based SSO authentication for the Probator web interface.Configuration OptionsOption nameDefault ValueTypeDescriptionstrictTrueboolStrict validation of SAML responsesdebugFalseboolService Provider Entity IDsp_entity_idService Provider Entity IDstringService Provider Entity IDsp_acsNonestringAssertion Consumer endpointsp_slsNonestringSingle Logout Service endpointidp_entity_idNonestringIdentity Provider Entity IDidp_ssosNonestringSingle Sign-On Service endpointidp_slsNonestringSingle Logout Service endpointidp_x509certNonestringBase64 encoded x509 certificate for SAML validationThis project is based on the work forCloud Inquisitorby Riot Games
Package: probator-collector-dns
probator-collector-dnsPlease open issues in theProbatorrepositoryDescriptionThis collector gathers and collates all related DNS information, with which the relevant DNS auditors can analyse for potential  security issues.Configuration OptionsOption nameDefault ValueTypeDescriptionenabledFalseboolEnable the DNS collector plugininterval15intRun frequency in minutescloudflare_api_keystringCloudFlare API Keycloudflare_emailstringEmail address associated with the API keycloudflare_enabledFalseboolEnable CloudFlare as a source for DNS recordscloudflare_endpointhttps://api.cloudflare.com/client/v4stringCloudFlare API endpointaxfr_domains[]arrayDomains to attempt to perform zone transfers foraxfr_serverstringServer from where to request zone transfersaxfr_enabledFalseboolEnable using DNS Zone Transfers for recordsThis project is based on the work forCloud Inquisitorby Riot Games
Package: probator-scheduler-sqs
probator-scheduler-sqsPlease open issues in theProbatorrepositoryDescriptionThis scheduler takes care of scheduling the actual SQS messaging and tracks the status of the jobs as workers are executing said jobs.Configuration OptionsOption nameDefault ValueTypeDescriptionenabledTrueboolEnable SQS based schedulerqueue_regionus-west-2stringRegion of the SQS Queuesstatus_queue_urlNonestringURL of the SQS Queue for worker reportsjob_delay2floatSeconds between scheduled jobs. Can be used to avoid spiky loads during execution of tasksThis project is based on the work forCloud Inquisitorby Riot Games
Package: probatus
ProbatusOverviewProbatusis a python package that helps validate binary classification models and the data used to develop them. Main features:probatus.interpretprovides shap-based model interpretation toolsprobatus.metric_volatilityprovides tools using bootstrapping and/or different random seeds to assess metric volatility/stability.probatus.sample_similarityto compare two datasets using resemblance modelling, f.e.trainwith out-of-timetest.probatus.feature_elimination.ShapRFECVprovides cross-validated Recursive Feature Elimination using shap feature importance.probatus.missing_valuescompares performance gains of different missing values imputation strategies for a given model.InstallationpipinstallprobatusDocumentationDocumentation ating-bank.github.io/probatus/.You can also check out blog posts about Probatus:Open-sourcing ShapRFECV — Improved feature selection powered by SHAP.Model Explainability — How to choose the right tool?ContributingTo learn more about making a contribution to Probatus, please seeCONTRIBUTING.md.
Package: probayes
# probayes
Probability package supporting multiple Bayesian methods including MCMCUnlike existing libraries, probayes adopts a model-driven approach with full flexibility constrained only by the rules
of probability. Since probayes is its infancy and in a state of flux, there is no manual. Currently probayes supports
the following:Multiple random variable sampling in untransformed and transformed domain space.Transitional simulation, including random walks, using Markov chain conditionals.Discrete grid exact inference.Ordinary Monte Carlo random sampling.Ordinary Monte Carlo rejection sampling.Metropolis-Hastings MCMC sampling.Limited support for multivariate normal-covariance Gibbs sampling.In the near-future, it is intended to expand the scope of probayes to include:Code initial support for approximate inference using using dense mean field messaging.Support derivative-based updates (HMC, gradient ascent/descent optimisation).A quickstart is also intended, but for now there are examples in the examples/ subdirectories:checks/       Simple check scriptsrv_examples/  Random variable examplesmarkov/       Markov chain examplescov_examples/ Examples of using covariance matricesdgei/         Discrete grid exact inference examplesomc/          Ordinary Monte-Carlo examplesmcmc/         Markov chain Monte Carlo examples (Metropolis-Hastings, Gibbs…)
Package: probcalc
probcalcis a library that aims to provide a probability calculator in any Python REPL. It's mainly aimed at IPython, since this is often regarded as the best Python REPL, but any of them will work with this library.In statistics, we are often faced with random variables which are distributed as certain probability distributions and we need to work out the probability of certain things happening. This is often tedious and error-prone.probcalcis the solution.Let's say you flip a coin 100 times and want to work out the probability of getting at least 30 but less than 50 heads. You can say thatXis a random variable representing the number of heads you get in 100 throws and thenXis distributed as a binomial distribution with 100 trials and probability of one half. This is whereprobcalccomes in. You just define the variables and let the computer do the work:>>>fromprobcalcimportP,B>>>X=B(100,0.5)>>>P(10<X<=20)0.4601893013Or, let's say there's a call center that gets an average of 5 calls every minute and you want to know the probability that they get more than 10 calls in a minute. For this, we can use a Poisson distribution:>>>fromprobcalcimportPo>>>Y=Po(5)>>>P(Y>10)0.3840393452And of course, you can use the normal distribution:>>>fromprobcalcimportN>>>Z=N(0,1)>>>P(-1<Z<1)0.6826894723This project will implement more distributions in the future, and they will all be able to be used like this.For ease of use, you can also set the desired number of significant figures in your answer:>>>P(-1<Z<1)0.6826894723>>>P.set_sig_figs(4)>>>P(-1<Z<1)0.6827The full docs are availablehere.Changelog[Unreleased]v0.5.0Add geometric distributionv0.4.0Allow setting number of sig figs in resultv0.3.2Disallow keyword arguments when usingP()Make backend attributes and methods non-publicUse readthedocs.io for documentationv0.3.1Improve performance of normal distribution CDF with stdlibv0.3.0Add normal distributionUse more efficient calculations of binomial PMFCheck for nonsense in inequality assignmentv0.2.8Remove SciPy dependency by implemeting Poisson pmf with stdlibv0.2.7Fix bug inProbabilityCalculatorwith resetting bounds after getting an errorDeal with edge case inBinomialDistribution.calculate()v0.2.6Handle edge case in binomial distributionsAllow Poisson to handle much larger numbersv0.2.5Add nice repr for probability calculatorImplement!=functionalityv0.2.4Fix mistake in publish workflowv0.2.3Fix bug in automated testsv0.2.2Add changelogv0.2.1Set up PyPI stuff and GitHub Action for PyPIv0.2.0Add Poisson distributionv0.1.2Fix minor issuesv0.1.1Improve documentation and add alias table0.1.0Initial versionAdd Binomial distributionAdd P() syntax
Package: probconcept
probconceptReadme in progress.This python package was originally published in my Surface Velocity Tools
(SurfVelTools) application:Engel, F. L., 2023, Surface Velocity Tools (SurfVelTools), U.S. Geological
Survey software release,https://doi.org/10.5066/P9I5JABK.I am working to publish the internal probconcept module separately so that
it can be installed for various other uses.DocumentationUsageThe basic curve fitting is handled by theProbConceptclass. All methods for creating the fit and producing results
that can be plotted are contained within the class. See the documentation in the class for more information.At a minimum, to fit a curve to your velocity data and derive an output, you will need to import the class with
something like:fromprobconceptimportProbConceptaspcFrom here, supplynumpyarrays (1D vectors) for depth-velocity pairs of measurements made of the velocity profile
in the y-axis (see more info about what the y-axis is below).  An optional numpy array of the same shape
as the input depth-velocity pairs containing the uncertainty of the velocity can be supplied and modeled in the fit.pc_fit=pc.ProbConceptFit()pc_fit.populate_velocity_profile_data(meas_depths,meas_velocities,meas_uncertainty)If known, you can also supply a measured total depth, and/or measured max velocity and distance from the surface of the
max velocity (h). These are optional arguments. TheProbConceptclass will attempt to solve for these unknowns without
user input if not supplied.pc_fit=pc.ProbConceptFit()pc_fit.populate_velocity_profile_data(meas_depths,meas_velocities,meas_uncertainty,meas_total_depth,meas_max_velocity,h)Theory and ExplanationWork in progress...
Package: probcount
A simple and efficient probabilistic counter, suited for rate limiting and counting efficiently, without uploading every action to the remote database.UsageInstructionsfromprobcountimportProbCounterfromfunctoolsimportpartialpc=ProbCounter()defold_increase_function(counter_name,value):# run the real call to the DB increasing the counter# for a redis# return redis.Redis().incrby(name=counter_name, amount=value)# or a MongoDB# return (pymongo.MongoClient().database_name.collection_name.find_one_and_update({"_id": counter_name},{"$inc":{"value":value}},{"value":1}).get("value",0)+value)passdefold_get_function(counter_name):# run the real call to the DB increasing the counter# for a redis# redis.Redis().get(name=counter_name)# or a MongoDB# pymongo.MongoClient().database_name.collection_name.find_one({"_id": counter_name}, {"value": 1}).valuepasspc.create("name_of_counter",partial(old_increase_function,"name_of_counter"),partial(old_get_function,"name_of_counter"))pc.inc("name_of_counter",42.0)pc.get("name_of_counter")Supported Python VersionsPython Project Template supports the following versions out of the box:CPython 2.6, 2.7, 3.3, 3.4, 3.5, 3.6, 3.7PyPy 1.9AuthorsTudor Aursulesei
Package: probdiffeq
probdiffeqProbabilistic solvers for differential equations in JAXProbDiffEq implements adaptive probabilistic numerical solvers for initial value problems.It inherits automatic differentiation, vectorisation, and GPU capability from JAX.Features include:Stable implementationCalibration, step-size adaptation, and checkpointingState-space model factorisationsDense output and posterior samplingFiltering, smoothing, and many other backendsCustom information operatorsPhysics-enhanced regressionCompatibility with other JAX-based libraries such asOptaxorBlackJAX.and many more.AN EASY EXAMPLE:LINKEXAMPLES:LINKCHOOSING A SOLVER:LINKAPI DOCUMENTATION:LINKISSUE TRACKER:LINKBENCHMARKS:LINKCHANGELOG:LINKInstallationGet the most recent stable version from PyPi:pip install probdiffeqThis installation assumes thatJAXis already available.To install ProbDiffEq withjax[cpu], runpip install probdiffeq[cpu]WARNING:This is a research project. Expect rough edges and sudden API changes.VERSIONING:As long as Probdiffeq is in its initial development phase (version 0.MINOR.PATCH), version numbers are increased as follows:Bugfixes and new features increase the PATCH version.Breaking changes increase the MINOR version.See also:semantic versioning.What's next?Start with the quickstart, continue with theSolvers & Solutionsexamples and only then move to theParameter estimationexamples and the API documentation.The examples show how to interact with the API, and explain some useful facts about probabilistic numerical solvers.
While the API is not stable yet, the examples may be more instructive than the API docs.The advanced examples show applications of probabilistic numerical solvers, often in conjunction with external libraries.
For example,this notebookshows how to combine ProbDiffEq withOptax, andthis notebookdoes the same withBlackJAX.ContributingContributions are absolutely welcome!
Start with checking the existing issues for a "good first issue" and have a look at  the developer documentation.If you have a feature that you would like to see implemented, create an issue!BenchmarksProbDiffEq curates a range of benchmarks that includes various library-internal configurations
but also other packages likeSciPy,JAX, orDiffrax.
To run the benchmark locally, install all dependencies viapip install .[example,test]and then either open Jupyter and go todocs/benchmarksor execute all benchmarks viamake benchmarks-runBe patient, it might take a while.
Afterwards, open Jupyter to look at the result or build the documentation viamkdocs serveWhat do you find?Similar projectsTornadoxProbNumDiffEq.jlProbNumDiffraxCheck out how to transition from those packages:link.Anything missing in this list? Please open an issue or make a pull request.You might also likediffeqzoo:
A library for example implementations of differential equations in NumPy and JAXprobfindiff:
Probabilistic numerical finite differences, in JAX.