Saravanan666/pypi-org huggingface dataset:
Package: anylogic-alpyne
AlpyneTheAnyLogic-Python connectorThis is a Python library for interactively running models exported from the RL experiment.Currently, this library released as apublic beta(so please excuse any rough edges). If you have problems or bugs, please file them in theIssuestab. For general talk or questions, there's theDiscussionstab.Full documentation (with background information, getting started guide, and class docs) can be found @https://t-wolfeadam.github.io/AlpyneInstallationAlpyne supports Python 3.6+Install this library running the following command in a terminal prompt:pip install anylogic-alpyne.Preparing an AnyLogic modelYou can useanyedition of AnyLogic (PLE, University, or Professional) with this library. However, be aware that limitations of the edition will still apply. For example, PLE users executing models which utilize industry-specific libraries have their runs limited to 1-hour simulation time.You will need to setup your model with the following components.RL experiment, with the Configuration, Observation, Action, and stopping conditions filled out, as per your specificationsA call to the RL experiment'stakeActionmethod, at the moment you wish an action to be takenTo export the model, navigate to the properties of your RL experiment and click the option at the top to export it. If you do not see an option for Alpyne or generic 3rd parties, you may use the one for "Microsoft Bonsai".Next StepsThe API and overall workflow for Alpyne is intentionally similar to the AnyLogic Cloud. In your Python code, you will create a single Client object, passing a reference to where your exported model is located, in addition to setting other options. This object then gives you access to templates for the inputs/outputs of the model in addition to methods for creating new model runs, which can then be interacted with.For more, see thedocumentation page, download the provided examples, or post in the Discussions tab.
Package: anymail-history
anymail-history - Email History (database storage) forDjango AnymailKeep history of all emails sent byDjango AnymailIntroductionanymail-history implements database storage for Django Anymail.ResourcesPackage on PyPI:https://pypi.org/project/anymail-history/Project on Github:https://github.com/pfouque/django-anymail-historyFeaturesStore sent emailsStore tracking eventsDisplay Adminhtml templating ?RequirementsDjango >=3.2Python >=3.8How toInstall AnymailInstall$ pip install "django-anymail-history"Register anymail_history in your list of Django applications:INSTALLED_APPS = [
    # ...
    "anymail",
    "anymail_history",
    # ...
]Then migrate the app to create the database tablemanage.py migrate🎉 Voila!SettingsYou can add settings to your project’s settings.py either as a singleANYMAILdict, or by breaking out individual settings prefixed with ANYMAIL_. So this settings dict:ANYMAIL = {
    "STORE_HTML": True,
}…is equivalent to these individual settings:ANYMAIL_STORE_HTML = TrueAvailable settingsANYMAIL_STORE_FAILED_SEND: (default: False) Store message even if esp didn't returned a message-id.ANYMAIL_STORE_HTML: (default: False) Store html alternatives.ContributePrinciplesSimple for developers to get up-and-runningConsistent style (black,ruff)Future-proof (pyupgrade)Full type hinting (mypy)Coding styleWe usepre-committo run code quality tools.Install pre-commithowever you like (e.g.pip install pre-commitwith your system python) then set up pre-commit to run every time you
commit with:>pre-commitinstallYou can then run all tools:>pre-commitrun--all-filesIt includes the following:poetryfor dependency managementRuff,blackandpyupgradelintingmypyfor type checkingGithub Actionsfor builds and CIThere are default config files for the linting and mypy.TestsTests packageThe package tests themselves areoutsideof the main library code, in a package that is itself a
Django app (it containsmodels,settings, and any other artifacts required to run the tests
(e.g.urls).) Where appropriate, this test app may be runnable as a Django project - so that
developers can spin up the test app and see what admin screens look like, test migrations, etc.Running testsThe tests themselves usepytestas the test runner. If you have installed thepoetryevironment,
you can run them thus:$ poetry run pytestor$ poetry shell
(anymail-history-py3.10) $ pytestCI.github/workflows/lint.yml: defines and ensure coding rules on Github..github/workflows/test.yml: Runs tests on all compatible combinations of Django (3.2+) & Anymail(8.4+), Python (3.8+)in a Github matrix..github/workflows/coverage.yml: Calculates the coverage on an up to date version.
Package: anymarkup-core
This is the core library that implements functionality ofhttps://github.com/bkabrda/anymarkup.
You can install this if you only want to use a subset of anymarkup parsers. For example, you
can do this:$ pip install anymarkup-core PyYAML
$ python -c "import anymarkup_core; print(anymarkup_core.parse('foo: bar'))"… and you don’t needxmltodictinstalled, for example. You can use anymarkup-core
in the same way you use anymarkup, except you have to import fromanymarkup_core, obviously.
Package: anymath
anymath-0.0.1tool for work with any number systems
Инструкция по эксплуатации библиотеки anymathАвтор - Рябов Никита Андреевич
версия - 0.0.1
описание - библиотека для работы с любыми системами счисленияБиблиотека содержит единственный класс System
В классе определены 5 методовметодinitописание - конструктор класса
аргументы:
alphabet
описание - алфавит системы счисления
тип - string
значение по умолчанию - "01"
требования - не содержит повторяющихся символов, не содержит ".", не содержит "-"
результат - нет
пример использования - test_system=anymath.System("0123456789ABC")метод Compare
описание - сравнение 2 чисел в пользовательской системе счисления
аргументы:
x
описание - рациональное число в пользовательской системе счисления
тип - string
значение по умолчанию - нет
требования - нет
y
описание - рациональное число в пользовательской системе счисления
тип - string
значение по умолчанию - нет
требования - нет
результат - один из символов ">", "<", "="
пример использования - test_system.Compare("-A45B4.C3","3B")метод To_10
описание - перевод числа в десятичную систему счисления
аргументы:
x
описание - рациональное число в пользовательской системе счисления
тип - string
значение по умолчанию - нет
требования - нет
accuracy
описание - точность перевода, необходима в случае некоторых дробных чисел
тип - integer
значение по умолчанию - 3
требования - нет
результат - int или float
пример использования - test_system.To_10("CCC5A.87A")метод To_system
описание - перевод числа в пользовательскую систему счисления
аргументы:
x
описание - рациональное число в десятичной системе счисления
тип - integer
значение по умолчанию - нет
требования - нет
accuracy
описание - точность перевода, необходима в случае некоторых дробных чисел
тип - integer
значение по умолчанию - 3
требования - нет
результат - string
пример использования - test_system.To_system(124)метод Calc
описание - математические операции
аргументы:
x
описание - рациональное число в пользовательской системе счисления
тип - string
значение по умолчанию - нет
требования - нет
y
описание - рациональное число в пользовательской системе счисления
тип - string
значение по умолчанию - нет
требования - нет
a
описание - математический оператор "+", "-", "/", "*"
тип - string
значение по умолчанию - нет
требования - нет
accuracy
описание - точность вычисления, необходима для операции деления
тип - integer
значение по умолчанию - 3
требования - нет
результат - string
пример использования - test_system.Calc("1A65","B2","/",5)
Package: anymlops
A data science operating platform that literally works ;)For everyone, from startups to the largest companies.InformationLinksLicenseTable of contentsTable of contentsAnymlopsCloud Providers ☁️Installation 💻Pre-requisitesInstall AnymlopsUsage 🚀Code of Conduct 📖LicenseAutomated data science platform. FromJupyterHubto Cloud environments withDask Gateway.Anymlops is an open source data science platform that enables enterprises to build and maintain cost-effective and scalable compute platforms
onKubernetesat day 0.AnymlopsThe Kubernetes version of Anymlops usesTerraform,Helm, andGitHub Actions.The infrastructure's construction, modifications, and version control are managed by Terraform.Kubernetes resources can be defined, installed, and maintained using Helm.The automatic creation of commits upon rendering the configuration file (anymlops-config.yaml) and initiation of deployment action is facilitated by GitHub Actions.At Anymlops, we're all about making things easy for you! That's why you don't need to worry about understanding any of the technical jargon we've mentioned. Our goal is to ensure your project is deployed smoothly and successfully, without any fuss on your end.Cloud Providers ☁️Anymlops offers out-of-the-box support for the major public cloud providers:Digital Ocean,
AmazonAWS,GCP, and MicrosoftAzure.Installation 💻Pre-requisitesOperating System: Currently, Anymlops supports development on Linux and Macos operating systems. Windows is NOT supported yet.You need Python >= 3.7 on your local machine or virtual environment to work on Anymlops.Virtual environments are our first class citizens. (conda,pipenvorvenv) is also encouraged.Install AnymlopsTo install Anymlops type the following commands in your command line:Install usingconda:condainstall-cconda-forgeanymlops# if you prefer using mambamambainstall-cconda-forgeanymlopsInstall usingpip:pipinstallanymlopsOnce finished, you can check Anymlops's version (and additional CLI arguments) by typing:anymlops--helpIf successful, the CLI output will be similar to the following:usage:anymlops[-h][-v]{deploy,destroy,render,init,validate}...

Anymlopscommandline

positionalarguments:{deploy,destroy,render,init,validate}Anymlops

optionalarguments:-h,--helpshowthishelpmessageandexit-v,--versionAnymlopsversionUsage 🚀To ensure a seamless and fully automated deployment with Anymlops, you must configure multiple environment variables. Obtain the required variables by consulting the [Anymlops Get started documentation][docs-get-started].After collecting the necessary credentials, establish them as UNIX environment variables. With this step complete, you'll be able to deploy Anymlops in mere minutes.For detailed step-by-step instructions on how to deploy Anymlops, check the [Anymlops documentation][docs-deploy].Code of Conduct 📖To guarantee a welcoming and friendly community, we require all community members to follow ourCode of Conduct.LicenseAnymlops is BSD3 licensed.
Package: anymotion-sdk
AnyMotion Python SDKThis is the Software Development Kit (SDK) for Python, which allows Python developers to write software that makes use ofAnyMotion.
It works on Python 3.6 or greater.InstallationInstall usingpip:$pipinstallanymotion-sdkIf you want to use aCV-based methods:$pipinstallanymotion-sdk[cv]UsageTo use AnyMotion Python SDK, you must first import it and tell it about your credentials which issued by theAnyMotion Portal:importanymotion_sdk# Setup clientclient=anymotion_sdk.Client(client_id="your_client_id",client_secret="your_client_secret")You can also use environment variables:exportANYMOTION_CLIENT_ID=<your_client_id>exportANYMOTION_CLIENT_SECRET=<your_client_secret># Setup client using environment variablesclient=anymotion_sdk.Client()The following is how to upload an image and extract the keypoints:# Upload image fileupload_result=client.upload("image.jpg")# Extract keypointkeypoint_id=client.extract_keypoint(image_id=upload_result.image_id)extraction_result=client.wait_for_extraction(keypoint_id)# Get keypoint data from resultkeypoint=extraction_result.jsonYou can get the uploaded data or the keypoint extracted data and so on as follows:# Get data of a specific idimage=client.get_image(image_id)movie=client.get_movie(movie_id)keypoint=client.get_keypoint(keypoint_id)drawing=client.get_drawing(drawing_id)analysis=client.get_analysis(analysis_id)comparison=client.get_comparison(comparison_id)# Get all dataimages=client.get_images()movies=client.get_movies()keypoints=client.get_keypoints()drawings=client.get_drawings()analyses=client.get_analyses()comparisons=client.get_comparisons()Inget_keypoint,get_drawing,get_analysis, andget_comparison, you can use thejoin_dataoption to get the relevant data at the same time.>>>client.get_keypoint(keypoint_id){'id':1,'image':2,'movie':None,'keypoint':[{'nose':[10,20]}],'execStatus':'SUCCESS','failureDetail':None,'createdAt':'2020-01-01T00:00:00.000000Z','updatedAt':'2020-01-01T00:00:00.000000Z'}>>>client.get_keypoint(keypoint_id,join_data=True){'id':1,'image':{'id':2,'name':'image','text':'','height':100,'width':100,'contentMd5':'ecWkdCSrnBa9+EYREt/fbg==','createdAt':'2020-01-01T00:00:00.000000Z','updatedAt':'2020-01-01T00:00:00.000000Z'},'movie':None,'keypoint':[{'nose':[10,20]}],'execStatus':'SUCCESS','failureDetail':None,'createdAt':'2020-01-01T00:00:00.000000Z','updatedAt':'2020-01-01T00:00:00.000000Z'}The way to output the log to stdout is as follows:# Log level is INFO by default.anymotion_sdk.set_stream_logger()# Set the log level to DEBUG.# At the DEBUG level, the content of the request and response to the API is output.anymotion_sdk.set_stream_logger(level=logging.DEBUG)For more examples, seehere.CV-based methodsIf you install the extra package,download_and_readis available.download_and_readreturns the downloaded image or video in numpy format.data=client.download_and_read(drawing_id)Warning: Large videos use a lot of RAM.Change LogSeeCHANGELOG.md.
Package: anynotify
anynotifyanynotify is a flexible, backend-independent error notification system for Python applications. It enables developers to seamlessly integrate error reporting with various notification channels such as Discord.FeaturesBackend Independent: Choose your preferred notification channel.Easy Integration: Simple setup process for Python applications.Rate-limiting: Prevent flooding notification channels.Quick Startimportanynotifyimportlogginganynotify.init(integrations=[anynotify.LoggingIntegration()],client=anynotify.DiscordClient(webhook_url='https://...'),)logging.warning('This will be sent to the Discord channel')
Package: anyoneai
AnyoneAIDemocratizing AI by providing an end-to-end solution for learning, prototyping, and deploying production ready Machine Learning models at ease.Contribute Â»AboutAnyoneAI aims to democratize AI education by providing an intuitive and interactive platform to gain a solid understanding of AI and learn how to solve problems with it. The platform will provide lab-based tutorials where the learners will build their own AI models as they learn, and be able to see the "actual python code" of the model automatically generated as they build.The short "10 minutes" micro tutorials are specifically designed for learners to quickly learn and invest in themselves in small pockets of time spread throughout the day.StatusReleaseBranchReleaseArtifactMasterPyPIDevelopmentGetting StartedInstallTo install the beta release:pip install anyoneaiUsageTo start AnyoneAI Lab Session (default port - 8089):anyoneaiTo start AnyoneAI Session at a specific port:anyoneai --port <PORT>LicenseGNU General Public License
Package: anypay
AnyPayAPIA simple, yet powerful library for AnyPayAPIUsageWithAnyPayAPIyou can easily create and retrieve payment and payout info, get informaition about your account's balance and commissions, etc.DocumentationOfficial docs can be found on theAPI's webpageInstallationpipinstallanypayRequirementsPython 3.7+httpxpydanticFeaturesAsynchronousException handlingPydantic return modelLightWeightBasic exampleimportasynciofromanypayimportAnyPayAPI,AnyPayAPIErrorapi=AnyPayAPI('api_id','api_key',check=False,# you can disable credentials check)asyncdefmain():try:awaitapi.get_balance()exceptAnyPayAPIErrorasexc:print(exc)payments=awaitapi.get_payments(project_id=1)# project_id can be provided in __init__print(payments[0].id,payments[0].status)bill=awaitapi.create_payment(pay_id=1234,project_id=1,method='qiwi',email='test@mail.ru',amount=100,currency='RUB',description='Test payment',)print(bill.id,bill.url)bill=awaitapi.create_bill(# easier way to create payment via SCIpay_id=1234,amount=100,project_id=1,project_secret='abcd',)print(bill.id,bill.url)#get balance via property (sync)print(api.balance)asyncio.run(main())Developed by Nikita Minaev (c) 2023
Package: anypay-wrapper
A small API wrapper for AnyPay bank system made for a project.Creating a paymentGetting payments listInstallationInstall AnyPay API with pippipinstallanypay_apicdmy-projectEnvironment VariablesTo use the class, you need to provide API_KEY and API_IDAPI_KEYAPI_IDUsage/ExamplesCreating a paymentfromanypay_apiimportPayApiapi=PayApi(API_ID,API_KEY,PROJECT_ID)asyncdefcreate_payment():payment=awaitapi.create_payment(pay_id,amount,currency,desc,method,method_currency)print(payment.payment_url)-https://anypay.io/pay/de13d3493-4508-4c6a-90d4Getting a paymentfromanypay_apiimportPayApiapi=PayApi(API_ID,API_KEY,PROJECT_ID)asyncdefcreate_payment():payments=awaitapi.get_payments(payout_id)print(payments.total)-2FeedbackI would be very pleased for a star ⭐️
Package: anyplayer
Play audio in any available way.ffplaympvvlcmpg123cmussimpleaudiopyaudioplaysoundautoInstallationpython-mpipinstallanyplayerwith any supported player:python-mpipinstallanyplayer[dev]Simple Examplefromanyplayerimportget_playerplayer=get_player('auto','./ba.mp3')player.start()player.wait()Recommended Optional DependenciesOnly wav audio or no audio: Python modulesimpleaudio.Multiple audio (& video) formats supported: command-line toolFFmpeg.
Package: anypubsub
A generic interface wrapping multiple backends to provide a consistent pubsub API.UsageCreate a pubsub object:from anypubsub import create_pubsub
pubsub = create_pubsub('memory')or create a pubsub object from settings:from anypubsub import create_pubsub_from_settings
pubsub = create_pubsub_from_settings({'anypubsub.backend': 'memory'}, prefix='anypubsub.')Subscribe to a channel:subscriber = pubsub.subscribe('a_channel')you can also subscribe to multiple channels:subscriber = pubsub.subscribe('a_channel', 'b_channel')the subscriber is an iterator object that returns the next published message at each loop increment, and blocks until
next message is published.Publish a message to a channel:pubsub.publish('a_channel', 'hello world!')

message = next(subscriber)
assert message == 'hello world!'Supported backendsmemoryredismongodbamqp (tested with rabbitmq)Backend specific optional settingsredis:host: hostname or full redis url, default: localhost
port: default 6379
db: default 0
max_connections: connection pool max connections
connection_pool: an already created redis-py ConnectionPoolmongodb:host: hostname or full mongodb url
port: mongodb port
max-pool-size: connection pool max connections
client: an already created pymongo MongoClient
database: database used to store messages, default anypubsub
collection: collection used to store messages, default anyps_messages
collection_size: messages collection size in bytes, default 10MB
Package: anypy
Generic Python Tools & UtilitiesA collection of python utilities, without hard dependenciesInstallationpipinstallanypyQuickstartDevelopment installationSetup the development environment:gitclonegit@github.com:lucmos/anypy.gitcdanypy
condaenvcreate-fenv.yaml
condaactivateanypy
pre-commitinstallRun the tests:pre-commitrun--all-files
pytest-vUpdate the dependenciesRe-install the project in edit mode:pipinstall-e.[dev]
Package: anypytools
AnyPyToolsAnyPyTools is a toolkit for working with theAnyBody Modeling System (AMS)from Python. It enables reproduceable research with the AnyBody Modeling System, and bridges the gap to whole ecosystem of open source scientific Python.The AnyPyTools Python package enables batch processing, parallization of model
simulations, model sensitivity studies, and parameter studies, using either
Monte-Carlo (random sampling) or Latin hypercube sampling. It makes reproducible
research much easier and replaces the tedious process of manually automating the
musculoskeletal simulations and aggregating the results.If you use the library for publications pleasecite as:Lund et al., (2019). AnyPyTools: A Python package for reproducible research with the AnyBody Modeling System. Journal of Open Source Software, 4(33), 1108,https://doi.org/10.21105/joss.01108InstallationDownload and install theAnaconda Python distributionAfter installation opens the Anaconda command prompt and type:condaconfig--addchannelsconda-forge
condainstallanypytoolsThe library is also available onPyPifor installing usingpip.UsageThe simplest case:fromanypytoolsimportAnyPyProcessapp=AnyPyProcess()macro=['load "Model.main.any"','operation Main.Study.InverseDynamics','run',]app.start_macro(macro)Please see theJupyter Notebook based tutorial, or check the the following for more information:AnyPyTools's Documentation
Package: anyrl
# anyrl-pyThis is a Python remake (and makeover) of [anyrl](https://github.com/unixpickle/anyrl). It is a general-purpose library for Reinforcement Learning which aims to be as modular as possible.# InstallationYou can install anyrl with pip:` pip install anyrl `# APIsThere are several different sub-modules in anyrl:models: abstractions and concrete implementations of RL models. This includes actor-critic RNNs, MLPs, CNNs, etc. Takes care of sequence padding, BPTT, etc.envs: APIs for dealing with environments, including wrappers and asynchronous environments.rollouts: APIs for gathering and manipulating batches of episodes or partial episodes. Many RL algorithms include a “gather trajectories” step, and this sub-module fulfills that role.algos: well-known learning algorithms like policy gradients or PPO. Also includes mini-algorithms like Generalized Advantage Estimation.spaces: tools for using action and observation spaces. Includes parameterized probability distributions for implementing stochastic policies.# MotivationCurrently, most RL code out there is very restricted and not properly decoupled. In contrast, anyrl aims to be extremely modular and flexible. The goal is to decouple agents, learning algorithms, trajectories, and things like GAE.For example, anyrl decouples rollouts from the learning algorithm (when possible). This way, you can gather rollouts in several different ways and still feed the results into one learning algorithm. Further, and more obviously, you don’t have to rewrite rollout code for every new RL algorithm you implement. However, algorithms like A3C and Evolution Strategies may have specific ways of performing rollouts that can’t rely on the rollout API.# Use of TensorFlowThis project relies on TensorFlow for models and training algorithms. However, anyrl APIs are framework-agnostic when possible. For example, the rollout API can be used with any policy, whether it’s a TensorFlow neural network or a native-Python decision forest.# StyleI useautopep8andflake8. Here is the command you can use to run autopep8:` autopep8--recursive--in-place--max-line-length100 . `I recommend the following flag for flake8:–max-line-length=100
Package: anyrun
APP.ANY.RUN CLIENTThis is a package that allows downloading and searching malware analysis from public submissions fromapp.any.run.
It is built as a websocket client applicationFeaturesRegister to all public submissionsRequirementswebsocket_client==0.56.0Python 3.5, 3.6, 3.7InstallationYou can install django-celery-results either via the Python Package Index (PyPI)
or from source.To install usingpip,::$ pip install -U anyrunQuickStartfrom anyrun import AnyRunClient


def callback(msg: dict) -> None:
    print(msg)


if __name__ == "__main__":
    client = AnyRunClient(
        on_message_cb=callback,
        enable_trace=False
    )
    client.connect()
    client.run_forever()And as a response you should get...
...
{'msg': 'added', 'collection': 'tasks', 'id': '5cf6d8005ed7525c25fe5660', 'fields': ... }
...
...Settingsparamdescriptionenable_traceenables debug trace logs, default: FalseTestingYou can run the tests by using tox.pip install toxThen:toxTODOAdd support for search.More examples.