Saravanan666/pypi-org huggingface dataset:
Package: rapyd_db
An opinionated lightweight wrapper around various SQL backend drivers.Support right now is available for the following:MySQL / MariaDB viamysqlclientMicrosoft SQL Server viapymssqlmongoDB viapymongoFeaturesOpens and closes a connection for every query. I realize this is not what everyone needs. But I use this workflow in a lot of my projects, hence, opinionated.Usesyieldto return a generator to fetch large amount of data from a DB without loading everything into the memory.Logs last executed query and time for query execution with a unique ID so queries can be traced in log messages.Installationpip install rapyd_dbMake sure you are also installing the appropriate drivers for your respective backend as mentioned above.Or you can install with driver like this:pip install rapyd_db[mysql]UsageMySQL Backend# import this
from rapyd_db.backends.mysql import MySQL

# create the DB object; no connection is done at this point
# any argument accepted by the underlying DB driver can be passed
# `cursorclass` will be overwritten
db = MySQL(host='', user='', passwd='')

# run queries like so

# select large data with stream=True; returns a generator
rows = db.execute("SELECT * FROM blah", stream=True)
for row in rows:
    print(row)

# insert data with stream=False (default); returns rows_affected, lastrowid and results of the query
rows_affected, last_inserted_id, results = db.execute(
    'INSERT INTO blah(key) VALUE (%s)',
    ('value1', )
)This is an excerpt of the log messages usingbasicConfig. This will change depending on your logging configuration:INFO:rapyd_db.backends:f2e47d87874d4055beba66b6c8221aff - Connecting to DB
INFO:rapyd_db.backends.mysql:f2e47d87874d4055beba66b6c8221aff - Starting executing query at 2019-10-28 15:47:31.182261
INFO:rapyd_db.backends.mysql:f2e47d87874d4055beba66b6c8221aff - SELECT * FROM blah
INFO:rapyd_db.backends.mysql:f2e47d87874d4055beba66b6c8221aff - 2844047 row(s) affected in 10 second(s)
INFO:rapyd_db.backends.mysql:f2e47d87874d4055beba66b6c8221aff - Ended query execution at 2019-10-28 15:47:41.747841
INFO:rapyd_db.backends:f2e47d87874d4055beba66b6c8221aff - Closed connection to DBMSSQL BackendTODOMongo BackendTODOTestingFor these tests to run successfully, the test rig should have access to a DB backend.Running TestsA very easy way to set these environment variables would be to add them to a shell script file and source them like so:source ./test_environment.shTo run all tests, run the following command:python -m unittest discover -v

or

make testallTo run tests for a specific backend, issue the following command:python -m unittest -v rapyd_db.tests.test_mysql

or

make testmysql
Package: rapydmain
rapydmain is framework for python batch script.FeaturesAutomatically load config(JSON format).Output message when changed rapydmain status(e.g. script began, script ended, error occued).Automatically setup logger(use python logging).UsageSetup project directories & files, like pj_template(https://github.com/aznuii/rapydmain/tree/main/pj_template).[pj_dir]/
    config/
        fw.json
        loggin.json
        [any config file]
    log/
        [any log file]Setup python script.pip install rapydmainCoding python script.
Use by "with" statement.with rapydmain(Path(__file__).parent.parent) as main:
    # load config
    value = main.config['any1']['any2']

    # logging
    main.logger.info('log')
Package: rapydo-http
RAPyDo REST APIsREST APIs integrated with the RAPyDo frameworkBased on:Flaskflask-apispec with apispecmarshmallowwebargsFlask-CachingPyJWTpasslibpyotp With segnoFlask-SQLAlchemyNeomodelPyMySQLRedis-pypika with amqpcelery with celery-redbeatgunicorn with gevent
Package: rapydscript
RapydScript is a compiler that brings Pythonic syntax to JavaScript,
allowing you to write cleaner JavaScript and even reuse some Python
code in your front-end.InstallationTo install Rapydscript simply use easy_install or pip:pip install rapydscriptoreasy_install rapydscriptRapydscript requires PyMeta, written by Waldemar Kornewald, which
currently does not have a PyPI package.  It is included as part of the
Rapydscript install.LicenseThe project is GPLv3, but the output is license free.
Seehttp://www.gnu.org/licenses/gpl-faq.html#WhatCaseIsOutputGPL.
Package: rapy-genius
Rapy_GeniusRapy is a package that uses the Genius API to collect data from genius.com as songs, artists and lyricsThis framework has two modules:
genius_api: Contains the GeniusApiManager class.
data_managger: Contains the GeniusApiDatabaseManager class that uses MongoDB and pymongo to store data and use itVisithttps://docs.genius.com/to get API client access.InstallationRun the following to install'''pythonpip install rapy_genius'''Usage'''pythonfrom genius_api import GeniusApiManager
from data_manager import GeniusApiDatabaseManager
API_CLIENT_ACCESS_TOKEN = "XXXXXXXXX" #The Api client access token generated from https://docs.genius.com/
api_manager = GeniusApiManager(API_CLIENT_ACCESS_TOKEN)
db_manager = GeniusApiDatabaseManager(db_name='Genius_scrapper')
add_artist(45) #Eminem'''Developing Rapy Geniusto install rapy_genius along with the tools you need to develop and run tests'''bashpip install -e .[dev]'''
Package: rapyuta-io
Rapyuta.io Python SDKRapyuta.io Python SDK enables you to access platform services and resources in
your python applications.The SDK supports Python 3.8+. For older Python 2.x support, you can use the
0.x.xreleasesfrom Pypi.InstallThe SDK is distributed through PyPi index, and can be installed directly usingpip.pipinstallrapyuta-ioTo install the SDK from source, you can use thesetup.pyscript directly.
Clone the repository and from the root of the directory, run the following
command.pythonsetup.pyinstallGetting StartedBefore using the SDK, you need the Rapyuta Token. You can get it fromhere.fromrapyuta_ioimportClientTOKEN="RAPYUTA_TOKEN"client=rapyuta_io.Client(TOKEN)# Create a Project and use itfromrapyuta_ioimportProjectproject=client.create_project(Project("python-sdk"))client.set_project(project.guid)# Create a Buildfromrapyuta_ioimportBuild,StrategyType,DeviceArchclient.create_build(Build("dummy",StrategyType.DOCKER,"https://github.com/ankitrgadiya/dummy-package",DeviceArch.AMD64,))
Package: rapyuta-io-cli
Rapyuta CLIRapyuta CLI exposes features of Rapyuta.io platform on the command-line.The application is written in Python 3 and it is distributed through PyPI for
Python 3 environments.For Reference on directory structure please refer Please have a look at the
corresponding article:http://gehrcke.de/2014/02/distributing-a-python-command-line-application/InstallRio CLI is available on PyPI index and can be installed directly by running the
following command.pipinstallrapyuta-io-cliOn Unix-like systems it places therioexecutable in the user's PATH. On
Windows it places therio.exein the centralizedScriptsdirectory
which should be in the user's PATH.To install the CLI from source, you can use thesetup.pyscript directly.
Clone the repository and from the root of the directory, run the following
command.pythonsetup.pyinstallGetting StartedTo begin using the CLI, it must be authenticated with the Platform.rioauthloginThe Email and Password can either be given through flags (for scripting
purposes) or interactively through the Prompts.NOTE: Entering password as a Flag is not recommended because it leaves the
Traces.DevelopmentRio CLI project usesPipenvfor
development. It needs to be installed to setup the development environment.pipinstallpipenvOnce Pipenv is installed, a Python virtual environment can be quickly
bootstrapped by running the following commands in the root of the repository.pipenvinstall--devThis will create a virtual environment in the Pipenv's preconfigured location
(if one doesn't already exists). It will also install all the dependencies andrioclipackage in the location.To run the CLI (or any command) under the context of Pipenv's virtual
environment, prepend the commands withpipenv runpipenvrunrioTo run the RIO CLI from the source directly, you can userioclimodule
directly.pipenvrunpython-mriocliNew dependencies can be installed directly usingpipenv. This modifies thePipfileandPipfile.lock.pipenvinstall{dependency}But using thepipenvdirectly doesn't sync the dependencies in thesetup.pyfile. For this, the project uses a utility calledpipenv-setupwhich allows us to sync the dependencies.pipenvrunpipenv-setupsync
Package: rarbg
rarbg → rssAdapter for Torrent API (see docs) that serves search results as broadcatching-ready RSS feed.InstallationRequires Python 3.6 or later.pip install -U rarbgDockerRun it with docker like this:docker build . --tag rarbg:v1
docker run -p 4444:4444 -it rarbg:v1UsageRun the server by typingrarbg. You can optionally specify host and port with-h(default 0.0.0.0) and-poptions (default 4444).Access it by passing parameters tohttp://localhost:444/as you would pass them to Torrent API.Note that Torrent API has a rate limit of one request per two seconds.Token updates and rate limits are handled automatically.Convenience methods/imdb/<imdb_id>search by imdb (equals to/?mode=search&search_imdb=<imdb_id>)/tvdb/<tvdb_id>search by tvdb (equals to/?mode=search&search_tvdb=<tvdb_id>)/search/<search_term>search by string (equals to/?mode=search&search_string=<search_term>)Available filterscategoryfilter by category, specify multiple categories like this:44;45limitnumber of results:25,50or100(default:25)sortorder byseeders,leechers(default:last)min_seedersandmin_leechershide results with less activityranked=0get non-scene releasesAll parameters can be mixed together and work with convenience methods.Examplehttp://localhost:4444/imdb/tt2802850?category=41will get you HD releases of FargoCategories4 XXX (18+)
14 Movies/XVID
48 Movies/XVID/720
17 Movies/x264
44 Movies/x264/1080
45 Movies/x264/720
47 Movies/x264/3D
42 Movies/Full BD
46 Movies/BD Remux
18 TV Episodes
41 TV HD Episodes
23 Music/MP3
25 Music/FLAC
27 Games/PC ISO
28 Games/PC RIP
40 Games/PS3
32 Games/XBOX-360
33 Software/PC ISO
35 e-Books
Package: raredecay
raredecayThis package consists of several tools for the event selection of
particle decays, mostly built on machine learning techniques. It
contains:adata-containerholding data, weights, labels and more and
implemented root-to-python data conversion as well as plots and
KFold-data splittingreweightingtools from the hep_ml-repository wrapped in a
KFolding structure and with metrics to evaluate the reweighting
qualityclassifier optimizationtools for hyper-parameters as well as
feature selection involving a backward-eliminationanoutput handlerwhich makes it easy to add text as well as
figures into your code and automatically save them to a file… and moreHowTo examplesTo get an idea of the package, have a look at the howto notebooks:HTML
versionor theIPython
NotebooksMinimal exampleWant to test whether your reweighting did overfit? Use train_similar:importraredecayasrdmc_data=rd.data.HEPDataStorage(df,weights=*pd.Seriesweights*,target=0)real_data=rd.data.HEPDataStorage(df,weights=*pd.Seriesweights*,target=1)score=rd.score.train_similar(mc_data,real_data,old_mc_weights=1*orwhateverweightsthemchadbefore*)Getting started right nowIf you want it the easy, fast way, have a look at theReady-to-use
scripts.
All you need to do is to have a look at every “TODO” task and probably
change them. Then you can run the script without the need of coding at
all.Documentation and APIThe API as well as the documentation:DocumentationSetup and installationPyPIThe package with all extras requires root_numpy as well as rootpy (and
therefore a ROOT installation with python-bindings) to be installed on
your system. If that is not the case, some functions won’t work.Recommended installation (requires ROOT):pip install raredecay[all] --process-dependency-linksAnacondaEasiest way: use conda to install everything (except of the rep, which
has to be upgraded with pip for some functionalities)conda install raredecay -c mayou36To make sure you can convert ROOT-NTuples, usepip install raredecay[root]  # *use raredecay\[root\] in a zsh-console*or, instead of root/additionally (comma separated)reweightorreweightfor the specific functionalities.
Package: rarfile
This is Python module forRARarchive reading.
The interface follows the style ofzipfile.
Licensed underISClicense.Features:Supports both RAR3 and RAR5 format archives.Supports multi volume archives.Supports Unicode filenames.Supports password-protected archives.Supports archive and file comments.Archive parsing and non-compressed files are handled in pure Python code.Compressed files are extracted by executing external tool:unrar(preferred),unar,7ziporbsdtar.Works with Python 3.6+.Backends:BackendStatusNotesunrarSupportedRecommended: full format support.Non-free software, but free to use.unarSupportedNot usable on Windows: last build is from 2013
(v1.8.1) that does support output to stdout.Does not support RAR2 locked files.Does not support RAR5 Blake2 hash checking.7zipSupportedRAR support not available on Debian/Ubuntu repos.p7zipSupportedUnmaintained?Requiresp7zip-rarpackage on Debian/Ubuntu.bsdtarSupportedNot recommended: limited RAR format support.Does not support multi-volume archives.Does not support solid archives.Does not support password-protected archives.Does not support RARVM-based compression filters.unrar-freeNot supportedUnusable: Does not support output to stdout.Based on libarchive so similar format support
asbsdtar.Links:DocumentationDownloadsGitrepo
Package: rarog
Rarog is a monitoring utility for machine learning experiments. You may use it as a
light-weight alternative forTensorBoardorVisdom. Rarog stores all records inClickHousedatabase usingClickHouse Python Driver.Featureslog common python data types(bool, int, float, string, iterables)log 1d numpy arraysdistributed experiments monitoringSetupInstall viapip:pipinstallrarogStartClickHousedatabase if required:dockerrun-d--nameclickhouse--ulimitnofile=262144:262144-p9000:9000yandex/clickhouse-serverImportant note:the example above is just the easiest way. For production, you should
setup database backups or replicated.Rarog supports Python 3.4 and newer.UsageimportrandomfromrarogimportTrackertracker=Tracker(name='experiment_name')# trace values one by oneforstepinrange(10):tracker.trace(name='int_value',value=random.randint(10,20),step=step)tracker.trace(name='float_value',value=random.random(),step=step)# provide experiment phase as a stringtracker.trace(name='list_value',value=[random.random(),random.random()],step=step,phase='val')# trace values by dictforstepinrange(10,20):tracker.multy_trace({'int_value':random.randint(10,20),'float_value':random.random()},step=step)# get names of traced metricstracker.metrics# Out: ['time', 'step', 'phase', 'int_value', 'float_value', 'list_value']If you are going to record more than 100 entries per second,
it’s better to usesync_steporsync_secondsarguments.
Thus writing to the database will be done with some period, which is much faster.# `exist_ok` flag allow to use the same name for experimentstep_tracker=Tracker(name='experiment_name',sync_step=1000,exist_ok=True)forstepinrange(20,10**4):step_tracker.trace(name='bool_value',value=bool(random.randint(0,1)),step=step)step_tracker.multy_trace({'int_value':random.randint(10,20),'float_value':random.random()},step=step)# tracker should be manually synchronized after last entrystep_tracker.sync_accumulated_values()Experiments can be handled via managerfromrarogimportManagermanager=Manager()manager.list_experiments()# Out: ['experiment_name']manager.remove_experiment('experiment_name')Retrieving your dataTODO (manually and with visualization)TODOPytorch tensors supportSupport 2d arraysTensorflow data types supportSplit Aggregation View for summarization and underlying tablesStore experiments metadata(config, author, etc.)Autodocs
Package: raruscloud
Esb ConnectDeveloped by mr. Lozovskoy Sergey from RarusCloud (c) 2022Examples of How execute workflowfromraruscloudimportEsbConnect# creating connection to ESBuser='username'password='password'esb=EsbConnect(user=user,password=password,esb_prod=True)# Other Codeesb.execute_workflow(request_data={},workflow_name='workflow',workflow_group='group',workflow_subgroup='subgroup')# When You Are Doneprint(esb.result)print(esb.task_result)Have questions?serloz@rarus.ru
Package: rasa-alice
Rasa Connector for Yandex Dialogs.Installing$pipinstallrasa-aliceUsageURL:/webhooks/alice/webhookcredentials.ymlrasa_alice.AliceInput:domain.ymltemplates:utter_text:-text:"Здравствуйте!Этомы,хороводоведы."utter_text_custom:-custom:text:"Здравствуйте!Этомы,хороводоведы."tts:"Здравствуйте!Этомы,хоров+одов+еды."end_session:true# false by defaultutter_text_with_buttons:-text:'Hello'buttons:-title:'Надписьнакнопке'url:'https://example.com/'hide:trueutter_big_image:-custom:text:"Здравствуйте!Этомы,хороводоведы."card:type:"BigImage"image_id:"1027858/46r960da47f60207e924"title:"Заголовокдляизображения"description:"Описаниеизображения."button:text:"Надписьнакнопке"url:"http://example.com/"utter_items_list:-custom:text:"Здравствуйте!Этомы,хороводоведы."card:type:"ItemsList"header:text:"Заголовокгалереиизображений"items:-image_id:"<image_id>"title:"Заголовокдляизображения."description:"Описаниеизображения."button:text:"Надписьнакнопке"url:"http://example.com/"The original request is stored in metadata, you can use it in custom actions:classActionHelloWorld(Action):defname(self)->Text:return"action_hello_world"defrun(self,dispatcher:CollectingDispatcher,tracker:Tracker,domain:Dict[Text,Any])->List[Dict[Text,Any]]:events=tracker.current_state()['events']user_events=[]foreinevents:ife['event']=='user':user_events.append(e)original_request=user_events[-1]['metadata']payload=original_request['request']['payload']
Package: rasa-audiocodes
AudioCodes VoiceAI Connect Channel for RasaAudioCodes VoiceAI Connect is an application that enables telephony access
for chatbots.The Rasa integration is using a REST protocol namedAC-Bot-API.InstallationThe easiest way to install the package is throughPyPI.pipinstallrasa-audiocodesRasa ConfigurationAdd the following content tocredentials.yml:rasa_audiocodes.AudiocodesInput:token:"CHOOSE_YOUR_TOKEN"DockerIf you're using Rasa on Docker, you can copy rasa_audiocodes and docker-compose.yml
to your application directory, and edit the volumes section to match your app directory.If you already have a docker-compose file, just add this to the rasa volumes:-./rasa_audiocodes:/opt/venv/lib/python3.8/site-packages/rasa_audiocodesVoiceAI Connect Cloud SetupThe easiest way to start using VAIC is by visitinghttps://voiceaiconnect.audiocodes.io.You can get a phone number, and route it to your bot.Configuration is straight-forward. Click the + sign, then enter the name
of the bot, URL and the token.You can now click Validate to verify that the bot is accessible and the
audiocodes channel is configured correctly.Click Next, then choose a country and a city to get a phone number.After a minute, the number should be routed to your bot.VoiceAI Connect ConfigurationIf you maintain a VAIC instance, configure a provider with the following attributes:{"name":"rasa","type":"ac-bot-api","botURL":"https://<RASA>/webhooks/audiocodes/webhook","credentials":{"token":"CHOOSE_YOUR_TOKEN"}}
Package: rasac
RASA Codeless provides a GUI for NLU pipeline configurations and model training and evaluation for Rasa Conversational AIs.FeaturesAllows configuring, training, and evaluating RASA models efficiently through a React based UIFully supports rasa 2.8.x projectsProvides an easy-to-use CLIProvides an efficient server-based GUIRequires minimum machine learning expertise and RASA framework specific knowledgeLearning curve insights for non-expertsWhat's Cooking?Training data managementStory designerCustom config file uploadNLU file uploadCDD (Interactive Learning)Rule designerConversation flow designerCustom NLU pipeline components attachingCross-validation and F1-score based performance evaluationAutomated validation dataset size configurationUser-friendly model and deployment managementOne-click deployDocker support for model deploymentCustom MongoDB botstore supportOmni-channel supportLimitations and Known IssuesDoes not support early stoppingTraining ETA not availableEvaluations are limited to DIET Classifier for now📒 Docs:https://rasa-codeless.github.io📦 PyPi:https://pypi.org/project/rasac/2.1.1/🪵 Full Changelog:here
Package: rasa-cli-completion
rasa-cli-completionSimple implementation of a bash / zsh autocomplete script forRasa.RequirementsInstalledrasalibraryA unix systemInstallationUsing pipRunpip install rasa-cli-completionRunpip install .in the cloned directoryAdd the output of the following command to your.bashrc/.zshrcpython-mrasa_cli_completion.show_configUsing this repositoryClone this repositoryRunpip install .in the cloned directoryAdd the output of the following command to your.bashrc/.zshrcpython-mrasa_cli_completion.show_configNotesThe completion is a bit slow at the beginning. However, the completion results are cached in~/.rasa-autocomplete.json(you can turn caching off by settingRASA_AUTOCOMPLETE_CACHING_OFF=true)
Package: rasa-core
Rasa CoreRasa Core is a framework for building conversational software, which includes
chatbots on:Facebook MessengerSlackMicrosoft Bot FrameworkRocket.ChatMattermostTelegramTwilioBut you can also build assistants using:Alexa SkillsGoogle Home ActionsRasa Core's primary purpose is to help you build contextual, layered
conversations with lots of back-and-forth. To have a real conversation,
you need to have some memory and build on things that were said earlier.
Rasa Core lets you do that in a scalable way.There's a lot more background information in thisblog post.What do Rasa Core & NLU do? 🤔Read About the Rasa StackI'd like to read the detailed docs 🤓Read The DocsI'm ready to install Rasa Core! 🚀InstallationI'm ready to start building! 🤖Rasa Stack starter-packI have a question ❓Rasa Community ForumI would like to contribute 🤗How to contributeWhere to get helpThere is extensive documentation:master(if you install fromgithub) orstable(if you install frompypi)Please useRasa Community Forumfor quick answers to
questions.README Contents:How to contributeDevelopment InternalsLicenseHow to contributeWe are very happy to receive and merge your contributions. There is
some more information about the style of the code and docs in thedocumentation.In general the process is rather simple:create an issue describing the feature you want to work on (or
have a look at issues with the labelhelp wanted)write your code, tests and documentationcreate a pull request describing your changesYou pull request will be reviewed by a maintainer, who might get
back to you about any necessary changes or questions. You will
also be asked to sign aContributor License Agreement.Development InternalsRunning and changing the documentationTo build & edit the docs, first install all necessary dependencies:brew install sphinx
pip install -r dev-requirements.txtAfter the installation has finished, you can run and view the documentation
locally using:make livedocsVisit the local version of the docs athttp://localhost:8000in your browser.
You can now change the docs locally and the web page will automatically reload
and apply your changes.LicenseLicensed under the Apache License, Version 2.0.
Copyright 2019 Rasa Technologies GmbH.Copy of the license.A list of the Licenses of the dependencies of the project can be found at
the bottom of theLibraries Summary.