Saravanan666/pypi-org huggingface dataset:
Package: connectedcar
The ConnectedCar Python SDK is an open-source, python package that provides the ability to send commands to your Ford Sync Connect connected vehicle.Installationpython3-mpipinstallconnectedcarRequirementsTo make requests to a vehicle, the end user must have signed up for an account usingFord Pass. These credentials will be used to authenticate your requests.Getting StartedImport the ConnectedCar SDKimportconnectedcarCreate a new connectedcarclientNote the default ConnectedCar client_id is9fb503e0-715b-47e8-adfd-ad4b7770f73bclient=connectedcar.AuthClient('9fb503e0-715b-47e8-adfd-ad4b7770f73b',None,None,None,'US')# Region argument is only required if you live outside the United States.Note: If your region is outside of the US you can pass different region parameters. Regions: (US, CA, EU, AU)Useclient.get_user_access_token()to exchange your user credentials for anaccess object. To make any vehicle data request to the Ford Sync Connect API, you'll need to give the SDK a validaccess token.access=client.get_user_access_token('<username>','<password>')This access object will look like this:{"access_token":"...","token_type":"Bearer","expiration":"2021-03-01T18:04:25+00:00","refresh_token":"...","refresh_expiration":"2021-03-02T18:03:25+00:00","expires_in":"..."}Access tokens will expire every 2 hours, so you'll need to constantly refresh them by callingclient.exchange_refresh_token()refreshToken=client.exchange_refresh_token(access['refresh_token'])With your access token in hand, useconnectedcar.User()to get a User object representing the user.user=connectedcar.User(access['access_token'],"US")# Region argument is only required if you live outside the United States.Useuser.vehicles()to return an array of all the vehicles associated with a users account. The response will include thevehicle vin.vehicles=user.vehicles()vehicleList=[]# Array of vehiclesforuserVehicleinvehicles:# For each user vehiclevehicleList.append(userVehicle['VIN'])Now with avehicle vinin hand, useconnectedcar.Vehicle()to get a Vehicle object representing the user's vehicle.currentVehicle=connectedcar.Vehicle(vehicleList[0],access['access_token'],"US")# Region argument is only required if you live outside the United States.Now you can ask the car to do things, or ask it for some data! For example:currentVehicle.start()Examples & DocumentationFor more examples on what you can do with Sync Connected car, see theexamplesfolder or take a peek at thedocumentation.Funding & SupportIf you are interested in supporting the development of my projects check out mypatreonorbuy me a coffee.DisclaimerTHIS CODEBASE IS NOT ENDORSED, AFFILIATED, OR ASSOCIATED WITH FORD, FOMOCO OR THE FORD MOTOR COMPANY.
Package: connectedcars
Python wrapper for the ConnectedCars REST APIConnected Cars is an AVL/data collection service installed in most newdanishvehicles from Audi, Volkswagen, Skoda and SEAT. The service has an API that is usingGraphQL. This is a thin python wrapper for that API which handles authentication and refresh of access tokens. It allows the direct execution ofGraphQLqueries, but also includes some predefined queries that returns pyton objects for convenience.Installationpip install connectedcarsData availableDepends on the car, but examples includes:gps positionfuel levelodometerUsagefromconnectedcarsimportConnectedCarsClientclient=ConnectedCarsClient(username='XXX',password='XXX',namespace='XXX')vehicles=client.vehicles_overview()forvehicleinvehicles:print(vehicle.make,vehicle.model,vehicle.fuelLevel.liter)Could output something like:Volkswagen Golf 38.0NamespacesYou will need to provide a namespace corresponding to your login. Known namespaces are:semler:minvolkswagen
Package: connectedcarscomm
Simple communication library for ConnectedCars.ioThis simple communication layer is originally created to accommodate a Home Assistant integration. Purpose is to pull current state of a vehicle connected to ConnectedCars.io (for instance with MinVolkswagen).Installationpip install connectedcarscommNamespaceConnected Cars needs a namespace to work. In my case (using MinVolkswagen) this wassemler:minvolkswagen.Supplied queriesThe package comes with three predefined queries:QUERY_CURRENT_STATEQUERY_USERQUERY_VEHICLE_VIN
Package: connected-conics
Connected ConicsA library for geometric analysis of connected conic sections.UsageInstall from PyPI
Also install matplotlib (optional) to generate a plot for this example.pipenvinstallconnected_conics
pipenvinstallmatplotlibimportyamlimportnumpyimportmatplotlib.pyplotaspltfromconnected_conicsimportconic,helpersfullspec="""- r: [8]e: [0.0]d: 6.0- r: [9]e: [0.5]d: 10.0- r: [11]e: [1.1]d: 12.0"""fullspec_dict=yaml.safe_load(fullspec)c=helpers.get_conic_from_fullspec(fullspec_dict,0)X=numpy.linspace(0,6,1000)Y=conic.find_val_vectorized(c["rs"],c["es"],c["hds"],c["offsets"],X)plt.figure()plt.plot(X,Y)plt.show()Local developmentgitcheckout...
pipenvinstall--dev
pipenvinstall-e.
pipenvshell
py.testDeploymentpythonsetup.pysdistbdist_wheel
twineuploaddist/*Copyright 2020 Innovatus Technology Pty Ltd
Package: connected-home
Connected Home -- APIPublish a new version of the api on PyPiFirst we need to install twine and bumpip install twine bumpversionThen, we use bumpversion to increment the new version. In the example below,
change the version to the current one. Choose from major, minor or patch version.cd ./api
bumpversion --current-version 0.0.1 patch setup.py connected-home/__init__.pypython setup.py sdist bdist_wheelpython -m twine upload dist/*
Package: connect-four-game
Installationpip install connect-four-gameUsageHuman vs Humanfromconnect_four_gameimportGame,Agentif__name__=='__main__':red_agent=Agent('RED TEAM','red')blue_agent=Agent('BLUE TEAM','blue')lcm=Game(red_agent,blue_agent)lcm.start_game()AI vs AIfromconnect_four_gameimportGameCOLUMN_COUNT=12# normally 7ROW_COUNT=9# normally 6if__name__=='__main__':red_rl_agent=RLAgent('red')# RLAgent is not included in the packageblue_rl_agent=RLAgent('blue')game=Game(red_rl_agent,blue_rl_agent,row_count=ROW_COUNT,column_count=COLUMN_COUNT)game.start_game()Example of an RL Agent which randomly chooses columnsimportrandomfromconnect_four_gameimportBaseAgentimportnumpyasnpclassRLAgent(BaseAgent):def__init__(self,color:str,initial_exploration_rate=0.9):self.name=f'RL-Agent-{color.capitalize()}'self.color=colorself.symbol=color[0].capitalize()defchoose_action(self):returnrandom.randint(0,len(self.game.grid[0])-1)defplace_block(self):column=self.choose_action()returnself.board.place_block(column,self.symbol)defpost_evaluation_hook(self):print('This method is called after each move has been evaluated')Human vs AIOn the way...
Package: connectify
ConnectifyConnectifyprovides convenient Python interfaces to interact with popularI/Oservices such asSlack,Facebook, databases etc.Slack servicesSupported actionsCurrently, we support the following functionalities from Slack API inside SlackConnection/AsyncSlackConnection classeschannel_conversation_history- Get channel conversion history for
given channelget_channel_latest_message- Fetch latest message from specific
channelsend_text_message- Send text type message to specific channelsend_scheduled_text_message- Schedule text message send for
specific timesend_file_message- Send file to channel[!NOTE]
Full documentation is available inreadme.mdFacebook Graph API connectionSupported actionsCurrently, we support the following functionalities from Graph API inside FacebookConnection classget_login_url- Generate login URL with given scope of permissions to get access code
and
then access token from Graph APIget_ads_related_auth_url- Generate login URL for ads related permissionsget_access_token- Get access token from the access code fetched from the steps 1 or 2get_ad_accounts- Get ad accounts existing under the access token initialize the class
with.get_ad_account_campaigns- Get campaigns under given ad accountget_ad_campaign_ad_sets- Get ad sets under given campaignget_ad_set_ads- Get ads under given ad set[!NOTE]
Full documentation is available inreadme.md
Package: connectIO
ConnectIOA simple module for sending data across the internet!Use casesMultiplayer pygame games (sending objects)Chat server with multiple clientsTransferring files over a networkAnything else you can think of!Installationpip install connectIOSimple ExampleServerfromconnectIOimportServer,threadedclassExampleServer(Server):def__init__(self,ip='127.0.0.1',port=5555):super(ExampleServer,self).__init__(ip,port)self.func=self.threaded_conn@threadeddefthreaded_conn(self,conn,addr):raddr=conn.getpeername()greeting='hello client!'print(f'Server ->{raddr}:{greeting}')self.send(conn,greeting)print(f'{raddr}-> Server:{self.recieve(conn)}')response='great you?'print(f'Server ->{raddr}:{response}')self.send(conn,response)print(f'{raddr}-> Server:{self.recieve(conn)}')if__name__=="__main__":ExampleServer().run()ClientfromconnectIOimportClientdefmain():client=Client()client.connect()resp1=client.recieve()print(f'From server:{resp1}')msg1='hi server! how are you?'client.send(msg1)print(f'To server:{msg1}')resp2=client.recieve()print(f'From server:{resp2}')msg2='great thanks!'client.send(msg2)print(f'To server:{msg2}')client.close()if__name__=="__main__":main()
Package: connectionist
ConnectionistConnectionistcontains some tools for classicalconnectionist models of readingin TensorFlow. This project is a course companion python library forContemporary neural networks for cognition and cognitive neuroscience.FeaturesReady-to-use models of reading in TensorFlowVarious "brain" (model) damaging APIsBasic building blocks (layers) for connectionist modelsRequirementsPython >=3.8TensorFlow >=2.9InstallationpipinstallconnectionistQuick startEnd-to-end toy example with Plaut, McClelland, Seidenberg and Patterson (1996), simulation 3 model:importtensorflowastffromconnectionist.dataimportToyOPfromconnectionist.modelsimportPMSPdata=ToyOP()model=PMSP(tau=0.2,h_units=10,p_units=9,c_units=5)model.compile(optimizer=tf.keras.optimizers.Adam(),loss=tf.keras.losses.BinaryCrossentropy(),)model.fit(data.x_train,data.y_train,epochs=3,batch_size=20)model(data.x_train)Documentationhttps://jasonlo.github.io/connectionist/
Package: connectionmaster
Connection MasterA command line remote server connection and management toolInstallation$ pip install connectionmasterMacOS$ echo alias cm='python3 -m connectionmaster' > ~/.bash_profileLinux$ echo alias cm='python3 -m connectionmaster' > ~/.bashrcThe config file is~/.cmrc.yamlUsageThere are long and one letter forms for every command$ cm connect myServer
$ cm c myServer
$ cm execute mySecondServer "echo hi > test.txt"
$ cm x mySecondServer "echo hi > test.txt"
$ cm scp myThirdServer localfile remotefile
$ cm scp myThirdServer localfile remotefile
$ cm ping myFourthServer
$ cm p myFourthServer
$ cm edit
$ cm eConfig filemyServer:ip:'xxx.xxx.xxx.xxx'user:'myUser'# The jump option takes the server to jump ssh throughmySecondServer:ip:'yyy.yyy.yyy.yyy'port:'2000'key:'mySecondServerKey'jump:'myServer'# The ip isn't needed when you specify a full command but is needed to ping the servermyThirdServer:ip:'zzz.zzz.zzz.zzz'command:'vncviewerzzz.zzz.zzz.zzz:0'justRunCommand:TrueConfig valuesValueMeaningDefaultcommandThe command to runsshuserThe user to log in asThe current useripThe IP to connect tolocalhostportThe port to connect to22keyThe ssh key to useNo keyjumpA server to jump ssh throughNothingjustRunCommandJust runscommandwithout adding ip, port, etcFalse
Package: connection-monitor
READMEThis package provides a long-running command to measure network performance.Following figures are recorded to a csv file:packets sentpackets receivednetwork latencystatistics from the home routerPrerequisitesPython >= 3.7, due to usage ofdataclassesandannotations;Normally handled nicely py installation procedure:bs4,click,pyyaml,psutilandrequeststhird-party libraries.InstallationThis package is hosted onPypi, so it's as easy as:pip install connection-monitorUsageCreate asecrets.ymlfile with credentials to connect to your router;
see content of sample file.Open a terminal, activate the virtualenv if appropriate, and simply type::monitorAvailable options:-s SECRETS_FILENAMEFilename to the secrets file.
Defaults tosecrets.ymlin current working folder.-o FILENAMEFilename to write the stats into.
By default, the stats go in current folder, in file named'network_data.csv'.Writing is in append mode; previous data is kept.-h HOST [HOST...]Address(es) to connect to, to measure latency.
Defaults to some French web servers.-d DELAYDelay between two samplings. Defaults to 1 minute, to behave fairly.-vVerbosity setting; use it to show measurements to command line.
Repeat it to show debug messages, i.e.-vv.
Default behavior is to show only errors and warnings.
Package: connection_pool
Thread-safe connection pool for pythonInstall$pipinstallconnection_poolExamplesCreate a poolimportmemcachefromconnection_poolimportConnectionPool# via create functiondefcreate_memcache_client():returnmemcache.Client(['127.0.0.1:11211'])pool=ConnectionPool(create=create_memcache_client,max_size=10,max_usage=10000,idle=60,ttl=120)# via lambdapool=ConnectionPool(create=lambda:memcache.Client(['127.0.0.1:11211']),max_size=10)# via functools.partialfromfunctoolsimportpartialpool=ConnectionPool(create=partial(memcache.Client,['127.0.0.1:11211']),max_size=10)# using a connectionwithpool.item()asmemcache:memcache.set('foo','bar')LicenseThe MIT License (MIT). Please see License File for more information.
Package: connection-server
connction_serverDescription.
The package connction_server is used to:
-
-InstallationUse the package managerpipto install connction_serverpipinstallconnction_serverUsagefromconnction_serverimportconnect_tcpconnect_tcp.createConnectionTCP()fromconnction_serverimportconnect_udpconnect_udp.createConnectionUDP()AuthorMilena LimoeiroLicenseMIT
Package: connectivator
ConnectivatorSet of wrapper functions to abstract repetitive tasks such as reading data from a Redshift/postgres databases, and writing their contents to a CSV or Google sheet. The lack of flexibility is done on purpose to simplify and standardize importing/exporting procedures. For more flexibility, simply use the original packages these wrapper functions were built upon, i.e. pandas, gspread, etc.Current StateConnect to Postgres / Redshift:postgres.get_engineRead output of a sql query into a data frame:transfer.read_sql_dataWrite output of SQL files to CSV:transfer.sqls_to_csvRead contents of a Google sheet into a data frame:gsheets.get_ws_dataUpdate contents of a Google sheet using a data frame:gsheets.update_wsWrite output of SQL files to Google sheet:transfer.sqls_to_gsEnvironment VariablesAdd a.envfile to the project root directory.Add the following environment variables, replacing with values relevant for your connections. Format:Postgres/Redshift Environment Variablesexport PGHOST = instance-name.abc123.us-east-1.redshift.amazonaws.com
export PGPORT = 5439
export PGUSER = your_user_name
export PGPASSWORD = your_passwordLoad the environment variables before running the script. For example, by runningsource .envin the terminal you are using to run Python scripts.Google Sheets Credient File and Environment VariableRequires a json credentials file, which can be downloaded when creating a service account within a Google developer console project. The Google Sheets API enabled also must be enabled. See:https://developers.google.com/sheets/api/quickstart/pythonAlso, the google sheet to be modified needs to give access to the Service Account. To do so, find the email associated to the service account under Credentials >> Manage Service Accounts of the Google Console project. Then go to the google sheet to be modified and allow edit access to this email.export GOOGLE_APPLICATION_CREDENTIALS="filepath/to.json"
Package: connect-markdown-renderer
CloudBlue Connect Markdown RendererIntroductionconnect-markdown-rendereris a small library that allow to render markdown documents in a terminal shell.Installconnect-markdown-renderercan be installed from pypi.org with pip:$pipinstallconnect-markdown-rendererUsage examplefromconnect.utils.terminal.markdownimportrendermy_md="""# Heading level 1 - ParagraphThis is a paragraph with inline formatting like *italic*, **strong**, ~~strikethrough~~, `inline code` and :clapping_hands: emojis!.## Heading level 2 - Lists*Ordered list:*1. First item2. Second item3. Third item**Unordered list:*** First* Second* Third### Heading level 3 - blockquote> This is a blockquote.> > ...and a nested blockquote.#### Heading level 4 - tables| Col 1 | Col 2 | Col 3 ||:------|:-----:|------:|| a | b | c |##### Heading level 5 - codeblock```pythondef this_is_my_python_function(args):return 'Hello World!'"""print(render(my_md))This code will produce the following output:Featuresconnect-markdown-rendereruses the newmarkdown-it-pyparser and supportsCommonMarkplus the following extensions:tablesstrikethroughemojiconnect-markdown-rendererusesrichto render the markdown in the terminal.Licenseconnect-markdown-rendereris released under theApache License Version 2.0.
Package: connectmon
ConnectMonA tool for monitoring tasks and connectors in Kafka Connect.InstallationWith pippip install connectmonWith poetrypoetry add connectmonUsagefromconnectmonimportenv,APIfromconnectmon.messagingimportTeamsService## Setup Kafka Connect Rest API client and check if cluster is reachableconnect=API(env.CONNECT_URL)## Get all connectors and check if any are in a failed stateconnectors=connect.get_all_connectors()## Loop through all channels and send messagesforchannelinenv.CHANNELS.channels:ifchannel.type=="teams":service=TeamsService(connect,channel)service.process_channel_connectors(connectors)service.send_message()ConfigurationBase configuration for ConnectMon is handled through Environment Variables.Environment VariablesNameTypeDescriptionDefaultCONFIG_PATHOptional stringThe path to the channel configuration file""CONNECT_URLstringThe URL of the Connect cluster"http://localhost:8083"SKIP_TLS_VERIFYbooleanWhether to skip TLS verificationFalseENVIRONMENTstringThe environment the application is running in"dev"LOG_LEVELstringThe log level for the application"INFO"LOG_FORMATstringThe log format for the application"(asctime)s - ..."Name in bold are required!IfCONFIG_PATHis provided, the application will attempt to load
configuration from the file, which will setsettings.CHANNELSwith the
channels configured in the supplied configuration file.Channel ConfigurationYou can configure specific channels to receive notifications when connectors or tasks are paused or failed.A config file could look like thischannels:-name:my-teams-team-nametype:teamsurl:https://my-org.webhook.office.com/webhookb2/...actions:-RESTART_FAILEDinclude:-i-want-to-monitor-this-connector-this-tooexclude:-who-cares-about-this-connector-this-is-someone-elses-problemSupported fields for channels are:NameTypeDescriptionDefaultnamestringName of the channel""typestringType of channel""urlstringUrl to send payload to""actionslist of stringsCan be any ofRESTART_FAILED,RESTART_FAILED_CONNECTORS,RESTART_FAILED_TASKS,RESUME_PAUSED_CONNECTORSRESTART_FAILEDincludelist of stringsNames of connectors to include for this channel["*"]excludelist of stringsNames of connectors toexcludefor this channel[]Current only Microsoft Teams ortype: "teams"is supported.
Package: connect-mongodb
PyPi-connect_mongodbConnect python with MongoDBInstallationRun below commandto install whole packagepip install connect_mongodbPrerequisitepymongo must be installed
To install pymongo, run below commandpip install pymongoImportfrom connect_mongodb import MongodbConnect to Mongodbcon=Mongodb()con.connection(connection_url, database_name=None)connection function establishes a connection with mongo serverParameters:

    ->connection_url: connection url with password
    database_name(optional): name of the databaseCreate Databasecon.create_database(database_name)create_database function helps to create a new databaseParameters:

    ->database_name: name of the databaseCheck existing Databasecon.available_database()available_database function returns list of all the existing databaseCreate Collectioncon.create_collection(collection_name)create_collection function is used to create a new collectionParameters:   

    ->collection_name: name of the collectionRecord Insertioncon.insert(collection_name, record)insert function is used to insert value in the tableParameters:  

    ->collection_name: name of the collection
    ->record: data to be inserted
        -to insert one record datatype should be dictionary
        -to insert many record datatype should be listFinding Recordscon.find(collection_name)find function is used find all the records in a collectionParameters:  

    ->collection_name: name of the collectionUpdating a Recordcon.update(self, collection_name, present_record, new_record))update function is used to alter/update the recordParameters:  

    ->collection_name: collection name
    ->present_record: existing record
        -datatype as dict
    ->new_record: new record
        -datatype as dictDeletecon.delete(collection_name,query)delete function is used to delete record from collectionParameters:  

    ->collection_name: name of the collection
    ->query: any condition in that particular record
        -datatype as dictionaryDrop a Connectioncon.drop_collection(self, collection_name)drop_collection function is used to drop the collectionParameters:  

    ->collection_name: name of the collection
Package: connectmp
ConnectMP - Taking Multi-Process Data Sharing to the moon 🚀Contribute·Community·Documentation🎫 Introduction :🍤ConnectMPis an simple, easy way to share data betweenProcessesusing DB. It's superfast, can handle big datas, can
createmultipledata connection and reallysimpleto get started. 🍰🥐ConnectMPis created out of pureFrustrationof not being able to find a good solution to comminucate between Processes 🥨🥗 Installation :via pip (recommended) :pip install connectmp🧇 Quickstart : (Docs)🥨 connectmp.ConnectionLet's see how to create a connection Object.First, import this:fromconnectmpimportConnectionYou can create your ownConnectionwith this. Let me show you how:fromconnectmpimportConnectionconnection=Connection()that's it! 🎉 You can also create multipleConnectionlike this!
pass it as an argument and use it anywhere you want 🥂Here's an example below:🌮 Example:importtimefromconnectmpimportProcess,Connectiondefdo_something(connection):connection.data="Sending Some Data."deftrack_data(connection):time.sleep(1)print(f"Track i:{connection.data}")if__name__=='__main__':conn=Connection()# Creating connectionp1=Process(target=do_something,args=(conn,))p2=Process(target=track_data,args=(conn,))p1.start()p2.start()Here we created aConnectionobject namedconn, and we created  2Process. Both of then share the sameConnectionobject so they can communicate with each other. In ourdo_somethingfunction we send the data and intrack-datafunction we get the data and print it out.That's all you needed to know aboutConnectMP. Hope this helped you 🎉🌟 Back to Top
Package: connectome
Connectome is a framework for datasets management with strong emphasis on simplicity, composability and reusability.FeaturesSelf-consistency: connectome encourages data transformations that keep entries' fields consistentCaching: transformations' caching works out of the box and supports both caching to RAM and to DiskAutomatic cache invalidation: connectome tracks all the changes made to a dataset and automatically invalidates the
cache when something changes, making sure that your cache is always consistent with the dataInvertible transformations: write consistent pre- and post- processing to build production-ready pipelinesInstallThe simplest way is to get it from PyPi:pipinstallconnectomeOr if you want to try the latest version from GitHub:gitclonehttps://github.com/neuro-ml/connectome.gitcdconnectome
pipinstall-e.# or let pip handle the cloning:pipinstallgit+https://github.com/neuro-ml/connectome.gitGetting startedThe docs are locatedhereAlso, you can check out ourIntro to connectomeseries of
tutorialshereAcknowledgementsSome parts of our automatic cache invalidation machinery vere heavily inspired by
thecloudpickleproject.