Saravanan666/pypi-org huggingface dataset:
Package: car-connector-framework
Car Connector FrameworkDevelop a connectorUse connectors/reference_connector fromcp4s-car-connectorsproject as an example of how this framework is to be used.A CAR connector project will need to extend the framework classes:BaseFullImport,BaseIncrementalImport,BaseApp,BaseAssetServerandBaseDataHandler, and implement abstract methods of those classes.Things to note:All communications with CAR digestion microservice are managed by the framework. Connector code normally does not need to access CAR digestion microservice directlyThe framework is trying to make some intelligent choice for whether to run full vs incremental import. Normally, for performance reasons we would always prefer to run incremental import if one is possible. Some examples of when it is not possible are:The model states for generating delta are not availableThe model on the CAR side is emptyThe previous incremental import session failed and a new incremental import session can potentially create a gap in the model dataBecause of the above the the connector code should properly use (throw/raise) one of three following exceptions when detecting failures:RecoverableFailureis to be used when the failure cannot potentially create a data gap and we can attempt an incremental import session when running next time. One example of a recoverable failure is a connectivity problem.UnrecoverableFailureis to be used when the failure can potentially create a data gap and we must run full import session to recover.DatasourceFailureis to be used when there is datasource API issues.RecoverableFailure and UnrecoverableFailure are mostly handled in the framework. DatasourceFailure is to be used in the connector code witha corresponding error code. Example:return ErrorCode.TRANSMISSION_AUTH_CREDENTIALS.value.
If none of the Failure classes is used, the framework will raise aGENERAL_APPLICATION_FAILURE(Unknown error) and print the error stack.For more information use guideshttps://github.com/IBM/cp4s-car-connectors/blob/develop/README.mdhttps://github.com/IBM/cp4s-car-connectors/blob/develop/guide-build-connectors.mdhttps://github.com/IBM/cp4s-car-connectors/tree/develop/connectors/reference_connectorhttps://github.com/IBM/cp4s-car-connectors/blob/develop/best-practices.mdTest deployhttps://github.com/IBM/cp4s-car-connectors/blob/develop/deployment/README.mdPublishUse the guide fromPUBLISH.md
Package: carculator-bus
carculator_busProspective environmental and economic life cycle assessment of urban and coach buses.A fully parameterized Python model developed by theTechnology Assessment groupof thePaul Scherrer Institutto perform life cycle assessments (LCA) of urban and coach buses.
Based on the Life Cycle Assessment tool for passenger vehiclescarculator.Seethe documentationfor more detail, validation, etc.The model has been the subject of a submission to the journalEnvironmental Science and Technology.
You may find a preprint versionhere.How to install?For the latest version, using conda::conda install -c romainsacchi carculator_busor for a stable release, from Pypi::pip install carculator_busNo extensive documentation right now, but check out the examples notebook.How to use it?See the notebook withexamples.SupportDo not hesitate to contact the development team atcarculator@psi.ch.MaintainersRomain SacchiChris MutelContributingSeecontributing.LicenseBSD-3-Clause. Copyright 2020 Paul Scherrer Institut.
Package: carculator-truck
carculator_truckProspective environmental and economic life cycle assessment of medium and heavy duty vehicles.A fully parameterized Python model developed by theTechnology Assessment groupof thePaul Scherrer Institutto perform life cycle assessments (LCA) of medium and heavy duty trucks.
Based on the Life Cycle Assessment tool for passenger vehiclescarculator.Seethe documentationfor more detail, validation, etc.The model has been introduced and detailed in a publication to the journalEnvironmental Science and Technology.[1] Sacchi R, Bauer C, Cox BL. Does Size Matter? The Influence of Size, Load Factor, Range Autonomy, and Application Type on the Life Cycle Assessment of Current and Future Medium and Heavy-Duty Vehicles.
Environ Sci Technol 2021.https://doi.org/10.1021/acs.est.0c07773.How to install?For the latest version, using conda::conda install -c romainsacchi carculator_truckor for a stable release, from Pypi::pip install carculator_truckWhat does it do?carculator_truckallows to model vehicles across:different conventional and alternative powertrains: diesel, compressed natural gas, hybrid-diesel, plugin hybrid, electric, fuel celldifferent gross weight cateogries: 3.5t, 7.5t, 18t, 26t, 32t, 40t and 60tdifferent fuel pathways: conventional fuels, bio-based fuels (biodiesel, biomethane), synthetic fuels
(Fischer-Tropsch-based synthetic diesel, synhtetic methane)different years: from 2000 to 2050. Technological progress at the vehicle level but also in the rest of the world energy
system (e.g., power generation) is accounted for, using energy scenario-specific IAM-coupled ecoinvent databases produced bypremise.Inventories can be imported intoBrightway2andSimaPro 9.x..The energy model ofcarculator_truckconsiders the vehicle aerodynamics, the road gradient and other factors.
    It also considers varying efficiencies of the transmission and engine at various load points for each second
    of the driving cycle.The energy model and the calculated tank-to-wheel energy consumption is validated against the simulation softwareVECTO.Benefits of hybrid powertrains are fully conidered: the possibility to recuperate braking energy as well as efficiency gains from engine
    downsizing is accounted for.Global warming potential impacts per ton-km for a 40-t truck, across different powertrain technologies,
    using an urban driving cycle.How to use it?See the notebook withexamples.SupportDo not hesitate to contact the development team atcarculator@psi.ch.MaintainersRomain SacchiChris MutelContributingSeecontributing.LicenseBSD-3-Clause. Copyright 2020 Paul Scherrer Institut.
Package: carculator-two-wheeler
carculator_two_wheelerProspective life cycle assessment of two-wheelers made blazing fast.A fully parameterized Python model developed by theTechnology Assessment groupof thePaul Scherrer Institutto perform life cycle assessments (LCA) of two-wheelers.
Builds upon the initial LCA model developed byCox et al. 2018.Seethe documentationfor more detail, validation, etc.Whycarculator_two_wheeler?carculator_two_wheelerallows yout to:producelife cycle assessment (LCA)results that include conventional midpoint impact assessment indicators as well cost indicatorscarculator_two_wheeleruses time- and energy scenario-differentiated background inventories for the future, based on outputs of Integrated Asessment ModelREMIND.calculate hot pollutant and noise emissions based on a specified driving cycleproduce error propagation analyzes (i.e., Monte Carlo) while preserving relations between inputs and outputscontrol all the parameters sensitive to the foreground model (i.e., the vehicles) but also to the background model
(i.e., supply of fuel, battery chemistry, etc.)and easily export the vehicle models as inventories to be further imported in theBrightway2LCA framework or theSimaProLCA software.carculator_two_wheelerintegrates well with theBrightway2LCA framework.Installcarculator_two_wheeleris at an early stage of development and is subject to continuous change and improvement.
Three ways of installingcarculator_two_wheelerare suggested.We recommend the installation onPython 3.7 or above.Installation of the latest version, using condacondainstall-cromainsacchicarculator_two_wheelerInstallation of a stable release (1.3.1) from Pypipipinstallcarculator_two_wheelerUsageAs a Python libraryFor more examples, seeexamples.As a Web appcarculator_two_wheelerhas agraphical user interfacefor fast comparisons of vehicles.SupportDo not hesitate to contact the development team atcarculator_two_wheeler@psi.ch.MaintainersRomain SacchiChris MutelContributingSeecontributing.LicenseBSD-3-Clause. Copyright 2020 Paul Scherrer Institut.
Package: carculator-utils
carculator_utilsBase classes and functions for the carculator package suite.
Provides base classes to:carculatorcarculator_buscarculator_truckcarculator_two-wheelerSupportDo not hesitate to contact the development team atcarculator@psi.ch.MaintainersRomain SacchiContributingSeecontributing.LicenseBSD-3-Clause. Copyright 2023 Paul Scherrer Institut.
Package: cardaio
❤️ cardaioA Python library for adjustable delays.The main use case is when you want to periodically pull something from a third-party system but you don't know exactly how often the system will have updates. For example, you're monitoring a news website for a particular topic and you want to pull news as soon as they are available but at the same time you don't want to send too many requests when there are no updates.😎 Features🐍 Pure Python.🐎 asyncio-first with synchronous API available.🛡 Fully type-annotated and type-safe.🪶 Zero-dependency.🔧 Highly configurable.☑️ 100% test coverage.📦 Installationpython3-mpipinstallcardaio🛠️ UsagefromcardaioimportHeartbeatasyncdefrun()->None:hb=Heartbeat(# never pull more often than every 1s.fastest=1,# never pull less often than every 1m.slowest=60,# start with 10s between iterations.start=10,)asyncfor_inhb:# Pull messages from a remote system.msgs=awaitpull_messages()# If there are more than 5 messages,# start pulling more often.ifmsgs>5:hb.faster()# If there are no messages,# pull less oftenelifnotmsgs:hb.slower()...# do something with messages🖨 AcknowledgmentsThe project is inspired bycardio.goGo script introduced byXe Iasoin theAnything can be a message queue if you use it wrongly enoughblog post. The implementation and API are different, though, to make it pythonic and user-friendly.
Package: cardamom
CARDAMOMCARDAMOM is an executable Gene Regulatory Network (GRN) inference method, adapted for timestamped scRNA-seq dataset. The algorithm consists in fitting the parameters of a mechanistic model of gene expression: the simulation of the model, once calibrated, allows to reproduce the dataset used for the inference. The method has been introduced in [1]. It has been benchmarked among other GRN inference tools and applied on a real dataset in [2].[1] E. Ventre. “Reverse engineering of a mechanistic model of gene expression using metastability and temporal dynamics”. In: In Silico Biology 14 (2021), pp. 89–113.[2] E. Ventre, U. Herbach et al. "One model fits all: combining inference and simulation of gene regulatory networks". In: BioRxiv (2022).
Package: cardano
This software is in development phase. Please consider it experimental and don’t rely on any
API to be stable before version 1.0 comes.There’s release 0.8.2 available. It is usable for basic payment acceptance and spending but more
advanced features haven’t been well tested yet.PrerequisitesYou need to have acardano-noderunning.You need to have acardano-walletrunning and connected to the node.The rest you find in thedocumentation.Please note it is most safe to run both node and wallet software on your local machine. The network
security is well beyond the scope of this documentation, though. Also, I strongly recommend using
the Cardanotestnetfor any software development and testing.RoadmapThis module has been started asan ideasubmitted to the Catalyst Project. The funded part
concluded with version 0.7.dateversionfeatures2021-03-160.1classes for Wallet, Address and Transactioncreate wallet from seedretrieve walletcheck balancelist historical transactionssend transfer2021-03-280.2fixed transaction APIlisting native assets (other than ADA)docs for 0.1 features2021-04-110.3add metadata to transactionsdocs for 0.2 features2021-04-180.4fee estimation2021-04-250.5stakeunstakedocs for 0.4 features2021-05-170.6UTXO statsdocs for 0.5 + 0.6 features2021-08-080.7advanced filtering of incoming and outgoing transfersnative assets transferEnd of the Catalyst-funded phase2021-11-110.8address validationfuturekey operations (HD wallet key generation)seed to key and vice versa conversioncoin selectiontransaction forgettinghandling of Byron walletsGoguen features (smart contracts?)Q:Why the roadmap above differs so much from the plan presented in Catalyst proposal?A:While developing the module I discovered that some of the features I had originally declared
were impossible to implement given the available tools. Also, the proposal lacked some features
that are important but somehow I missed them when proposing. The roadmap consists of items I found
both important and doable.DonateIf you like to support the idea with a donation, the address is:addr1qyjfql20ca09dnxrl3zdek40u24ylk6uqw3rf7p42p55xk8r26ptv047efk8fwmydynvawp4af7fr0xgjxqm7x9v32eq27496tThank you.
Package: cardano-explorer
Python Cardano ExplorerPython wrapper for accessing and processing information stored on the Cardano blockchain usingBlockfrost API.Installpipinstallcardano_explorerUsagefromcardano_explorerimportblockfrost_apiApi KeyIf you have an API key, you can either set it as environment variableBLOCKFROST_API_KEYor set it manually.cardano_mainnet=blockfrost_api.Auth()#orcardano_mainnet=blockfrost_api.Auth(api_key=api_key)Using With Proxyproxies={"http":"http://user:password@server:port","https":"https://user:password@server:portt",}cardano_mainnet=blockfrost_api.Auth(proxies=proxies)NetworkYou can specify the cardano network with the class parameternetwork.cardano_mainnet=blockfrost_api.Auth()# mainnet by default#orcardano_mainnet=blockfrost_api.Auth(network='mainnet')#orcardano_mainnet=blockfrost_api.Auth(network='testnet')QuickstartReturn detailed about the network.cardano_mainnet.network_info(){'supply': {'max': '45000000000000000',
  'total': '33206309572085375',
  'circulating': '32854605043085013',
  'locked': '11030148142156',
  'treasury': '630037263793143',
  'reserves': '11793690427914625'},
 'stake': {'live': '23374530755001598', 'active': '23395112387185878'}}DocumentationThe official documentation is hosted onGitHub.CreditBlockfrost API.DisclaimerThe project is still under development, If you find bugs or want additional features, open an issue and/or create a pull request.
Package: cardano-method
cardano-methodA fast, reliable Python library to solve cubic equations of all kinds. You can test outcardano-methodin your browser.How It Workscardano-methodimplements Gerolamo Cardano's famous method of solving cubic equations - 'Cardano's Method'. Split amongst various stages of processing, this librarymirrors the steps described in Cardano's Method.Installation$ pip install cardano-methodUsagefromcardano_methodimportCubicEquationa=CubicEquation([1,3,4,4])print(a.answers)# [(-2+0j), (-0.5+1.322875j), (-0.5-1.322875j)]Note that theanswersattribute contains a list ofcomplexobjects representing the zeroes of the cubic equation.
Package: cardanopythonlib
CardanoPythonLibCardano Python LibraryThis is a Python library to interact with Cardano Blockchain.PrerequesitesMinimum: Cardano-cli. You will be able to run offchain code, generate keys, create and sign transactions.Desired: Cardano-node running. You will be able to submit onchain and get confirmations from the blockchain.Install poetrycurl-sSLhttps://install.python-poetry.org|python3-

poetrynew<project_name>Add dependenciespoetryaddcardanopythonlibThe library relies on a cardano_config.ini file which connects to Cardano testnet by default. If you want to overwrite some of the parameters, please create a new ini file in your folder:[node]
KEYS_FILE_PATH = ./.priv/wallets
SCRIPTS_FILE_PATH = ./.priv/scripts
TRANSACTION_PATH_FILE = ./.priv/transactions
CARDANO_NETWORK = testnet
CARDANO_ERA = babbage-era
CARDANO_NETWORK_MAGIC = 2
CARDANO_CLI_PATH = cardano-cli
URL = http://localhost:8090/v2/wallets/
[logger]
LEVEL=INFOWhen using CARDANO_NETOWRK = mainnet the CARDANO_NETWORK_MAGIC is ignored.Instantiate the class as follows:fromcardanopythonlibimportbaseconfig_path='./cardano_config.ini'# Optional argumentnode=base.Node(config_path)# Or with the default ini: node = base.Node()node.query_tip_exec()Working with the librarycardanopythonlib folder is the package that contains the main functionalities.base.py file contains 3 classes.StarterNodeKeysFor usage please go to docs folder.Credits:Latest implementations related to Plutus are based on the following repo:pycardano
Package: cardberg
Cardberg Bindings for PythonPython library forCardberg's API to work with gift cards.The bindings currently allow you to:Get information on a specific cardCreate a transaction on a specific cardDetailed information of Cardberg's API can be found attheir website. If you feel like you need covering additional API methods, please open an issue or create a pull request.SetupYou can install this package by usingpip:pip install cardbergIf you fancypipenvuse:pipenv install cardbergTo install from source, run:python setup.py installFor the API client to work you would need Python 2.7+ or Python 3.4+.To install viarequirementsfile from your project, add the following for the moment before updating dependencies:git+git://github.com/palosopko/cardberg-python.git#egg=cardbergUsageFirst off, you need to require the library and set the authentication information by providing your user handle and shared secret you got from the provider.import cardberg
cardberg.api_credentials = ("partner_id", "shared_secret")Getting card informationis accomplished by callingcardberg.Card.get(). The method returns aCardobject that includesid,name,surname,statusand availablecreditsandpoints. On thisCardobject we maycreate a transaction(whether positive or negative) by callingcreate_transaction()method with transaction type, decimal value of the transaction and optional bill ID for further reference.Possible transaction types are either "credits" or "points" depending on what budget do we use. If you want to make a debit (for example your user is buying something) then provide a negative value.Example:import cardberg
from decimal import Decimal

cardberg.api_credentials = ("partner_id", "shared_secret")

card = cardberg.Card.get("D1nd17h")

card.create_transaction(
    cardberg.Card.TRANSACTION_CREDITS,
    Decimal("-1.00")
)ContributingCheck for open issues or open a new issue for a feature request or a bug.Fork the repository and make your changes to the master branch (or branch off of it).Send a pull request.DevelopmentRun all tests on all supported Python versions:make testRun the linter with:make lintThe client library uses Black for code formatting. Code must be formatted with Black before PRs are submitted. Run the formatter with:make fmtChangelogv0.2.0: 03/10/2019Python 3 compatibility, code formatting covered by Black and various small fixes and formal changes to make everything better.v0.1.1: 21/03/2016Added rounding to two decimal places on credits and points returned from Cardberg's API.v0.1.0: 14/03/2016Initial version with support forcard_infoandcreate_transactionAPI methods.
Package: cardbin
开源协议: MIT license在线文档:https://cardbin.readthedocs.io.安装$pipinstallcardbin使用fromcardbin.cardbinimportvalidvalid('6222600260001072444')Out:{'bank':'交通银行','type':'太平洋借记卡'}部署[docker]$cddocker$docker-composeup功能完全支持 python3。 python2 将不再支持。检查一个银行卡号是哪个银行，什么卡。支持 zeromq 和 http 方式访问。docker 部署独立服务。CreditsHistory0.1.0 (2017-09-22)First release on PyPI.
Package: card-cli
Configured ReadersACS ACR122U (rfid)pcProx RDR-7582AKU (rfid)Usagevia command line:::
card-cli –device-vendor-id 0x03f0 –device-product-id 0x2724card-cli is a command line utility for reading smartcards and magnetic stripe cards.–device-vendor-id (-dvid) and –device-product-id (-dpid) is only required to connect msr device.
Package: card-data-parsers
fyle-bank-feed-parser-sdk-pyBank feeds parsers collection.UsageTo use the VCF parser, use the VCFParser class:from card_data_parsers import VCFParser, ParserError

try:
  with open(dir + '/input.vcf') as input_file:
    result = VCFParser.parse(
      file_obj=input_file,
      account_number_mask_begin=4,
      account_number_mask_end=4
    )
  print(result)
except ParserError as e:
  print(f'Omg! error {e}')Similarly, you can use AmexParser, CDFParser, S3DFParser and HappayParser for the right file types.Developmentpip install -r requirements.txtCheck implemented parsers for examples.Run testschmod +x test.sh

./test.shReleasing a new versionTo release new version to Pypi:Create a new releasehere.
ORCreate a git tag and push that tag(after merge) with version number like:git tag v0.1.0
git push origin v0.1.0Versioning semanticsThe parse method is supposed to return a list of transactions. This is a list of python dict objects that looks like this:[{"bank_name": "Test BANK", "vendor": "Test", "sync_type": "BANK FEED - VCF", "transaction_type": "debit", "currency": "EUR", "amount": "124.74", "transaction_date": "2018-11-30T10:00:00.000000Z", "account_number": "4142********6333", "transaction_dt": "2018-11-30T10:00:00.000000Z", "external_id": "b2a242d1d9814394b594044b77f36f2f"}]If there is any non-backward compatible change to this structure e.g. a key is deleted, then bump up major number. Otherwise, bump up minor number.
Package: carddeck
Poker Hands from a Deck of CardsAn object model of cards is loaded into a deck, that is shuffled and dealt out as hands is packaged in the python modulecarddeck.Carddeck is composed of four python classes: 1) cards, 2) deck, 3) pokerhands, and 4) errors.  It is a concept used to exercise python setup, coding, documenting and packaging for pypi distribution.Install from PyPipip install carddeckExampleThe defult of five cards in four hands are dealt after creating a deck, and shuffling it.from carddeck.deck import deck
myD = deck()
myD.shuffle()
myD.deal()
myD.printHands()
--------
Outout:
['Q♥', '8♥', 'A♥', '2♠', '7♣'] 
['4♣', 'K♣', 'J♦', 'J♠', '4♦'] 
['4♥', '7♦', '9♥', '2♦', '10♠'] 
['2♥', 'Q♣', 'A♦', 'J♣', '5♦']The four hands are evaluated using the PokerHands object.from carddeck.pokerhands import PokerHands
myPH = PokerHands()
for i in range(0,len(myD.hands)):
    theCards = myD.hands[i]
    theHand = myPH.getHand(theCards)
    print(theCards, theHand)
--------
['Q♥', '8♥', 'A♥', '2♠', '7♣'] High card: A
['4♣', 'K♣', 'J♦', 'J♠', '4♦'] Two pair: 4 and J
['4♥', '7♦', '9♥', '2♦', '10♠'] High card: 10
['2♥', 'Q♣', 'A♦', 'J♣', '5♦'] High card: AThe next deal from the remaining cards can then be evaluated.myD.deal()
for i in range(0,len(myD.hands)):
    theCards = myD.hands[i]
    theHand = myPH.getHand(theCards)
    print(theCards, theHand)
--------
['Q♥', '8♥', 'A♥', '2♠', '7♣'] High card: A
['4♣', 'K♣', 'J♦', 'J♠', '4♦'] Two pair: 4 and J
['4♥', '7♦', '9♥', '2♦', '10♠'] High card: 10
['2♥', 'Q♣', 'A♦', 'J♣', '5♦'] High card: A
Package: card-deck
Card DeckAn object model of a pack of cards, written in Python, to streamline writing programs to model/play games involving cardsDocumentationDocumentation for the library can be found here:https://card-deck.readthedocs.io/en/latest/InstallationThe library has been published on PyPi, so can be found here:https://pypi.org/project/card-deck/, and can be installed as follows:pip install card-deckRequirementsThere are no additional required libraries