Saravanan666/pypi-org huggingface dataset:
Package: cpeparser
Cpe-parserThe cpe-parser libray is a parses for CPE value. This cpe value can be either uri biding cpe or formatted binding value.🔨  Installation$pipinstallcpe-parserGuidefromcpeparserimportCpeParsercpe=CpeParser()result=cpe.parser("cpe:2.3:a:ipython:ipython:*:*:*:*:*:*:*:*")print(result){'part':'a','vendor':'ipython','product':'ipython','version':'*','update':'*','edition':'*','language':'*','sw_edition':'*','target_sw':'*','target_hw':'*','other':'*'}Default values are returned as asterisks '*' that represent ANY.NIST DocumentationThis library follows the guidelines outline here:https://nvlpubs.nist.gov/nistpubs/Legacy/IR/nistir7695.pdf
Package: cpf
Checa e gera CPFs de acordo com o padrÃ£o brasileiro. Disponivel tambem funcoes como a de checar a regiao do CPF e tambem gerar com regioes especificas. Acessehttps://github.com/pedrokpp/gerador-e-checker-de-cpfpara mais informacoes.ChangeLogs2.0 (20/01/2021)> Released as a package
Package: cpfeed
cpfeed (Crypto Price Feed)cpfeed(short for Crypto Price Feed) is a Python package for fetching cryptocurrency prices from theCoinGecko API.RequirementsBefore installing and using this project, make sure you have the following dependencies installed:Python(>= 3.9).Poetry.InstallationYou can installcpfeedvia pip:pip3installcpfeedUsageNavigate to thecpfeeddirectory:cdcpfeedActivate the Poetry shell:poetryshellFetch cryptocurrency prices:cpfeedbitcoinLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.TODOUpdate usage examples with more detailed explanations.Add troubleshooting section for common issues.Add contributing guidelines.Add more commands.
Package: cpf-generator
CPF-GeneratorInstalling$pipinstallcpf-generatorUsagefromcpf_generatorimportCPFcpf=CPF.generate()# Will generate a random CPF # EX: 46064927240formatedCpf=CPF.format(cpf)# Will format the CPF to be printed in some place # EX: 460.649.272-40CPF.unformat(formatedCPF)# Works with formated CPF # EX: 46064927240CPF.validate(cpf)# Will return True if the CPF is valid or False if not # EX: TrueCPF.validate(formatedCPF)# Works with formated CPF # EX: TruebyMatheus AlmeidaWork with braziian CPF easilyMIT License
Package: cpfinder
Welcome to CPFinder 👋Online changepoint detection for python🏠Homepage✨DemoInstallpipinstallcpfinderUsagepythonexample.py---# Import libraries>>fromcpfinderimportcpfinder
>>fromcpfinder.datasetsimportgenerate_normal_time_series# Example data>>data=generate_normal_time_series(3)# Changepoint Detection>>detector=cpfinder(data=data,method="bocpd")>>detector.fit(animationFlag=True,interval=100,plotFlag=False)>>print(detector.changepoints)'''[171, 1538]'''# You can save animation as gif>>detector.saveAnimationVideo("assets/animation.gif")Run testspythontestsAuthor👤I.Onur SerbetciWebsite: kaggle.com/onurserbetciTwitter:@iborahamGithub:@iborahamLinkedIn:@ionur-serbetci🤝 ContributingContributions, issues and feature requests are welcome!Feel free to checkissues page.Show your supportGive a ⭐️ if this project helped you!📝 LicenseCopyright © 2021I.Onur Serbetci.This project isMITlicensed.This README was generated with ❤️ byreadme-md-generator
Package: cpflow
CPFlowImplementation of the synthesis algorithms for quantum circuits described in ... Distributed under the MIT licence.InstallationCPFlowis available viapip.  It is highly recommended to install the package in a new virtual environment.pipinstallcpflowA feature that allows to decompose sythesized circuits into Clifford+T basis requires yet experimentalqiskitbranch that can be installed throughpipinstallgit+https://github.com/LNoorl/qiskit-terra@d2e0dc1185ccc3b0c9957e3d7d9bc610dede29d4Basic exampleDecomposing the CCZ gate with linear qubit connectivity 0-1-2. Can be executed in python console but intended for use with Jupyter notebooks.importnumpyasnpfromcpflowimport*u_target=np.diag([1,1,1,1,1,1,1,-1])# CCZ gatelayer=[[0,1],[1,2]]# Linear connectivitydecomposer=Synthesize(layer,target_unitary=u_target,label='ccz_chain')options=StaticOptions(num_cp_gates=12,accepted_num_cz_gates=10,num_samples=10)results=decomposer.static(options)d=results.decompositions[3]# This turned out to be the best decomposition.d.refine()print(d)d.circuit.draw()Output:<ccz_chain|Rational|loss:1.1920928955078125e-07|CZcount:8|CZdepth:8>More featuresFor further examples we encourage to explore atutorial notebookinteractively. For motivation and background see the original paper link_to_paper.
Package: cpf-tools
cpf_toolsBiblioteca com ferramentas para auxiliar na checagem, formatação etc. de CPFs.Instalação:$ pip install cpf-toolsFunções:cpf-tools.cpf_int_validation(cpf: int) -> bool:Essa função recebe um valor inteiro e verifica se o digito verificador do CPF é verdadeiro (True) ou falso (False).cpf-tools.cpf_str_validation(cpf: str) -> bool:Essa função recebe um valor string e verifica se o digito verificador do CPF é verdadeiro (True) ou falso (False).cpf-tools.cpf_format(cpf: Union[int, str, float]) -> str:Essa função recebe um valor integer, string ou float de um CPF e retorna uma string formatada.
(Exemplo: "00000000000" -> "000.000.000-00")Usando com Pandas:Para utilizar com a biblioteca Pandas, utilize o seguinte comando:df['your-cpf-field'].apply(cpf-tools.cpf_int_validation)oudf['your-cpf-field'].apply(cpf-tools.cpf_str_validation)oudf['your-cpf-field'].apply(cpf-tools.cpf_format)
Package: cpfValidationTool
cpfValidationToolThe package cpfValidationTool is used to:- Validate a CPF using the verification digits of it;- Return if given CPF string is either valid or not.InstallationUse the package managerpipto install cpfValidationToolpipinstallcpfValidationToolUsagefromcpfValidationTool.cpfValidationimportcpfcpf.validateCPF(cpfStr)AuthorRodrigo MeloLicenseMIT
Package: cpg
CPG (stands for configurable playlist generator) is a command line application
for recursively scanning a directory for audio files and outputing a playlist
file.  Several ways of influencing the ordering of tracks in the generated
playlist are available.  The generated playlist’s file format and the type of
its paths (absolute or relative) can be configured too.Quick feature overview:recursively scans a directory for audio filessorts the found tracks into groups according to tag informationit can be configured based on which tags to create groupsgroups can be additionally split up based on configurable criteriatracks can be blacklisted in a configurable waythe resulted groups are randomly shuffledordering of tracks withing groups is configurableresulted playlists can be exported into various playlist formatsFor more information regarding installation and usage please refer to thedocumentation.
Package: cpg2code
EnhancedPHPJoern Framework Cpg2Code LibraryIntroductionThis framework helps you convert code from CodePropertyGraph of PHP.Installationinstall from pip (not public yet)pip3installcpg2codeinstall from source codegitclonepythonsetup.pyinstallUsagefromenhanced_phpjoern_frameworkimportNeo4jEnginefromenhanced_phpjoern_framework.graph_traversalimportControlGraphForwardTraversalfromcpg2code.cpg2code_factoryimportCpg2CodeFactory# WordPress-5.7.1/wp-load.phpneo4j_engine=Neo4jEngine.from_dict(NEO4J_DEFAULT_CONFIG)traversal_entity=ControlGraphForwardTraversal(neo4j_engine)file=neo4j_engine.get_file_name_belong_node('wp-load.php')x=neo4j_engine.get_ast_child_node(neo4j_engine.get_ast_child_node(file))origin=neo4j_engine.get_ast_root_node(x)traversal_entity.origin=[origin]traversal_entity.run()rec=traversal_entity.get_record()# type:nx.DiGraphrec_list=[kfork,pinrec.nodes.items()]result=Cpg2CodeFactory.extract_code(neo4j_engine,rec_list)print(result)The compare of result can be seen as follows(the left is the source file and the right is the generated file)ChangelogSee[CHANGES.md](https://github.com/ninthDevilHAUNSTER/code2cpg/blob/master/CHANGES.md)AuthorsSee[AUTHORS.md](https://github.com/ninthDevilHAUNSTER/code2cpg/blob/master/AUTHORS.md)LicenseSee[LICENSE.txt](https://github.com/ninthDevilHAUNSTER/code2cpg/blob/master/LICENSE.txt)
Package: cpgame
cpgameSimple "game" framework forCircuitPythonembedded hardware.Overviewcpgame provides a number of decorators and a simple event loop to provide a responsive
framework for building applications and games on embedded hardware using CircuitPython.Run a function on every "tick" of the event loop:fromcpgameimportstart,tick@tickdefloop(now):...start()Blink a Neopixel every second:@every(1)defperiodic(now):pixel[0]=REDifint(now)%2elseOFFTurn a Neopixel on and off with a button press:@on(board.BUTTON_A,DOWN)defpressed(now):pixel[0]=RED@on(board.BUTTON_A,UP)defreleased(now):pixel[0]=OFFCreate or reset timers:@on(board.BUTTON_A)defflood(now):pixels.fill(random.choice(COLORS))delay=random.randint(20,100)/10# between 2 and 10 secondsafter(delay,flood)@on(board.BUTTON_B)defhalt(now):cancel(flood)Play sound:enable_speaker()middle_a=sample(440)@on(board.BUTTON_A)defnoise(now):play_sound(middle_a,1)RoadmapSupport more boards than Circuit Playground Express.Add helper functions for animating NeoPixels, raw audio clips, and LCD displays.Licenseaiosqlite is copyrightJohn Reese, and licensed under the
MIT license.  I am providing code in this repository to you under an open source
license.  This is my personal repository; the license you receive to my code
is from me and not from my employer. See theLICENSEfile for details.
Package: cpgclientlib
A Python client library for Joern ServerA library to script code analyzers based on code property graphs
(e.g., Joern/Ocular) via Python.RequirementsPython 3 and Pip(we donotsupport Python 2.7)
see (https://packaging.python.org/tutorials/installing-packages/)InstallationTo download and install the latest version, issue the following command:pip install cpgclientlibUsageAssuming thatjoerndis running locally, the following short scripts creates a code property graph and runs a query to retrieve all methods in JSON format.#!/usr/bin/env python3fromcpgclient.CpgClientimportCpgClientserver='127.0.0.1'port=8080client=CpgClient(server,port)client.create_cpg('/path/to/cpg')methods=client.query('cpg.method.toJson')print(methods)ReferencesCode Property Graph specification and toolshttps://github.com/ShiftLeftSecurity/codepropertygraph/The open source code analyze joern:https://joern.ioShiftLeft Ocular:https://ocular.shiftleft.io
Package: cpgdata
Cell painting gallery data handling and validationGetting startedInstallcpgdatapackagepipinstallcpgdataSync pre-generated index filescpgindexsync-o"path to save index files"Example of using the index for filtering files to download from the Cell painting galleryfrompathlibimportPathfrompprintimportpprintimportpolarsasplfromcpgdata.utilsimportdownload_s3_files,parallelindex_dir=Path("path to dir containing index files")index_files=[fileforfileinindex_dir.glob("*.parquet")]df=pl.scan_parquet(index_files)df=(df.filter(pl.col("dataset_id").eq("cpg0016-jump")).filter(pl.col("source_id").eq("source_4")).filter(pl.col("leaf_node").str.contains("Cells.csv")).select(pl.col("key")).collect())# print first 10 resultspprint(df.to_dicts()[0:10])# Download filtered filesdownload_keys=list(df.to_dict()["key"])parallel(download_keys,download_s3_files,["cellpainting-gallery",Path("path to save downloaded files")],jobs=20)
Package: cpg-gnomad
Hail utilities for gnomADThis repo contains a number ofHailutility functions and scripts for thegnomAD projectand theTranslational Genomics Group. As we continue to expand the size of our datasets, we are constantly seeking to find ways to reduce the complexity of our workflows and to make these functions more generic. As a result, the interface for many of these functions will change over time as we generalize their implementation for more flexible use within our scripts. We are also continously adapting our code to regular changes in the Hail interface. These repos thus represent only a snapshot of the gnomAD code base and are shared without guarantees or warranties.We therefore encourage users to browse through theAPI referenceto identify modules and functions that will be useful in their own pipelines, and to edit and reconfigure relevant code to suit their particular analysis and QC needs.
Package: cpg-hail
HailHailis an open-source, general-purpose, Python-based data analysis tool with additional data types and methods for working with genomic data.Hail is built to scale and has first-class support for multi-dimensional structured data, like the genomic data in a genome-wide association study (GWAS).Hail is exposed as a Python library, using primitives for distributed queries and linear algebra implemented in Scala,Spark, and increasingly C++.See thedocumentationfor more info on using Hail.CommunityHail has been widely adopted in academia and industry, including as the analysis platform for thegenome aggregation databaseandUK Biobank rapid GWAS. Learn more aboutHail-powered science.ContributeIf you'd like to discuss or contribute to the development of methods or infrastructure, please:see theFor Software Developerssection of the installation guide for info on compiling Hailchat with us about development in ourZulip chatroomvisit theDevelopment Forumfor longer-form discussionsHail uses a continuous deployment approach to software development, which means we frequently add new features. We update users about changes to Hail via theDiscussion Forum. We recommend creating an account on the Discussion Forum so that you can subscribe to these updates as well.MaintainerHail is maintained by a team in theNeale labat theStanley Center for Psychiatric Researchof theBroad Institute of MIT and Harvardand theAnalytic and Translational Genetics UnitofMassachusetts General Hospital.Contact the Hail team athail@broadinstitute.org.Citing HailIf you use Hail for published work, please cite the software. You can get a
citation for the version of Hail you installed by executing:importhailashlprint(hl.citation())Which will look like:Hail Team. Hail 0.2.13-81ab564db2b4. https://github.com/hail-is/hail/releases/tag/0.2.13.AcknowledgementsThe Hail team has several sources of funding at the Broad Institute:The Stanley Center for Psychiatric Research, which together with Neale Lab has provided an incredibly supportive and stimulating home.Principal Investigators Benjamin Neale and Daniel MacArthur, whose scientific leadership has been essential for solving the right problems.Jeremy Wertheimer, whose strategic advice and generous philanthropy have been essential for growing the impact of Hail.We are grateful for generous support from:The National Institute of Diabetes and Digestive and Kidney DiseasesThe National Institute of Mental HealthThe National Human Genome Research InstituteThe Chan Zuckerberg InitiativeWe would like to thankZulipfor supporting
open-source by providing free hosting, and YourKit, LLC for generously providing
free licenses forYourKit Java
Profilerfor open-source development.
Package: cpghstore
Info:cpghstore is a fast decoder/encoder of the PostgreSQL hstore
data type (http://www.postgresql.org/docs/9.0/static/hstore.html).Author:Robert Kajic (http://github.com/kajic)Aboutcpghstore is written in C and is meant to be a faster alternative topghstore. Runpython setup.py test-sbenchmarkto see how it performs (requires pghstore to be installed if
you want comparisons to be shown).InstallationRunpip install cpghstoreor download the
source fromGitHuband runpython setup.py install.UsageDecode and encode hstore string:>>> import cpghstore
>>> # decode
>>> d = cpghstore.loads('"name"=>"Norge/Noreg"')
>>> d
{'name': 'Norge/Noreg'}
>>> # encode
>>> s = cpghstore.dumps(d)
>>> s
'"name"=>"Norge/Noreg"'TestsTo run the test suite, runpython setup.py test.BenchmarkRunpython setup.py test-sbenchmarkto see how it performs (requires
pghstore to be installed if you want comparisons to be shown).LimitationsSupports only str strings (i.e. not unicode strings).
Can only decode (loads) string values, i.e. “key”=>”123” is supported while
“key”=>123 is not.
Package: cpgqls-client
cpgqls-client-pythoncpgqls-client-pythonis a simple Python library for communicating with an instance of
a Code Property Graph Query Language server.RequirementsPython 3.7+Installationpip install cpgqls-clientExample usageA prerequisite for the following example is access to a running instance of a
Code Property Graph server. An easy way to set one up is by using the open
source code analyzerjoern:$./joern--serverfromcpgqls_clientimportCPGQLSClient,import_code_query,workspace_queryserver_endpoint="localhost:8080"basic_auth_credentials=("username","password")client=CPGQLSClient(server_endpoint,auth_credentials=basic_auth_credentials)# execute a simple CPGQueryquery="val a = 1"result=client.execute(query)print(result)# execute a `workspace` CPGQueryquery=workspace_query()result=client.execute(query)print(result['stdout'])# execute an `importCode` CPGQueryquery=import_code_query("/home/user/code/x42/c","my-c-project")result=client.execute(query)print(result['stdout'])query=import_code_query("/home/user/code/x42/java/X42.jar","my-java-project")result=client.execute(query)print(result['stdout'])Running the test suite# set up the virtual environment$python-mvenv.venv
$..venv/bin/activate# install the dependencies$pipinstall-rrequirements.txt
$pipinstall-rrequirements-tests.txt# run the tests$python-mpytestReferencesCode Property Graph specification and toolshttps://github.com/ShiftLeftSecurity/codepropertygraph/The open source code analyzer joern:https://joern.io
Package: cpg-scpi
cpg_scpiEducational client library to use Adafruit Circuit Playground (CPG) or BBC micro:bit via SCPI protocol in Python3.DescriptionThe corresponding Arduino Sketch for the Circuit Playground can be foundhere.The corresponding MicroPython source code for the BBC micro:bit will be published in future.... more docu to come ...DevelopmentBuild pypi packageTools needed to build and publish to PyPi under Windows:python -m pip install --upgrade build
python -m pip install --upgrade twineTools needed to build and publish to PyPi Linux/MacOS:python3 -m pip install --upgrade build
python3 -m pip install --upgrade twineBuild package:In project root folder:python -m buildUpload package to pypi:In project root folder:twine upload --skip-existing dist/*Alternatively, before uploading, delete outdated build artifacts in thedistfolder, such that only the latest build files are uploaded:twine upload dist/*Test pypi packageCreate a test folder (e.g.tests) and cd into it.Create virtual environment:Windows:python -m venv .venvLinux/MacOS:python3 -m venv .venvActivate virtual environment:Windows CMD:.venv\Scripts\activate.batLinux/MacOS:source .venv/bin/activateInstall packageWindows CMD:pip3 install cpg-scpiLinux/MacOS:pip3 install cpg-scpi
Package: cpg-utils
cpg-utilsThis is a Python library containing convenience functions that are specific to the CPG.In order to install the library in a conda environment, run:condainstall-ccpgcpg-utilsTo use the library, import functions like this:fromcpg_utils.cloudimportemail_from_id_token_email_string=email_from_id_token(id_token_jwt='TOKEN_STRING')We usebumpversionfor incrementing the library's semantic version. A new conda package gets published automatically in thecpgconda channel whenever a version bump commit is merged with themainbranch.ContentsMethods to facilitate cloud computingHelper functions for Hail Batch jobsCloning git repositories inside Hail Batch jobs
Package: cpg-utils-ms
cpg-utils-msThis is a Python library containing convenience functions that are specific to the CPG.In order to install the library, run:pipinstallcpg-utils-msTo use the library, import functions like this:fromcpg_utils.cloudimportemail_from_id_token_email_string=email_from_id_token(id_token_jwt='TOKEN_STRING')We usebumpversionfor incrementing the library's semantic version. A new conda package gets published automatically in thecpgconda channel whenever a version bump commit is merged with themainbranch.ContentsMethods to facilitate cloud computingHelper functions for Hail Batch jobsCloning git repositories inside Hail Batch jobs
Package: cpgw
COOPER Control ToolThis is the Gateway tool for cooper dongleInstallationYou can installcpgwdirectly from PyPI:sudopip3install-UcpgwNote: You may need to usesudobefore the command - it depends on the operating system used...ConfigurationInsert this snippet to the file /etc/cooper/cpgw.yml:device: /dev/ttyUSB0
zmq:
  publisher:
    host: 127.0.0.1
    port: 5680
  dispatcher:
    host: 127.0.0.1
    port: 5681Usagecpgw-c/etc/cooper/cpgw.ymlStart using systemdInsert this snippet to the file/etc/systemd/system/cpgw.service:[Unit]
Description=COOPER cpgw
After=network.target

[Service]
Type=simple
User=pi
ExecStart=/usr/local/bin/cpgw -c /etc/cooper/cpgw.yml
Restart=always
RestartSec=5
StartLimitIntervalSec=0

[Install]
WantedBy=multi-user.targetStart the service:systemctl start cpgw.serviceEnable the service start on boot:systemctl enable cpgw.serviceView the service log:journalctl -u cpgw.service -fStart using PM2pm2start`whichpython3`--name"cpgw"--`whichcpgw`-c/etc/cooper/cpgw.ymlUsage as Python modulefromcpgw.gatewayimportGatewaydefon_recv(payload):print(payload)gw=Gateway("/dev/ttyUSB0")gw.on_recv=on_recvgw.run()LicenseThis project is licensed under theMIT License- see theLICENSEfile for details.
Package: cpie
cpieClustering-based Promising Individual Enclosre(CPIE) in Python.CPIE is an evolutionary computation algorithm which aims to search best parameters minimizing black-box function, especially UV-function.UV-function has U-valley, which occupies large search space but  contains only local optima, and V-valley, which occupies small search space but contains global optima.e.g. Double-sphere: f(x) = min((x-2)^2 + 0.1, 10*(x+2)^2)Installation$ pip install cpieCPIE depends on numpy, so please install numpy if needed.Example of usageFirst, you can import CPIE module as below.fromcpieimportCPieThen, you need to prepare objective function to be minimized.defsphere(x):returnsum(xi*xiforxiinx)defobjective_func(x):returnmin(sphere(x-2)+0.1,10*sphere(x+2))You can minimize objective function like below.dimension=2bounds_min=[-10.0]*dimensionbounds_max=[10.0]*dimensioncpie=CPie(bounds_min,bounds_max,Ns=7*dimension)foriinrange(2000):solution=cpie.sample()f_value=objective_func(solution)cpie.update(f_value)cpie.print()"bounds_min" and "bounds_max" means search space.CPIE starts optimization with Ns solutions sampled unimormally in the search space.After optimization loop, you can get optimized solution.print("global best x",cpie.best.x)print("global best f",cpie.best.f)CPIE is niching algorithm, so you can also get best solutions from each mode.bests=cpie.get_bests()fori,binenumerate(bests):print("mode",i," f",b.f)example_main.py shows full example code.
Package: cpilib
CPI-LibPython Library to handle CPI data.InstallationpipinstallcpilibUsagefromcpilibimportHICP# Will fetch new data if the data from cache (if any)# is older than 1 week (time_limit in days)hicp=HICP.from_cache(time_limit=7)# COICOP codenode='CP01'# Country codecountry='DE'# DataFrame of the prices of the children of (node, country)price_children=hicp.prices.children(node,country)# DataFrame of the weights of the children of (node, country)weight_children=hicp.weights.children(node,country)# Series of the weights of (node, country)total_weight=hicp.weights.node(node,country)# Yearly (int)
Package: cpimgs
#cpimgs1. Installationpip install cpimgs2. InstructionUse this package to check drug corresponding plates and wells. \The output is a python dictionary. \The dict can be viewed in console byprintfunction.\The plates and wells info can be viewed in console as well.Images will be shown automatically by functiongetPlotsByNameAndChn3. NoteThe package is still actively under dev. The data is far from curated. Chemical names is a huge problem.4. DisclaimerThe DMSO plots shown are random DMSO plots in the same well from the drug, not necessarily the representative ones.