Saravanan666/pypi-org huggingface dataset:
Package: pnk
pnkProduce a new combination of subdomains - generates permutations P(n,k).Make it workswap subs themselves: web.test.domain.com -> test.web.domain.comincrease/decrease digits on subs with numbers: test1.domain.com -> test2.domain.comMake it rightstdin: reads standard input. This is useful for capturing a pipeline’s contents at an intermediate stage of processing.stdout: supports standard output.Works with the next arguments:usage: pnk [-h] [-i | -c] [-d] [-v] [FILE ...]

Generates a new subdomains on provided input

positional arguments:
  FILE             list of subdomains/hosts to process

optional arguments:
  -h, --help       show this help message and exit
  -i, --increment  increment any one or two digits on subdomains
  -c, --cartesian  increment digits on subdomains creating their Cartesian product
  -d, --data       generate test data from the stdin
  -v, --version    show program's version number and exit$catsubs1.txtsubs2.txt|pnkMake it fastTo turn this single process script into multiprocess useinterlaceFeaturesPermutations:echo"aws3-3.s11.env2.tesla.com"|pnk
aws3-3.s11.env2.tesla.com
aws3-3.env2.s11.tesla.com
s11.aws3-3.env2.tesla.com
s11.env2.aws3-3.tesla.com
env2.aws3-3.s11.tesla.com
env2.s11.aws3-3.tesla.comWith incrementation option:echo"aws3-3.s11.env2.tesla.com"|pnk-i
aws0-3.s11.env2.tesla.com
aws1-3.s11.env2.tesla.com
aws2-3.s11.env2.tesla.com
...
aws8-8.s11.env2.tesla.com
aws9-9.s11.env2.tesla.com
...Using the cartesian product option:echo"v0.1-v2.tcsbank.ru"|pnk-c
v0.dev0-v0.tcsbank.ru
v1.dev0-v0.tcsbank.ru
...
v0.dev1-v0.tcsbank.ru
v0.dev2-v0.tcsbank.ru
...
v9.dev9-v8.tcsbank.ru
v9.dev9-v9.tcsbank.ruInstall & UsagePyPi:pip3install--no-depspnkFrom the source code:$catsubs1.txtsubs2.txt|./src/pnk/__main__.pyConstraintsLimitationsDoes't handle increment option in the following cases: more then two digits:v123.tesla.com -> v123.tesla.com
aws.1002030v.amazon.com -> aws.1002030v.amazon.comDoes not ships with DNS resolver, usemassdns$pnk<list.txt|massdns-Possible out of memory issues when redirectingstdoutto a file, please read theWikiFeature requestSee the openissuefor the following feature requests:replace keywords with word in wordlist: v2.test.domain.com -> v2.stage.domain.com .. v2.prod.domain.comprepend/append word by creating new subs: test.domain.com -> demo.test.domain.com, test.demo.domain.com
Package: pnlogger
nanuaddnumb1Node.js module that generates a nanu-addnumb1.Uses crypto.randomBytes when available, falls back to unsafe methods for node.js <= 0.4.To increase performance, random bytes are buffered to minimize the number of synchronous calls to crypto.randomBytes.Installation$ npm install nanu-addnumb1Usagevar add = require('nanu-addnumb1');console.log(add(1,3,343,4,34,345));
Package: pnmac-aws-common
# PNMAC Secrets Manager Utility[[_TOC_]]## To Installpip install pnmac-aws-common## Secrets ManagerUsed to fetch AWS secrets per the company’s pattern for secrets management.This code expects that a “secrets_path” is either provided to the function or established as an environment variable called “secrets_path”.It will try to get your secrets from manager.Should you not have access to the secrets manager (for example, while during local development) environment variables are returned in that case. So simply add your secrets as environment variables to accomodate this case.## SQSA set of utilities to chunk data for queues. TODO: add a bit of logic that manages on body size to make sure we don’t hit the 256kb limits.## Packagingpython setup.py bdist_wheelpython -m twine upload dist/*
Package: pnmac-common
# PNMAC Common UtilitiesA set of utilities to be (re)used## Installationpip install pnmac_common## ToolkitConfig Reader - A small utility to read in application settings from YAML config files prefixed with environments.
- Per current SOP, these configs should be dev, stg, qa, prdFormatter - A set of utility functions to help safely transform data typesScrubber - A set of utility functions to help scrub PII and NPI out for use with logging.## Packagingpython setup.py bdist_wheelpython -m twine upload dist/*
Package: pnmap
pnmapA simplified Python implementation of nmap (network mapper/port scanner).FeaturesQuick and easy multi-target subnet scans.Single target external scans.UDP and TCP scans.Oftenfasterthan nmap for simple scans, due to program simplicity.Installationpip3 install pnmapUsageFor quick subnet scans:sudo pnmapFor additional configuration, utilize help menu:pnmap --helpSeeoutputsfor specific use cases.Additional InformationAs pnmap utilizes packet injection, it must be run as sudoDue to network briding, pnmap does not work deterministically inside VM's (e.g. Virtualbox, VMWare)Results which return as "closed" implies the port returned a responseResults which return "filtered" are due to connection issues or are filtered by a firewall.ScreenshotsSubnet ScanHelp
Package: pnnl-atlas
ATLASDocumentationInstallAll dependencies are installed viacondausing thebiocondachannel. The
workflow and some dependencies require Python 3.The intended usage requiresconda.Further dependencies will be installed on the first execution of the
assembly or annotation protocol and re-used on subsequent executions of
the protocols.For more information related to bioconda, see:https://bioconda.github.io/As a New EnvironmentWithconda, execute:conda create -n atlas -c bioconda python=3.6 \
    snakemake bbmap=37.78 click ruamel.yamlLoad the environment:source activate atlasInstallatlas:pip install -U pnnl-atlasIn the Same Environmentconda install -c bioconda python=3.6 \
    snakemake bbmap=37.78 click ruamel.yaml
pip install -U pnnl-atlasGetting StartedAfter installing, one needs to download the required databases and
create a sample configuration file.DatabasesTo download the databases and their respective metadata databases:atlas download -o ~/databasesThe downloads use approximately 30 GB of disk space.Configuration FileTo create a configuration file run:atlas make-config --database-dir ~/databases \
    config.yaml ~/directory_with_fastqsSample names and file paths along with default settings will populate
config.yaml. ThisYAMLfile can be
updated with any text editor.Sample names should be A-Z characters and can be dash (“-”) delimited.For complete documentation, please see:AssemblyAfter editing your configuration file and adjusting any additional
parameters we run assemblies across our samples using:atlas assemble config.yamlBy default, this will write results into our current working directory
across the total number of CPU cores available.LicenseBSD-3.DisclaimerThis material was prepared as an account of work sponsored by an agency
of the United States Government. Neither the United States Government
nor the United States Department of Energy, nor Battelle, nor any of
their employees, nor any jurisdiction or organization that has
cooperated in the development of these materials, makes any warranty,
express or implied, or assumes any legal liability or responsibility for
the accuracy, completeness, or usefulness or any information, apparatus,
product, software, or process disclosed, or represents that its use
would not infringe privately owned rights.Reference herein to any specific commercial product, process, or service
by trade name, trademark, manufacturer, or otherwise does not
necessarily constitute or imply its endorsement, recommendation, or
favoring by the United States Government or any agency thereof, or
Battelle Memorial Institute. The views and opinions of authors expressed
herein do not necessarily state or reflect those of the United States
Government or any agency thereof.PACIFIC NORTHWEST NATIONAL LABORATORY operated by BATTELLE for the
UNITED STATES DEPARTMENT OF ENERGY under Contract DE-AC05-76RL01830
Package: pnno
Language:
    🇺🇸🇨🇳«pnno» realizes the conversion of dataset or annotation data in different formatsThis repo provides several script tools for the following aspects:convert different data sets into the format specified by the algorithm. Currently implementedLabelImgannotation ->YoloV5data formatVisDronedata set ->TLT KITTIdata formatconvert PyTorch ImageFolder dataset to LMDB formatshow image in command lineTable of ContentsTable of ContentsBackgroundInstallUsageDataset/Label ConvertShow Image in Command LineMaintainersContributingLicenseBackgroundThe processing of data sets is often involved in the process of algorithm development, which needs to be converted into the format specified in algorithm training. Many scripts are accumulated over time. Whether these programs can be integrated together can not only be reused, but also reduce the difficulty of the next implementation. Let's Do It ! ! !Install$ pip install pnnoUsageDataset/Label ConvertBasic operation as follows:$ pnno -f <cfg_file>Operation 1: converttzutalin/labelImglabel file toultralytics/yolov5specified dataset format.Refer to the configuration fileconfigs/labelimg_2_yolov5.yamlOperation 1: convertVisDrone/VisDrone-Datasetdataset toKITTIlabel format.Refer to the configuration fileconfigs/visdrone_2_tlt.yamlOperation 3: convert PyTorch ImageFolder dataset to LMDB format.Refer todemo/imagenet_lmdbFor more usage, refert todemo/Show Image in Command Line$ simg -f <IMG_FILE>Maintainerszhujian -Initial work-zjykzjContributingAnyone's participation is welcome! Open anissueor submit PRs.Small note:Git submission specifications should be complied withConventional CommitsIf versioned, please conform to theSemantic Versioning 2.0.0specificationIf editing the README, please conform to thestandard-readmespecification.LicenseApache License 2.0© 2020 zjykzj
Package: pnoise
pnoisepnoiseis a pure-Python, Numpy-based, vectorized port of Processing'snoise()function. Thepinpnoisestands for Processing, Perlin, Python, "port", and probably other things I haven't though of yet.Why?I wrote this port before switching tovnoiseand, although I'm no longer using it, I figured I would keep it around.How does it compare tovnoise?pnoisevnoiseAlgorithm"classic Perlin noise of 1983""Perlin improved noise"LicenseLGPL v2.1MITScalar API✅✅Vectorized API✅✅3D function✅✅2D function❌ (can be derived from 3D but slower)✅1D function❌ (can be derived from 3D but slower)✅See a performance comparisonhere.Installationpip install pnoiseLicenseLGPL 2.1, seeLICENSEfile.
Package: pnoj-tg
pnoj-tgThis is a testcase generator for the PNOJ Online Judge.It is recommended to install pnoj-tg using the PIP package available on PYPI.Installationpipinstallpnoj-tgUsageFirst create a file namedconfig.yaml. Change the content to suit your needs.config.yaml:version:v1generator:input:["python3","generator.py"]# command to execute the input generatoroutput:["python3","solution.py"]# command to execute the solutiontestcase:batch:-name:batch1# batch namepoints:100# pointstestcase:name:t-{0}# testcase name, "{0}" is replaced with the testcase numbernum:10# number of testcases-name:examplepoints:0testcase:name:samplenum:1Then create two programsgenerator.pyandsolution.py.generator.pyshould generate a random case.solution.pyshould be the reference solution to the problem you are creating testdata for.Then run:pnoj-tgconfig.yamlBuild Instructionsgitclonehttps://github.com/pnoj/pnoj-tg.git# clones the repositorycdscrapec
pipinstall.# install pnoj-tg
Package: pnote
Manage your notes with pnote!Aboutpnoteis a text file, format agnostic, note manager.
It allows to keep a consistant hierarchy of note files that you can work with.Getting startedInstallation:> pip install pnoteCreate a new project:> pnote ~/mynotes
> # See configuration in ~/mynotes/config.jsonOpen and edit today's note file:> pnote ~/mynotes -tFeaturesSearch for files:> pnote ~/mynotes search -hTag files:> pnote ~/mynotes tag -hAdministrate your project:> pnote ~/mynotes admin -hFor more information onpnote:> pnote -h
Package: pnow-client
Usage:from pnow.pdapi import PredictNowClientSetup client along with its api keyapi_key = "<YOUR_API_KEY>"
client = PredictNowClient(api_key)Train demotrain_input_path = 'C:/Users/devstack/Documents/example_input_train.csv'
train_params = {
"username": "welly",
"email": "welly@predictnow.ai",
"label": "futreturn",
"timeseries": "yes",
"type": "classification",
"feature_selection": "shap",
"analysis": "small",
"boost": "gbdt",
"mode": "train",
"testsize": "0.2",
"weights": "no",
"prob_calib": "no",
"suffix": "myfirstsuffix",
"eda": "no",
}response = client.train(train_input_path, train_params)
print(response)Predict demolive_input_path = 'C:/Users/devstack/Documents/example_input_live.csv'
username = train_params["username"]
suffix = train_params["suffix"]
path = "../" + train_params["username"]
predict_params = {
"username": username,
"model_name": "saved_model_" + suffix + ".pkl",  # TODO proper model name
"eda": "no",
}
response = client.predict(live_input_path, params=predict_params)
print(response)Save Result demoresponse = client.save_to_output({"username": "welly", "output": "C:/Users/devstack/Documents"})
print(response)
Package: pnp
Pull 'n' PushPulls data from sources and pushes it to sinks with optional transformations in between.Installationpip install pnpInstallation with extras:pip install pnp[fswatcher,faceR]Please consult thecomponent documentationto see if a
component requires an extra or not.Getting startedDefinepullsto fetch / pull data from source systems.
Define onepushor multiplepushesper pull to transfer the pulled data anywhere else (you only need a plugin that
knows how to handle the target). You configure your pipeline inyaml:tasks:-name:hello-worldpull:plugin:pnp.plugins.pull.simple.Repeatargs:interval:1srepeat:"HelloWorld"push:-plugin:pnp.plugins.push.simple.EchoCopy this configuration and create the filehelloworld.yaml. Run it:pnp helloworld.yamlThis example yields the stringHello Worldevery second.Hint: You can validate your config without actually executing it withpnp--checkhelloworld.yamlIf you want to learn more please see the documentation atRead the Docs.
Package: pnp-graphql
Plug & Play GraphQL (pnp-graphql)--Change LogA library for making GraphQL API with Python/Django. This is like a flash drive,
just how you plug into computer and transfer files.Read Full Documentation here:https://docs.ashraful.dev/pnp-graphqlQuick StartDocumentation is coming soon...Install from pippip install pnp-graphqlAddgraphene_djangoon top of installed apps on settings.pyAddpnp_graphqlinto installed apps on settings.pyAdd PnP GraphQL config on settings.GRAPHENE={'SCHEMA':'pnp_graphql.schema.schema'}PNP_GRAPHQL={'ENABLED_APPS':['example_app'],# If you want to use Token Authentication. Otherwise it's optional'AUTHENTICATION_CLASS':'pnp_graphql.authentication.TokenAuthentication'}Add the following code onurls.pyfrompnp_graphql.urlsimporturlpatternsasupurlpatterns=[# ... ... ...]urlpatterns+=upSetDEBUG = Falsefor production use.That's it :)Now visit:http://your-ip:port/api/graphql-explorer/for explore GraphQL built-in UI explorer for query.Production ready API :http://your-ip:port/api/graphql/What's working?GraphQL queryMutation (Create, Update, Delete)PaginationAPI filtering for Number, String, Date, DateTimeAuthenticationWhat are the plans?Proper error handlingField validationCachingmany more ... ... ...Waiting for your contribution :smile:
Package: pnplabs
pnplabs.ioclientCommand aliases are dead. Create and share your commands online today.Go topnplabs.ioand try out our platform!🔭 We're currently working on:Cross-platform support for adding a directory to the OS PATH env var.Cross-platform support for adding Python scripts to run on startup / user login.📫 How to reach us: ...Email us atanything@pnplabs.ioLive chat onpnplabs.ioAbout usOur STORYObsessed with making dev teamwork as seamless and frictionless as possible, we've built an innovative platform to enhance team sync and collaboration.Our VISIONWhen it comes to technical communication, we believe in seamless team synchronization. We reduce technical noise by making information visible and auditable to all team members.Our PHILOSOPHYBuilt with great respect to developers precious time, our software requires zero attention, zero maintenance, zero learning curve.
Package: pnp-mace
IntroductionThis python package provides methods and utilities to explore the PnP algorithm and MACE framework in the context
of image reconstruction problems, along with some simple demos.  The ideas leading to this package are outlined inA SIAM News overview article from March 2021The 2020 SIAM Imaging Sciences Best Paper Award lecturewith more detail inthe papers in the references.Documentation on this package is available athttps://pnp-mace.readthedocs.io, which
includesinstallation instructions.Demo files are available as python scripts and Jupyter notebooks.  See theDemos pagefor more details.FeaturesImplementation of basic Plug-and-Play method as described inPlug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation,
*IEEE Transactions on Computational Imaging*, vol. 2, no. 4, Dec. 2016.Implementation of the solution of MACE equations using Mann iterations as inPlug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium,
*SIAM Journal on Imaging Sciences*, vol. 11, no. 3, pp. 2001-2020, Sept. 2018.Demo code illustrating reconstruction with image subsampling and tomography.This is free software with aBSD license.
Package: pnp-timeit
Project descriptionAuthor: PyPnPteampypnp@protonmail.comIntroductionMeasure execution time of small code snippets, write to log, Plug and Play styleInstallationThis package can either be installed using pip or from a tarball using the standard Python distutils.If installing using pip, you can download latest version from PyPI:pip install pypnp-timeitIf you are installing from a tarball, run the following command as an administrative user:python setup.py installExample & UsageExample Code 1copy following code to file: aa.pyimport time
from pnp_timeit.pnp_timeit import Pnp_Timeit

@Pnp_Timeit.timeit
def myfunc(arg1, arg2, arg3='hello'):
    time.sleep(3)

if __name__ == "__main__":
    Pnp_Timeit.enable()
    myfunc("a1", "a2", arg3="hello world")execute file: aa.py$ python aa.py

{"func": "myfunc", "args": ["a1", "a2"], "kwargs": {"arg3": "hello world"}, "time_cost_seconds": 3.002114}Example Code 2copy following code to file: bb.pyimport time
import logging
from pnp_timeit.pnp_timeit import Pnp_Timeit

@Pnp_Timeit.timeit
def myfunc(arg1, arg2, arg3='hello'):
    time.sleep(3)

if __name__ == "__main__":

    logger = logging.getLogger("MYTEST_LOGGER")
    ch = logging.StreamHandler()
    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
    ch.setFormatter(formatter)
    logger.addHandler(ch)
    logger.setLevel(logging.INFO)

    Pnp_Timeit.enable(logger)
    myfunc("a1", "a2", arg3="hello world")execute file: bb.py$ python bb.py

2020-06-08 16:39:54,151 - MYTEST_LOGGER - INFO - {"func": "myfunc", "args": ["a1", "a2"], "kwargs": {"arg3": "hello world"}, "time_cost_seconds": 3.01584}LicenseMIT license.If needed for inclusion in other open source projects, I’m glad to relicense this code.Bugs, Feature Requests & PatchesSecurity IssuesContactPyPnP teampypnp@protonmail.com
Package: pnq
PNQPNQ is a Python implementation like Language Integrated Query (LINQ).!!! danger
PNQはベータ版です。- 現在、ドキュメントとAPIが一致していません。
- ライブラリが十分な品質に到達するまで、頻繁に内部実装やAPIが更新される恐れがあります。
- 本番環境では利用しないでください。Featuresコレクション操作に関する多彩な操作アクセシブルなインタフェース型ヒントの活用非同期ストリームに対応Similar toolsPyFunctionallinqitpython-linqaioitertoolsasyncstdlibasqDocumentationSeedocumentationfor more details.DependenciesPython 3.8+InstallationInstall with pip:$pipinstallpnqGetting Startedimportpnqforxinpnq.query([1,2,3]).map(lambdax:x*2):print(x)# => 2, 4, 6pnq.query([1,2,3]).map(lambdax:x*2).result()# => [2, 4, 6]pnq.query([1,2,3]).filter(lambdax:x==3).one()# => 2importasyncioimportpnqasyncdefaiter():yield1yield2yield3asyncdefmain():asyncforxinpnq.query(aiter()).map(lambdax:x*2):print(x)# => 2, 4, 6awaitpnq.query(aiter()).map(lambdax:x*2)# => [2, 4, 6]awaitpnq.query(aiter()).filter(lambdax:x==3)._.one()# => 3asyncio.run(main())release notev0.0.1 (2021-xx-xx)Initial release.
Package: pnrw
PNRW: Python Nanocurrency RPC WrapperPNRW is a Python Wrapper for the Nano RPC protocol.PNRW supports Banano as well!InstallationPNRW can be installed using PyPIpipinstallpnrwLooking to install from source code?pipinstall.DocumentationPNRW uses the same variable and function names as the RPC Protocol, this means that you can use the official documentation for this library.(There are some differences, please check the difference guide below)pnrw.NodeThe node class handles all node connections.importpnrwnode=pnrw.Node("nodeIp")# Create a new node instanceArgumentsip(str): The node IP addressOptional argumentsport(int): The node's RPC port, default value is 7076dontUseHTTPS(bool): Disables HTTPS, default value if Falseheaders(dict): Custom headers that are sent with each request, default value is "Default"banano(bool): Ensures correct configuration when using PNRW for Banano, default is FalseExamplesStarting codeimportpnrwnode=pnrw.Node("mynano.ninja/api/node")# Create a new node instanceStarting code for Bananoimportpnrwnode=pnrw.Node("kaliumapi.appditto.com/api",banano=True)# Create a new node instanceGetting basic node informationimportpnrwnode=pnrw.Node("mynano.ninja/api/node")# Create a new node instanceprint(node.block_count())# Check node block countprint(node.version()["protocol_version"])# Print protocol version of nodeprint(node.uptime())# Print node uptime in secondsShowing current account balanceimportpnrwnode=pnrw.Node("mynano.ninja/api/node")# Create a new node instancebalance=node.account_balance("nano_396phmigwi883hk4x3teedtjk1ejskckmqe7xz3ymfnhe58p9o8gzmkygx91")# Get balance of an accountmyBalance=node.rai_from_raw(balance["balance"])# Convert from raw to Nanoprint(f"I currently have{myBalance}Nano!")Convert unitsfrompnrwimportconvertprint(convert.convert(123,"nano","knano"))# convert 123 Nano to kNanoprint(convert.convert(456,"ban","banoshi"))# convert 456 Banano to banoshiDifferences from official commandsNode.signOfficial documentation:https://docs.nano.org/commands/rpc-protocol/#signChanged to:sign_private()&sign_acount()&sign_block_hash()ChangesUsesign_private()when signing a block with a private key,sign_account()when signing a block with an account from wallet andsign_block_hash()when signing a block hash.DonationsAll donations and contributions are welcome! <3Nano:nano_396phmigwi883hk4x3teedtjk1ejskckmqe7xz3ymfnhe58p9o8gzmkygx91Banano:ban_1aws637mb3qnuf9j8swzufq3nj3fttuzkixbd817nmmhyms6a6kt1zyptq87
Package: pnt2
pnt2Calculating the degree of similarity between geometric figures of different shapes (by 2 points)Installationpip install pnt2How to use:frompathlibimportPathfrompnt2.src.get_similarityimportget_similarity,calc_similarity,convert_to_canonical""" Get input data """path_1=str(Path.cwd()/'DATA'/'first.png')path_2=str(Path.cwd()/'DATA'/'second.png')similarity_1=get_similarity(path_1,path_2)image_1_can=convert_to_canonical('1',path_1)image_2_can=convert_to_canonical('2',path_2)similarity_2=calc_similarity(image_1_can,image_2_can)print(f'\nsimilarity_1 ={round(similarity_1,7)}'f'\nsimilarity_2 ={round(similarity_2,7)}')
Package: pnt3
pnt3Calculating the degree of similarity between geometric figures of different shapes (by 3 points)Installationpip install pnt3How to use:frompathlibimportPathfrompnt3.src.get_similarityimportget_similarity,calc_similarity,convert_to_canonical""" Get input data """path_1=str(Path.cwd()/'DATA'/'first.png')path_2=str(Path.cwd()/'DATA'/'second.png')similarity_1=get_similarity(path_1,path_2)image_1_can=convert_to_canonical('1',path_1)image_2_can=convert_to_canonical('2',path_2)similarity_2=calc_similarity(image_1_can,image_2_can)print(f'\nsimilarity_1 ={round(similarity_1,7)}'f'\nsimilarity_2 ={round(similarity_2,7)}')