Saravanan666/pypi-org huggingface dataset:
Package: badger-batcher
Badger BatcherBadger Batcher contains useful utilities for batching a sequence on recordsFree software: MIT licenseDocumentation:https://badger-batcher.readthedocs.io.Installation$pipinstallbadger_batcherFeaturesImport Batcher:>>>frombadger_batcherimportBatcherSplit records based max limit for batch len:>>>records=[f"record:{rec}"forrecinrange(5)]>>>batcher=Batcher(records,max_batch_size=2)>>>batcher.batches()[['record: 0','record: 1'],['record: 2','record: 3'],['record: 4']]Split records with max limit for batch len and max limit for record size:>>>records=[b"aaaa",b"bb",b"ccccc",b"d"]>>>batcher=Batcher(...records,...max_batch_size=2,...max_record_size=4,...size_calc_fn=len,...when_record_size_exceeded="skip",...)>>>batcher.batches()[[b'aaaa',b'bb'],[b'd']]Split records with max batch len and size:>>>records=[b"a",b"a",b"a",b"b",b"ccc",b"toolargeforbatch",b"dd",b"e"]>>>batcher=Batcher(...records,...max_batch_len=3,...max_batch_size=5,...size_calc_fn=len,...when_record_size_exceeded="skip",...)>>>batcher.batches()[[b'a',b'a',b'a'],[b'b',b'ccc'],[b'dd',b'e']]When processing big chunks of data, consider iterating instead:>>>importsys>>>records=(f"record:{rec}"forrecinrange(sys.maxsize))>>>batcher=Batcher(records,max_batch_size=2)>>>forbatchinbatcher:...# do something for each batch...some_fancy_fn(batch)If you need to encode records before applying the batcher, just encode it before applying.
Batcher will not eagerly realize the whole iterable, so use a generator for bigger iterables.>>>records=["a","a","a","b","ccc","bbbb","dd","e"]>>>encoded_records_gen=(record.encode("utf-16-le")forrecordinrecords)>>>batcher=Batcher(...encoded_records_gen,...max_batch_len=3,...max_record_size=6,...max_batch_size=10,...size_calc_fn=len,...when_record_size_exceeded="skip",...)>>>batched_records=batcher.batches()[[b"a\x00",b"a\x00",b"a\x00"],[b"b\x00",b"c\x00c\x00c\x00"],[b"d\x00d\x00",b"e\x00"],]Full example for e.g. Kinesis Streams like processingimportrandomfrombadger_batcherimportBatcherdefget_records():records=(f"""{{'id': '{i}', 'body':{('x'*random.randint(100_000,7_000_000))}}}"""foriinrange(10_000))returnrecordsrecords=get_records()encoded_records=(record.encode("utf-8")forrecordinrecords)batcher=Batcher(encoded_records,max_batch_len=500,max_record_size=1000*1000,max_batch_size=5*1000*1000,size_calc_fn=len,when_record_size_exceeded="skip",)fori,batchinenumerate(batcher):# do somethingCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-04-09)First release on PyPI.
Package: badger-mk
Badger Mk -- Badge MakerScript for exporting a series of files after replacing placeholders in templatesvgfiles with data from acsvfile.UsageInstallationInstallable withpiporpipx(recommended).pipx install badger-mkCLITBA.PlatformShould work on all major platforms.LicenseGNU General Public License v3.0 or laterSeeLICENSEfor the full text.
Package: badger-proxy
badgermDNS-based reverse proxy for naming services on a local network.Sample configConfiguration should be placed in~/.badger/config.toml, but this can be changed with--config <path>. The config can also be given as eitherJSON,YAML,YML,INIorXML.[badger]level="INFO"# Set default logging level to INFO.enable_docker=false# Disable Docker support.mappings=["service1@10.0.0.3:5000",# Map service1.local -> 10.0.0.3:5000."service2@10.0.0.50:80",# Map service2.local -> 10.0.0.50:80."service3@10.0.0.4:5256"# Map service3.local -> 10.0.0.4:5256.]
Package: badgers
Badgers: bad data generatorsbadgersis a python library for generating bad data (more precisely to augment existing data with data quality deficits such as outliers, missing values, noise, etc.). It is based upon a simple API and provides a set of generators object that can generate data quality deficits from existing data.A word of caution: badgers is still in an early development stage. Although the core structure of the package and thegenerate(X,y)signature are not expected to change, some API details (like attributes names) are likely to change.The full documentation is hosted here:https://fraunhofer-iese.github.io/badgers/.For a quick-start, you can installbadgerswith pip:pipinstallbadgersImport badgers as any other library and start using it:fromsklearn.datasetsimportmake_blobsfrombadgers.generators.tabular_data.noiseimportGaussianNoiseGeneratorX,y=make_blobs()trf=GaussianNoiseGenerator(noise_std=0.5)Xt,yt=trf.generate(X,y)More examples are available in thetutorialssection.The API documentation is also available in theAPIsection.Interested developers will find relevant information in theCONTRIBUTING.mdpage.
Package: badger-voter-sdk
Badger SDK for autovotersThis is the library for shared code that is used in autovoters, such as cvx autovoter and aura autovoterBuildCoveragePYPIList of functional modules:UtilitiesGet web3 instanceimportosfrombadger_voter_sdk.web3importget_web3web3=get_web3(os.getenv("ETHNODEURL"))Or web3 will be created from default secrets inconstants.pyAWS get secretGetting secret from AWS by id and keyfrombadger_voter_sdk.awsimportget_secretsecret=get_secret(secret_id="SecretId",secret_key="SecretKey")Json loggerPreconfigured json logger to be shared across voting bots projectsfrombadger_voter_sdk.json_loggerimportloggerlogger.info()Snapshot and data collectorsGet all voters for snapshotfrombadger_voter_sdk.collectors.snapshot_collectorsimportget_votersvoters=get_voters(snapshot_id="some_snapshot_id")Get vlTOKEN amounts per wallet voted for given snapshot roundfrombadger_voter_sdk.collectors.api_collectorsimportget_scoresscores=get_scores("aurafinance.eth","1","<SNAPSHOT_ID>",["<ADDRESS1>"],[{'name':"erc20-votes",'network':"1",'params':{'symbol':"<vlTOKEN>",'address':"<STRATEGY_ADDR>"}}])Get voting choices with voting powerYou need to combine two previous functions outputs:frombadger_voter_sdk.collectors.data_processorsimportextract_voting_power_per_poolchoices_with_votes=extract_voting_power_per_pool(voters={"<wallet>":{"1":123}},scores={"<wallet>":123333.1})
Package: badges
Install$[sudo]pipinstallbadgesFeaturesmd,rstsupportautoformat strings with instance and class variablesminimalistic design, easy subclassingCircleCI, Travis, ... badge classesExamples# format attributes - {attrname}>>>classCustomBadge(badges.Badge):branch="master"link="https://site.com/{fullname}"image="https://site.com/{fullname}?branch={branch}.svg">>>CustomBadge(fullname="owner/repo")'[![](https://site.com/owner/repo?branch=master.svg)](https://site.com/owner/repo)'# format @property - {0.propname}>>>classCustomBadge(badges.Badge):branch="master"link="https://site.com/{0.fullname}"image="https://site.com/{0.fullname}?branch={branch}.svg"@propertydeffullname(self):return"/".join(os.getcwd().split("/")[-2:])>>>CustomBadge()'[![](https://site.com/owner/repo?branch=master.svg)](https://site.com/owner/repo)'Sourcesbadges/badge.Badgebadges/bettercodehub.Bettercodehub(GitBadge)badges/circleci.Circleci(GitBadge)badges/codeclimate.Codeclimate(GitBadge)badges/codecov.Codecov(GitBadge)badges/codefactor.Codefactor(GitBadge)badges/gitbadge.GitBadge(Badge)badges/scrutinizer.Scrutinizer(GitBadge)badges/semaphoreci.Semaphoreci(GitBadge)badges/sonarcloud.Sonarcloud(Badge)badges/travis.Travis(GitBadge)
Package: badges-gitlab
Badges GitlabThis project was created to generate badges for Gitlab in CI jobs, mainly for private repositories where
other common methods are not available (direct API Calls, shields.io, etc...).By default, Gitlab supports only two types of badges: pipeline and test coverage.These badges are better detailed at:Gitlab Project Badges.InstallationYou can install this package from pypi using pip.$pipinstallbadges-gitlabGeneral Usageusage: badges-gitlab [-h] [-p PATH] [-t TOKEN] [--junit-xml FILE_PATH] [-s LABEL MESSAGE COLOR][-lb URLS [URLS ...]] [-V]Generate Gitlab Badges using JSON files and API requests. Program version v0.0.0.optional arguments:-h, --help            show this help message and exit-p TEXT, --path TEXT  path where json and badges files will be generated/located (default: ./public/badges/)-t TEXT, --token TEXT specify the private-token in command line (default: ${PRIVATE_TOKEN})--junit-xml TEXT      specifies the path of a JUnit XML file for parsing the test results-s LABEL MESSAGE COLOR, --static-badges LABEL MESSAGE COLORspecify static badges in command line using lists-lb URLS [URLS ...], --link-badges URLS [URLS ...]specify shields.io urls to download badges-V, --version         returns the package versionAuthorFelipe Pinheiro SilvaContributorsBenjamin Maréchal (irmo322)Further DocumentationSlowly moving documentation toReadTheDocs.
Package: badges-io
badges_ioUploads total badge data to an online service running athttps://badges.vidstige.se/UsageJust issuepython3 -m badges_io coverage 97%Here the "coverage" is a subject appearing to the left in the badge, while
"97%" here is the status of the badge, appearing to the right.The color of the badge will be auto computed from the status. A third argument
will override the color, e.g. "green".to upload coverage to the badges service.badges_iowill try to autodetect the
repo slug (e.g. "user/repo") by first looking at the origin remote of the
current git repo, and also using environment variables if run on Travis CI.AuthorSamuel Carlsson
Package: badgie
BadgieAdd all the badges with Badgie!Using BadgieInstall Badgie:pipinstallbadgieAdd Badgie tags to your README:<!-- BADGIE TIME -->
<!-- END BADGIE TIME -->Run Badgie:badgie-wREADME.mdAnd enjoy magic badges:<!-- BADGIE TIME -->

[![pipeline status](brettops/containers/verible/badges/main/pipeline.svg)](brettops/containers/verible/-/commits/main)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)

<!-- END BADGIE TIME -->Use as a pre-commit hookBadgie can be used as a pre-commit hook, so you can get fresh badges on every
commit.Add the following to a.pre-commit-config.yamlfile. Note the emptyrevtag:repos:-repo:https://gitlab.com/buildgarden/tools/badgierev:""hooks:-id:badgieRunpre-commit autoupdateto pin to the latest version:pre-commitautoupdateRunpre-commitdirectly or install as a hook:# directlypre-commit# as a Git hookpre-commitinstall
gitcommit-m"..."CaveatsBadgie makes decisions on the assumption that you do sensible things with your
repository structure. It does not try to work around bad practices. MRs that
encourage this will be rejected.
Package: badgrclient
Quickstartbadgrclientis a python library forBadgrAPIsInstallationpipinstallbadgrclientDocshttps://badgrclient.readthedocs.io/UsageInstantiate a clientfrombadgrclientimportBadgrClientclient=BadgrClient('username','password','client_id')Fetch your entities with the client or by giving an entityId.>>>my_issuers=client.fetch_issuer()[Issuer(7fde21f03a30dfg),Issuer(de21ce2d52df0)]>>>baby_badger=client.fetch_badgeclass('<baby_badgr_entity_id>')[0]BadgeClass(<baby_badgr_entity_id>)Use member functions to perform actions on the entity>>>baby_badger.issue('jane@gmail.com')Assertion(<entity_id>)Or directly import a model and get going>>>frombadgrclientimportAssertion>>>janes_assertion=Assertion(client,eid='<entity_id>')Assertion(<entity_id>)>>>janes_assertion.revoke('Revocation Reason')
Package: badkeys
badkeysTool and library to check cryptographic public keys for known vulnerabilitieswhat?badkeys checks public keys in a variety of formats for known vulnerabilities.
A web version can be found atbadkeys.info.installbadkeys can be installedvia pip:pip3 install badkeysAlternatively you can call./badkeys-clidirectly from the git repository.usageBefore using badkeys you need to download the blocklist data:badkeys --update-blAfter that you can callbadkeysand pass files with cryptographic
public keys as the parameter:badkeys test.crt my.keyIt will automatically try to detect the file format. Supported are public and
private keys in PEM format (both PKCS #1 and PKCS #8), X.509 certificates,
certificate signing requests (CSRs) and SSH public keys.
You can find some test keys in thetests/datadirectory.By default badkeys will only output information about vulnerable keys, meaning
there will be no output if no vulnerabilities are found. The-aparameter
creates output for all keys.scanningbadkeys can directly scan SSH and TLS hosts and automatically check their public
keys. This can be enabled with the parameters-s(for SSH) and-t(for TLS).
By default SSH will be scanned on port 22 and TLS will be scanned on several
ports for common protocols (https/443, smtps/465, ldaps/636, ftps/990,
imaps/993, pop3s/995 and 8443, which is commonly used as a non-standard https port).Alternative ports can be configured with--tls-portsand--ssh-ports.TLS and SSH scanning can be combined:badkeys -ts example.orgPython module and APIbadkeys can also be used as a Python module. However currently the software is
in alpha state and the API may change regularly.aboutbadkeys was written byHanno Böck.This work was funded in part by Industriens Fond through the CIDI project
(Cybersecure IOT in Danish Industry) and in part by theCenter for Information Security and Trust (CISAT)at the IT University of Copenhagen, Denmark.
Package: badlands
Badlands - Basin & Landscape DynamicsBasin and Landscape Dynamics (Badlands) is a long-term surface evolution model built to simulate landscape development, sediment transport and sedimentary basins formation from upstream regions down to marine environments.This program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.What’s in the box?Badlands is an open-source Python-based code and can be used to simulate:hillslope processes (linear & non-linear diffusion),fluvial incision (Stream Power Law, Transport Capacity Law)sediment transport and deposition,wave-induced longshore drift transport,reef growth and carbonate platform formation,submarine gravity currents (turbidity currents),spatially and temporally varying tectonic (horizontal + vertical displacements) andeffects of climate changes (rainfall) and/or sea-level fluctuations.Documentation & Installationhttps://badlands.readthedocs.io/
Package: badlands-companion
Badlands - CompanionBasin and Landscape Dynamics (Badlands) is a long-term surface evolution model built to simulate landscape development, sediment transport and sedimentary basins formation from upstream regions down to marine environments.This program is a free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.What’s in the box?Badlands is an open-source Python-based code and can be used to simulate:hillslope processes (linear & non-linear diffusion),fluvial incision (Stream Power Law, Transport Capacity Law)sediment transport and deposition,wave-induced longshore drift transport,reef growth and carbonate platform formation,submarine gravity currents (turbidity currents),spatially and temporally varying tectonic (horizontal + vertical displacements) andeffects of climate changes (rainfall) and/or sea-level fluctuations.Documentation & Installationhttps://badlands.readthedocs.io/
Package: bad-mason
bad_masonA small sample to demo what python can dobad_masonis a a small sample to demo what python can doDemoCALL bad_mason -g
or
CALL python -m bad_mason -g

DEMO!! You have been hacked
=====5a8fWOxg/4=====
Also write a hacked file in .ssh
It could send it out to somewhere!!
b'<!doctype html><html'Usageusage: bad_mason [-h] [-g]

optional arguments:
  -h, --help         show this help message and exit
  -g, --get          get some data like covid-19 (fake)

or as a Module:
  python -m bad_mason -gInstall$ pip install bad_masonPack itpython -m pip install setuptools
python -m pip install wheel
python -m zipfile -c datahelper.zip .\datahelper\__init__.py .\datahelper\__main__.py .\datahelper\datahelper.py
python setup.py sdist bdist_wheelTry to install locallypython -m pip install -U .\dist\bad_mason-1.0.1-py3-none-any.whlUpload to PyPIpython -m pip install twine
twine upload dist/bad_mason-1.0.1-py3-none-any.whlLicenseMIT
Package: badonker
badonkerfeature rich and hQ contentInstallationpip install badonkerEnhance and spice up your NSFW stuff with badonker.Quick rewrite and animated object fromscathach-api.FunctionDescriptionhonkerGet a gif or animated URL of a honkerbadonkersGet a gif or animated URL of a badonkershhonkerGet a gif or animated URL of a hentai honkweassGet a gif or animated URL of a asshassGet a gif or animated URL of a hentai assthiccGet a gif or animated URL of a thicchthiccGet a gif or animated URL of a hentai thiccmilfGet a gif or animated URL of a milfhmilfGet a gif or animated URL of a hentai milfmilkGet a gif or animated URL of a milkhmilkGet a gif or animated URL of a hentai milkbootyGet a gif or animated URL of a bootyhbootyGet a gif or animated URL of a hentai bootybellyGet a gif or animated URL of a bellyhbellyGet a gif or animated URL of a hentai bellygifTitsGet a gif or animated URL of a titsgifPussyGet a gif or animated URL of a pussygifAssGet a gif or animated URL of a assgifMissionaryGet a gif or animated URL of a armygifCowgirlGet a gif or animated URL of a cow gurlgifBjGet a gif or animated URL of a bjgifCumshotsGet a gif or animated URL of a cumshotsahegaoGet a gif or animated URL of a ahegaorandomGet a gif or animated URL of as randomUsage exampleimportbadonkerprint(badonker.booty())>>>'https://us.rule34.xxx/images/3941/ba4bda8b52cfb43a32bc589e8f84e6fa.gif'
Package: badoo
badooremotely control badooFree software: GNU General Public License v3FeaturesSend messages to users programmatically.Easily get user ids of users online, nearby, and who have visited your profileCan be used in “headless” mode on a remote server with no GUI.Automatically play the “encouters” game.Collect profile data as Python dictionary with a single line of code.print (b.get_profile_data("0817947789"))will return a dictionary like this:{'location' 'Caen, Calvados',
'Relationship': "I'm single",
'Sexuality': "I'm straight",
'Appearance': '165 cm, average body',
'Living': 'By myself',
'Children': 'Already have',
'Smoking': 'I smoke occasionally',
'Drinking': 'I drink socially',
'interests': ['Abd Al Malik',
              'Diana Krall',
              'Le Petit Journal',
              'Butterfly Effect',
              'Thelonious Monk',
              'Barry White',
              'Les Lascars']}


                     vTODO: Screenshots of profile pagesTODO: Handle chat messagesHistory1.3.4 (2020-10-01)Add language to profile dictCorrectly handle deleted users1.3.3 (2020-09-30)Added ability to save screenshots of badoo profile pagesAdded ability to save badoo user profile imagesOnline status now returned in profile dict0.1.0 (2020-09-26)First release on PyPI.
Package: badook-airflow
badook AirflowDag code example:from airflow import DAG
from airflow.utils.dates import days_ago

from badook_airflow.operators.badook_operator import BadookTestOperator


default_args = {
    'owner': 'airflow',
    'start_date': days_ago(0)
}

with DAG(
    dag_id='badook_test_runner',
    default_args=default_args,
    schedule_interval='@once'
) as dag:
    badook_test = BadookTestOperator(
        target_directory='test_dir',
        data_cluster_url='https://test.url',
        management_cluster_url='https://test.managment.url',
        client_id='APIKEY',
        client_secret='SECRETKEY',
        task_id="run_tests"
    )
    badook_test
Package: badook-tests
badook tests python SDKSetupPrerequisitesCurrent version supports Python 3.9 onlyInstallationTo install badook from pip use:python -m pip install badook-testsRunning the example localyTo run using a local server first set the local address correctly in theconfig\badook.yamlfile under thedata_cluster_urlentry.
Next run the example using the following command:python examples/test_dsl_example.py
Package: bad-otzyvy-net
bad_otzyvy_netbad_otzyvy_netFree software: MIT licenseDocumentation:https://bad-otzyvy-net.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-08-13)First release on PyPI.
Package: bad-phylo
BAD: Bold Assertor of DifficultyDescriptionBAD is a python tool for predicting the difficulty of phylogenetic placements. BAD uses RAxML-NG outputs as input. It requires a RAxML-NG installation.
It was trained on empirical datasets from TreeBASE and can use both AA and DNA data. The output of BAD is a score between 0 (easy) and 1 (difficult).
BAD provides an explanation of its prediction using the Shapley values implementation SHAP (Github,Paper).InstallationUsing pippip install bad-phyloUsage ExampleA simple command line call of BAD looks like this:bad -msa /test/example.fasta -tree /test/example.bestTree -model /test/example.bestModel -query /test/query.fasta -o test_badThis command will use the MSA and query file in fasta format, and the best tree inferred with RAxML-NG as well as the model.
It will compute features from all four data sources and predict the placement difficulties for each taxon in the query file.
All output files will be stored in an output folder calledtest_badin the current directory.
BAD will summarize the explanations for the prediction in the command line. For further details, please look at the SHAP summary plots or thebad.logfile in the output folder.Before interpreting the explanations provided by BAD, please make sure you know how to properly interpret Shapley values.
Easy to understand introduction to Shapley values:https://christophm.github.io/interpretable-ml-book/shapley.htmlPlease keep in mind that BAD requires an installation of RAxML-NG. By default, it uses the commandraxml-ng.
If your RAxML-NG installation is not part of the PATH variable, you can specify the path to the RAxML-NG binary file with the parameter-raxmlng PATH_TO_RAXMLNG.ReferencesA. M. Kozlov, D. Darriba, T. Flouri, B. Morel, and A. Stamatakis (2019)RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inferenceBioinformatics, 35(21): 4453–4455.https://doi.org/10.1093/bioinformatics/btz305S. M. Lundberg and S.-I. Lee. A unified approach to interpreting model predictions. In
Proceedings of the 31st International Conference on Neural Information Processing Systems,
NIPS’17, page 4768–4777. Curran Associates Inc., 2017. ISBN 9781510860964.https://proceedings.neurips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf