Saravanan666/pypi-org huggingface dataset:
Package: pybom
PyBOMGenerate a bill of materials and vulnerability information for your python projects.AboutPyBOM has two functions:Aggregate a python project's license, package, and vulnerability information in one place by leveraging the GitHubdependencyandvulnerabilityAPIs andPyPI JSON API.Report image vulnerability information for docker images pushed to a registry. (Currently in development; Google Container Registry will be the first supported.)Getting StartedInstallationpipinstallpybom...or addpybomto yourrequirements.txtand runpip install -r requirements.txt.UsageGitHub Personal Access TokenPyBOM uses GitHub's v4 GraphQL API to get dependency and vulnerability information. To use the API, you must have a Personal Access Token (PAT).To get a PAT:Navigate to thePersonal access tokenspage in GitHub's Settings (Settings > Developer settings > Personal access tokens)ClickGenerate new token.Add a note explaining what the token is for, and underScopes, selectRepo.ClickGenerate Token.Copy the token. You won't be able to see it again.PyBOM reads the token from theGITHUB_PERSONAL_ACCESS_TOKENenvironment variable. To set this variable for all bash sessions, include the following in your.bash_profile(on macOS):exportGITHUB_PERSONAL_ACCESS_TOKEN=<your-token-here>...thensource ~/.bash_profilein your current session to set the variable.Python BOMTo get a list of components in your Python project hosted in Github:
(Note: You must have the Dependency Graph API turned on for this to work.)frompybom.repositoryimportget_componentsrepo_name="pybom"repo_owner="carbonrelay"components=get_components(repo_name,repo_owner)type(components)# <class 'list'>type(components[0])# <class 'pybom.application_component.ApplicationComponent'>Python VulnerabilitiesTo get a list of vulnerabilities in your Python project hosted on Github:frompybom.repositoryimportget_vulnerabilitiesrepo_name="pybom"repo_owner="carbonrelay"vulnerabilities=get_vulnerabilities(repo_name,repo_owner)type(vulnerabilities)# <class 'list'>type(vulnerabilities[0])# <class 'pybom.vulnerability.Vulnerability'>Image VulnerabilitiesImage vulnerability reporting is not yet fully implemented. It will be  finished in a future release.ContributingPyBOM is developed and tested against Python 3.6. I recommend usingpyenvto manage your python versions andvenvto manage project dependencies.After cloning the repository:python-mvenvvenv
.venv/bin/activate
pipinstall-rrequirements.txt# run tests with coverage report./coverage.shTo build the project wheels:# adapted from https://packaging.python.org/tutorials/packaging-projects/pipinstall--user--upgradesetuptoolswheel
pythonsetup.pysdistbdist_wheelIf you bump a requirement version inrequirements.txt, freeze the new dependencies torequirements-freeze.txt:pipfreeze>requirements-freeze.txtLicenseThis project is licensed under the Apache license. SeeLICENSE.md.
Package: pybomb
PyBombThis will go into version 1.0 when all resources are supported.Currently Supported Resources:gamesgameplatformsSupport OSS, and me :)If you find this project useful, please feel free tobuy me a coffeeInstallpipinstallpybombExamplesTo see a working example of Pybomb, take a look at the example projectPyBomb-demoGamesClient - searchimport pybomb

my_key = your_giant_bomb_api_key
games_client = pybomb.GamesClient(my_key)

return_fields = ('id', 'name', 'platforms')
limit = 10
offset = 5
sort_by = 'name'
filter_by = {'platforms': pybomb.PS3}

response = games_client.search(
   filter_by=filter_by,
   return_fields=return_fields,
   sort_by=sort_by,
   desc=True,
   limit=limit,
   offset=offset
)

print response.results
print response.uri
print response.num_page_results
print response.num_total_resultsGamesClient - quick_searchimport pybomb

my_key = your_giant_bomb_api_key
games_client = pybomb.GamesClient(my_key)

response = games_client.quick_search(
  name='call of duty',
  platform=pybomb.PS3,
  sort_by='original_release_date',
  desc=True
)

print response.results
print response.uri
print response.num_page_results
print response.num_total_resultsDocumentationThe full documentation, including more examples can be found atreadthedocsContributingThis project usesPoetryandNoxso make sure you have those setup!Whilst working you can use$nox-rsteststo run the tests, but once you have finished, make sure to run all of nox before making a PR$noxThis project also usesPytestfor tests, and we aim for 100% test coverage. The build will fail when the coverage is less than this, but feel free to use# pragma: no coverif it makes sense to do so (although this should be very rare).Mypyis also used, and we even usetypeguardso typing is important here!I am always happy to help where I can and I try to be as responsive as possible to PRs. Email me if I am slow, or if you need any help!
Package: pybonaparte
BonaparteLibrary to control Napoleon eFIRE enabled fireplacesInstallationFor now the project uses thepybonapartename on PyPI.To install use:pip install pybonaparte.The name may change tobonapartesome time in the future depending on the outcome of this ticket:https://github.com/pypi/support/issues/2683Contributors ✨Thanks goes to these wonderful people (emoji key):This project follows theall-contributorsspecification. Contributions of any kind welcome!CreditsThis library is heavily inspired (if not shamelessly copied) from the work by J. Nick Koston (@bdraco) over athttps://github.com/bdraco/yalexs-bleThis package was created withCopierand thebrowniebroke/pypackage-templateproject template.
Package: pybond
pybondpybondis a spying and stubbing library inspired heavily by theclojurebondlibrary.Installationpippipinstallpybond==0.2.1requirements.txtpybond==0.2.1pyproject.tomlpybond="0.2.1"Example usageLet's say you wanted to test the functions in this module:# /sample_code/my_module.pyfromtypingimportAnyimportsample_code.other_packageasother_packagedeffoo(x:Any)->None:response=other_package.make_a_network_request(x)other_package.write_to_disk(response)returnresponsedefbar(x:Any)->None:returnfoo(x)Withpybondyou can easily spy on any given function or stub out functions
that perform IO:# /tests/test_my_module.pyfrompybondimportcalls,called_with_args,spy,stub,times_calledimportsample_code.other_packageasother_packageimportsample_code.my_moduleasmy_modulefromsample_code.my_moduleimportbardeftest_foo_is_called():withspy(my_module.foo):asserttimes_called(my_module.foo,0)bar(42)asserttimes_called(my_module.foo,1)bar(42)bar(42)asserttimes_called(my_module.foo,3)deftest_bar_handles_response():withstub((other_package.make_a_network_request,lambdax:{"result":x*2}),(other_package.write_to_disk,lambda_:None),),spy(my_module.foo,):asserttimes_called(my_module.foo,0)asserttimes_called(other_package.make_a_network_request,0)assertbar(21)=={"result":42}asserttimes_called(my_module.foo,1)asserttimes_called(other_package.make_a_network_request,1)assertcalled_with_args(my_module.foo,args=[21])assertbar(20)=={"result":40}assertcalls(my_module.foo)==[{"args":[21],"kwargs":None,"return":{"result":42},"error":None,},{"args":[20],"kwargs":None,"return":{"result":40},"error":None,},]assertcalls(other_package.write_to_disk)==[{"args":[{"result":42}],"kwargs":None,"return":None,"error":None,},{"args":[{"result":40}],"kwargs":None,"return":None,"error":None,},]LicenseDistributed under theEclipse Public License.
Package: pybongtvapi
pybongtvapipybongtvapiis a pythonic interface to thebong.tvplatform.Usagepybongtvapisupports direct access to bong.tv’s JSON web service:importpybongtvapi# log in with your username and passwordcredentials=pybongtvapi.UserCredentials("john","doe")api=pybongtvapi.API(credentials=credentials)# or, if there is a bong.tv cookie somewhere on your hard diskapi=pybongtvapi.API(cookie='path/to/your/cookie.txt')From here on, all methods outlined inbong.tv’s API specificationcan be called:# get list of recordingsrecordings=api.list_user_recordings()# list all channelschannels=api.list_channels()# let's assume today is 15th of May 2015. now, list today's broadcasts ..channel_id=1api.get_broadcasts(channel_id)# or tomorrow's broadcastapi.get_broadcasts(channel_id,date='16-05-2015')# create a recording from a broadcast IDbroadcast_id=12345api.create_recording(broadcast_id)# delete a recording from your BongSpacerecording_id=56789api.delete_recording(recording_id)# search for broadcastsbroadcasts=api.search_broadcasts('heute')You can also work with a higher-level API provided bypybongtvapi.
You can use thepybongtvapi.EPGto access theBongGuide:epg=pybongtvapi.EPG(api)forchannelinepg.channels:print(channel.name)forbroadcastinchannel.broadcasts:print()print(broadcast.title)print(broadcast.outline)Or use thepybongtvapi.PVRto access your personalBongSpace:pvr=pybongtvapi.PVR(api)forrecordinginpvr.recordings:print(recording.title)ifrecording.is_recorded():print(recording.url)Changelog0.2bugfix: Recording.is_scheduled() did’nt work0.1first public release. successor thepybongtvlibrary.Licensepybongtvapiis available underMIT
License.DownloadYou can downloadpybongtvapi.py.Alternatively:git clone git@github.com:cmaugg/pybongtvapi
Package: pybonjour
pybonjour provides a pure-Python interface (via ctypes) to Apple
Bonjour and compatible DNS-SD libraries (such as Avahi).  It allows
Python scripts to take advantage of Zero Configuration Networking
(Zeroconf) to register, discover, and resolve services on both local
and wide-area networks.  Since pybonjour is implemented in pure
Python, scripts that use it can easily be ported to Mac OS X, Windows,
Linux, and other systems that run Bonjour.
Package: pyboo
# pybooA Python package to compute bond orientational order parameters as defined by Steinhardt Physical Review B (1983) doi:10.1103/PhysRevB.28.784.Steinhardt's bond orientational order parameter is a popular method (>20k citations of the original paper) to identify local symmetries in an assembly of particles in 3D. It can be used in particle-based simulations (typically molecular dynamics, brownian dynamics, monte-carlo, etc.) or in particle-tracking experiments (colloids, granular materials) where the coordinates of all particles are known.## Licence, citations, contactThis code is under GPL 3.0 licence. See LICENCE file.Please cite Pyboo and it's author(s) in any scientific publication using this software.```@misc{pyboo,title={Pyboo: A Python package to compute bond orientational order parameters},author={Mathieu Leocmach},year={2017},doi={10.5281/zenodo.1066568},url={https://github.com/MathieuLeocmach/pyboo}}```ContactMathieu LEOCMACH, Institut Lumière Matière, UMR-CNRS 5306, Lyon, Francemathieu.leocmach AT univ-lyon1.fr## InstallationDependencies are numpy, scipy and numba. Tested with python 2.7 and python 3.5.You can install with pip:pip install pyboo## DocumentationDocumentation is avaiable on Readthedocs: [http://pyboo.readthedocs.io]
Package: pybooks
Install$pipinstallpybooksUsageSimple usagefrom pybooks.pbook import Pbooks 

pbook = Pbooks( author='jerome',
                title='elements of statistic',
                weights=(9, 1)) 
         

pbook.main()Theweightsargument is used to calculate the accuracy of all the items found and the final chosen url. For example, aweightsof(9, 1)will place more important on the author accuracy than title's hence it is more likely that the result will have author's name more similar to the input. Default weights are(1, 1)Optional arguments areshow_result: Print the result at the end or not,threshold: Only print result with accuracy higher than thresholdandlog: Show process while running or not, etc. More arguments can be found in the documentation of the code.You can modify or add additional sources by editing the file pybooks/sources.json and follow the existing format within the file.Sources.json format, rulesExample:Library Genesis with elements of statistics as search termThe only components we need in the URL are:request: ?req=...,delimiter: + (elements+of+statistics)andpagination:  page=...andterm_concatenation: & (req=...&page=...)INSIDE SOURCES.JSON

URL: main url without search terms
URL_RULES: dictionary contains keys and values as search rules.
HTML_RULES: Contains nested dictionary represent necessary components to conduct searches such as tags, attributes for titles, authors, rows and page body
.POSITION: The position of the that element in the row if there are other similar elements with similar tag and attributes, leave as null if it is unique
.TAG: The tag of the desired element
.ATTRIBUTE: Attributes of the tag, for exanple: {"class": "id", "...": "..", ...}. Leave the value of a key as true if an attribute has no valueCOMMAND LINE USAGEYou can use the package directly from the command line
Example: Searching a book with title: "Elements of statistical learning" and author: "Jerome Friedman"
You can add pbook.exe to environment variables and use it from the command line by this command with%PATH%as your path to pybooks directory (folder), usually found in\Python{version}\Lib\pybooks.setxPATH"%PATH%pbook-a"jerome friedman"-t"elements of statistical learning"OPTIONALARGUMENTS
-h,--helpshowthishelpmessageandexit-aAUTHOR,--authorAUTHORAuthorofthebookyouwanttofind-tTITLE,--titleTITLETitleofthebookyouwanttofind-wWEIGHTS,--weightsWEIGHTSAssignauthorandtitleweighttothefinalaccuracycalculation-thTHRESHOLD,--thresholdTHRESHOLDOnlygetresultsabovethethresholdamount-lLOG,--logLOGPrintouttheprocess-sSHOW,--showSHOWShowresultattheendofrunning-brBREAK_AT,--break-atBREAK_ATStopwhenencounterabookwithaccuracyhigherthanorequaltothisnumber-mMETHOD,--methodMETHODChooseabookevaluationmethodLICENSEMIT
Package: pybool
pybool: A python package that infers Boolean networks given a set of constraints.Consider the common scenario of a biologist who is studying a particular
regulatory network. They study a set of genes that are known to play a role in the
network. They have some background knowledge of particular regulatory connections
from his own studies or from the literature. In addition to this they have
perturbation data that reveals the temporal order of expression of the genes under
some conditions. For example, these could be derived from loss-of-function
or over-expression experiments. The biologist would like to elucidate the entire
network and to this end can perform various experiments to test particular regulatory
connections. These experiments are costly and time-consuming. Which
connections should they focus on? This is where the pybool package can help.
By modelling candidate regulatory networks using Boolean logic, pybool evaluates
which networks are consistent with the perturbation data and the known
regulatory connections.
Package: pyboolean
pybooleanParse infix boolean expressions to postfix (RPN), evaluate and generate truth tables.CompatibilitiesPython 3.xAny Operating SystemInstallationpyboolean is published onPyPi, so you only need to run the following command:$ pip install pybooleanUsageCreating a new boolean expression:frompybooleanimportBoolExprbool_expr=BoolExpr('1 or 0')Printing the formatted version of the expression:print(bool_expr)# Output: "1 + 0"Evaluating our boolean expression:print(bool_expr.eval())# Output: "1"A boolean expression can also include variables:bool_expr=BoolExpr('1 . m + ( 0 + ! n ) . m')In order to evaluate an expression with variables, the eval() function can take arguments to replace them:print(bool_expr.eval(1,0))# Output: "1"For expressions with variables, one can generate a truth table like so:print(bool_expr.truthtable())# Output:#╔════════════╗#║  m  n  ┃ O ║#║━━━━━━━━╋━━━║#║  0  0  ┃ 0 ║#║────────╂───║#║  0  1  ┃ 0 ║#║────────╂───║#║  1  0  ┃ 1 ║#║────────╂───║#║  1  1  ┃ 1 ║#╚════════════╝Or if you need to manipulate the raw truth table data, generate a dictionary:print(bool_expr.truthdict())# Output: "{('0', '0'): '0', ('0', '1'): '0', ('1', '0'): '1', ('1', '1'): '1'}"Finally, a note on formatting. When creating an expression, all spaces are ignored and many different versions of the operator symbols are accepted, so one can be very 'creative' with the input and not need to worry:bool_expr=BoolExpr('p+1and   ¬m.(1∧p ) ∨notxand~0')print(bool_expr)# Output: "p + 1 . ! m . ( 1 . p ) + ! x . ! 0"Full list of accepted operator symbols:AND:and . & ∧OR:or + | ∨NOT:not ! ~ ¬
Package: pyboondmanager
BoondManager Python connectorThis Python connector is not an officially supported connector by BoondManager. It has been developed by a third-party and it is not officially endorsed or supported by the BoondManager team.BoondManager is an ERP for consulting and IT services companies.This python connector allow you to use the BoondManager API for your custom developments.If you encounter any issues or have questions about this connector, please reach out to the developer or community for support.fromboondmanager.apiimportBoondmanagerClientauth={'user':'YOUR_USER','password':'YOUR_PASSWORD'}client=BoondmanagerClient(basic_auth=auth)projects=client.projects.all()PrerequisitePython >= 3.8InstallationpipinstallpyboondmanagerContributeInstallationAfter cloning this repository and create a virtualenv, install the dependencies :pipinstall-rrequirements/dev.txtThen, generate the documentation :cd docs
make html
open _build/html/index.htmlThe next steps are on it !Links[Documentation] (WIP)VisitBoondmanager websitefor more informations.Boondmanager Support and documentations
Package: py-boost
Py-boost: a research tool for exploring GBDTsModern gradient boosting toolkits are very complex and are written in low-level programming languages. As a result,It is hard to customize them to suit one’s needsNew ideas and methods are not easy to implementIt is difficult to understand how they workPy-boost is a Python-based gradient boosting library which aims at overcoming the aforementioned problems.Authors:Anton Vakhrushev,Leonid Iosipoi,Sergey Kupriyanov.Py-boost Key FeaturesSimple. Py-boost is a simplified gradient boosting library, but it supports all main features and hyperparameters available in other implementations.Fast with GPU. Despite the fact that Py-boost is written in Python, it works only on GPU and uses Python GPU libraries such asCuPyandNumba.Efficient inference. Since v0.4 Py-Boost is able to perform the efficient inference of tree ensembles on GPU. Moreover, ones your model is trained on GPU, it could be converted to perform the inference on CPU only machine via converting to thetreeliteformat with build-in wrapper (limitation - model should be trained withtarget_splitter='Single', which is the default).Easy to customize. Py-boost can be easily customized even if one is not familiar with GPU programming (just replace np with cp).  What can be customized? Almost everything via custom callbacks. Examples: Row/Col sampling strategy, Training control, Losses/metrics, Multioutput handling strategy, Anything via custom callbacksSketchBoostpaperMultioutput training. Current state-of-atr boosting toolkits provide very limited support of multioutput training. And even if this option is available, training time for such tasks as multiclass/multilabel classification and multitask regression is quite slow because of the training complexity that scales linearly with the number of outputs. To overcome the existing limitations we createSketchBoostalgorithm that uses approximate tree structure search. As we show inpaperthat strategy at least does not lead to performance decrease and often is able to improve the accuracySketchBoost. You can try our sketching strategies by usingSketchBoostclass or if you want you can implement your own and pass to theGradientBoostingconstructor asmultioutput_sketchparameter. For the details please seeTutorial_2_Advanced_multioutputInstallationBefore installing py-boost via pip you should have cupy installed. You can use:pip install -U cupy-cuda110 py-boostNote: replace with your cuda version! For the details seethis guideQuick tourPy-boost is easy to use since it has similar to scikit-learn interface. For usage example please see:Tutorial_1_Basicsfor simple usage examplesTutorial_2_Advanced_multioutputfor advanced multioutput featuresTutorial_3_Custom_featuresfor examples of customizationMore examples are coming soon
Package: pyboot
Python project bootstrapBasic scaffolding for python3 CLI projectsUsage$ pyboot cli --help
Usage: pyboot cli [OPTIONS] NAME

  Create project from the cli template.

Options:
  -d, --directory PATH  Destination directory.
  --help                Show this message and exit.Create a CLI project in the current folder:$ pyboot cli myprojLoad the project in a venv:$ python -m venv venv
$ . venv/bin/activate
$ pip install .[dev]
$ pip list
myproj       0.1.0
...Run it:$ myproj --version
proj, version 0.1.0Test it:$ toxContributepybootis built from the same CLI template it generates.Install:$ pip install .[dev]Test:$ toxRelease:$ pip install twine
$ python setup.py sdist bdist_wheel
$ twine upload dist/*TodoWaiting for a editable standardization before moving to PEP517 (See:https://discuss.python.org/t/specification-of-editable-installation/1564/40)
Package: py-bootstrap
微服务配置加载器如何使用frompy_bootstrapimportconfig,get_app_homepageprint(config.get('你的配置'))# config 是由 项目根目录 bootstrap.py 文件内可json化数据与 spring-config服务获取你的服务配置的合并print(get_app_homepage("服务名"))# 可通过该方法获取服务ipbootstrap 文件配置解读app_name应用名称 与 eureka注册,config获取挂钩config_server_namespring-config 服务名eureka_urleureka地址register是否注册eureka(该配置可以从远程的config-file覆盖)profile启动模式(dev|prod)extra_profiles额外的配置文件加载( 逗号隔开会加载所有spring-config-file中的 application-文件名.yml 配置 )其余的可json化配置也会加载到config中,但是会被远程覆盖功能配置加载日志初始化eureka注册服务发现接口
Package: py-bootstrap-cli
py-bootstrap-cliDescriptionpy-bootstrapis a Python CLI application designed for easy setup and management of Python projects. It automates tasks like creating a structured project layout, setting up Poetry, managing virtual environments, and includes optional FastAPI support.FeaturesAutomated Project InitializationVirtual Environment ManagementPoetry IntegrationFastAPI Project SupportFile VerificationRequirementsPython 3.10 or higherPoetry for dependency management (optional)InstallationInstallpy-bootstrapusing pip:pip install py-bootstrapUsageCommands provided bypy-bootstrap-cli:Create a New Projectpbs new [project_name]Options:--default: Use default settings.--fastapi: Initialize a FastAPI app.Check Project Filespbs check_files [project_name]Option:--fastapi: Check FastAPI-specific files.Delete All Project Filespbs delete_all [project_name]Option:--fastapi: Delete FastAPI-specific files.Setup Poetrypbs setup-poetryCreate a Virtual Environmentpbs create_venvUse the Virtual Environmentpbs use_venv
Package: pyborg
Pyborg is a markov chain bot for many protocols (Discord, IRC, Twitter, etc.) that generates replies based on messages and it's database.InstallWe're on the cheeseshop! Yay!pip install pyborgIf you want the latest version from git you'll need to install the project withpoetryin the source directory.Early test/beta releases can be found via:pip install --pre pyborgNote that we're using Python 3.6+ (for fancy type declarations).Python 2 support wasdropped withthe release of pyborg 2.0.There are several extras: "nlp", "systemd", "subtitles", "graphing". Some extras may be rather experimental like "graphing" at time of writing.e.g.pip install pyborg[nlp]Basic Usagepyborgis our new unified pyborg command line interface.DocumentationDocs can be found on RTD.NotesPyborg is skipping version 1.3; this was used for a transitory database/"brain" restructuring that was underwhelming.Ancient original tarballs were hosted at Gna! which is now gone.Thankfully Internet Archive has a copy!Pyborg was originally developed by Tom Morton and Sébastien Dailly.Suggested NLTK dataPyborg can use nltk tagging and tokenizing when installed and configured. Tagging requiresaveraged_perceptron_taggerand tokenization requirespunkt. This needs thenlpextra.
Package: pybot-chatgpt
PYBOT-ChatGPTPython Bot which wraps ChatGPT APIUsesProject Setup:RunCmd:open ~/.secretsUpdate the Secrets File with Token and Save - (Note: ORG_APIKEY_CHATGPT is Optional):export APIKEY_CHATGPT=<Your ChatGPT API Token>export ORG_APIKEY_CHATGPT=<Organistaion API Token>RunCmd:source setup.shUploaded On:https://pypi.org/project/pyhelper-misc/