Saravanan666/pypi-org huggingface dataset:
Package: bootstrap-builder
Bootstrap BuilderThis is just a small lil package to make building HTML with Pythonhopefullya lil easier.>>>importbootstrap_builderasbb>>>page=bb.HTMLPage(title="Bootstrap Builder")# Make a page containing the head and body and etc>>>>>># Time for navbar>>>navbar_container=page.new_navbar()>>>print(navbar_container)# <nav class="navbar" />>>>navbar_container['class'].append('navbar-expand-sm')# Add 'navbar-expand-sm' to the class list>>>print(navbar_container)# <nav class="navbar navbar-expand-sm" />>>>withnavbar_container.new_navbar_nav()asnavbar:_=navbar.new_navbar_nav_item().new_child('a','Index',attrs={'href':'/index','class':'nav-link'})# You can add attrs inside new_XXX methods if you don't want to do it via __setitem__>>>print(navbar_container)# <nav class="navbar navbar-expand-sm" ><ul class="navbar-nav"><li class="nav-item"><a href="/index" class="nav-link">Index</a></li></ul></nav>>>>>>>child=page.new_child('div')>>>child['data-test']='test1'# Add a new attr>>>child['class'].append('owo')# Add a new class>>>print(child)# <div data-test="test1" class="owo" />>>>>>># Add some content to the tag>>>child=page.new_child('div')>>>_=child.new_child('p','Nested Tag')# This is a nested tag>>>print(child)# <div><p>Nested Tag</p></div>>>>>>># We can also just add plain content>>>child=page.new_child('div')>>>_=child.add_child('p','New Child')# Like .new_child, but doesn't create a new object, just adds it to the object>>>print(child)# <div>New Child</div>>>>
Package: bootstrap-discord-bot
Bootstrap Discord BotAn opinionated cli-tool to bootstrap\generate your discord bot code!DemoFeaturesSupport for code with/without cogs..env file support for keys and .gitignore to hide it.Bot code pre-built with a utility ping function to get you started!Setup new cogs using cli.UsageInstall usingpip install bootstrap-discord-botcd into your bot directoryRunbootstrap-discord-bot -hto get helpRunbootstrap-discord-bot --cogsto initialize a discord bot with cogs.Runboostrap-discord-bot --nocogsto initialize a discord bot without cogs.Runbootstrap-discord-bot --makecog cog_name file_nameto initialize a file in the cogs folder with required cog boilerplate.Built usingPythonDiscord.pyargparsesetuptools
Package: bootstraphistogram
bootstraphistogramA python package that provides a multi-dimensional histogram with automatic Poisson bootstrap re-sampling.InstallationInstall with pip from PyPI:python-mpipinstallbootstraphistogramUsage InstructionsFor usage instructions and examples see the documentation at:https://bootstraphistogram.readthedocs.io.Development InstructionsFor Linux systems, the provided setup script will setup a suitable python virtual environment
and install pre-commit-hooks.sourcesetup.shAlternatively, aDockerfileis provided for a consistent development environment.dockerbuild-tbootstraphistogram:latest.&&\dockerstartbootstraphistogram&&\dockerrun--namebootstraphistogram-it-dbootstraphistogram:latest/bin/bashThis package usesPython poetryfor dependency management.poetryinstallTo run the unit tests run:poetryrunpytestTo build documentation run:poetryrunpipinstall-rdocs/requirements.txt&&\poetryrunsphinx-build-Wdocsdocs-buildTo auto-build the documentation while editing:poetry run pip install sphinx-autobuild && sphinx-autobuild docs docs/_build/htmland find your documentation onhttp://localhost:8000.To generate a test coverage report run:poetryruncoveragerun-mpytesttests&&poetryruncoveragereport-m
Package: bootstrapi
BootstrAPIAutomatically generate an OpenAPI for your existing database.FeaturesGenerates an API for your current databaseGenerates OpenAPI docsSupports OData-like queries ($select, $filter, $expand)Support all RESTful HTTP methods (GET, POST, PUT, PATCH, DELETE)Supports all Databases supported by SQLAlchemyRequirementsPythonPython 3.6+DatabaseExactly one primary key per tableQuickstartThere are 3 main ways to run the code. As a standalone Python app, as a package and a Docker container.Python AppFirst clone the code into a folder, then run:$pipinstall-rrequirements.txt$python-mrunconnection_string[--title"API"][--host0.0.0.0][--port8000][--schemadbo]Python PackageInstall the bootstrapi package$pipinstallbootstrapiThen create an FastAPI app and register the routes.importuvicornfromfastapi.applicationsimportFastAPIfromsqlalchemyimportcreate_enginefromsqlalchemy.ext.automapimportautomap_basefrombootstrapi.routerimportBootstrAPIRouterconnection_string=""#Your SQLAlchemy Connection string hereengine=create_engine(connection_string)app=FastAPI(title='My API')Base=automap_base()Base.prepare(engine,schema=schema)router=BootstrAPIRouter(engine,Base)app.include_router(router)uvicorn.run(app,host='127.0.0.1',port=8000)Docker$dockerrun--envconnection="YOUR_CONNECTION_STRING"-p8000:8000mrpowerus/bootstrapi
Package: bootstrappable-cloud9
Bootstrappable Cloud9 Instance with SSMSimple stack example:exportclassCloud9EnvironmentExampleStackextendscdk.Stack{constructor(scope:Construct,id:string,props?:cdk.StackProps){super(scope,id,props);//CreateL2Cloud9Environmentconstenvironment=newCloud9Environment(this,"environment",{name:"example-environment",description:"An example environment",imageId:Cloud9AmiType.AMZN_LINUX_2,connectionType:Cloud9ConnectionType.SSM,ownerArn:"<YOUR_ARN>",});//ExistingCodeCommitRepositoryconstrepository=Repository.fromRepositoryName(this,"test","test-repo");//CloneGitRepositorieswithinCloud9Environmentenvironment.cloneCodeCommitRepo(repository,"test");environment.cloneGitRepo("https://github.com/aws-samples/aws-copilot-pubsub","copilot");environment.addInitCommands(["sudo yum update -y","sudo yum install -y jq"]);//-----------------------------------------------------//-Outputs-//-----------------------------------------------------newcdk.CfnOutput(this,"environmentUrl",{value:environment.environmentUrl,description:"The URL of the environment",});}}
Package: bootstrapped
bootstrappedis a Python library that allows you to build confidence
intervals from data. This is useful in a variety of contexts - including
during ad-hoc a/b test analysis.Motivating Example - A/B TestImagine we own a website and think changing the color of a ‘subscribe’
button will improve signups. One method to measure the improvement is to
conduct an A/B test where we show 50% of people the old version and 50%
of the people the new version. We can use the bootstrap to understand
how much the button color improves responses and give us the error bars
associated with the test - this will give us lower and upper bounds on
how good we should expect the change to be!The Gist - Mean of a SampleGiven a sample of data - we can generate a bunch of new samples by
‘re-sampling’ from what we have gathered. We calculate the mean for each
generated sample. We can use the means from the generated samples to
understand the variation in the larger population and can construct
error bars for the true mean.bootstrapped - BenefitsEfficient computation of confidence intervalsFunctions to handle single populations and a/b testsFunctions to understandstatistical
powerMultithreaded support to speed-up bootstrap computationsDense and sparse array supportExample Usageimportnumpyasnpimportbootstrapped.bootstrapasbsimportbootstrapped.stats_functionsasbs_statsmean=100stdev=10population=np.random.normal(loc=mean,scale=stdev,size=50000)# take 1k 'samples' from the larger populationsamples=population[:1000]print(bs.bootstrap(samples,stat_func=bs_stats.mean))>>100.08(99.46,100.69)print(bs.bootstrap(samples,stat_func=bs_stats.std))>>9.49(9.92,10.36)Extended ExamplesBootstrap
IntroBootstrap A/B
TestingMore notebooks can be found in theexamples/directoryRequirementsbootstrappedrequires numpy. The power analysis functions require
matplotlib and pandas.InstallationpipinstallbootstrappedHow bootstrapped worksbootstrappedprovides pivotal (aka empirical) based confidence
intervals based on bootstrap re-sampling with replacement. The
percentile method is also available.For more information please see:Bootstrap confidence
intervals(good intro)An introduction to Bootstrap
MethodsThe Bootstrap, Advanced Data
AnalysisWhen the bootstrap dosen’t
work(book)An Introduction to the
Bootstrap(book)Bootstrap Methods and their
ApplicationSee the CONTRIBUTING file for how to help out.ContributorsSpencer Beecher, Don van der Drift, David Martin, Lindsay Vass, Sergey
Goder, Benedict Lim, and Matt Langner.Special thanks to Eytan Bakshy.Licensebootstrappedis BSD-licensed. We also provide an additional patent
grant.
Package: bootstrapped-ng
bootstrappedis a Python library that allows you to build confidence
intervals from data. This is useful in a variety of contexts - including
during ad-hoc a/b test analysis.Motivating Example - A/B TestImagine we own a website and think changing the color of a ‘subscribe’
button will improve signups. One method to measure the improvement is to
conduct an A/B test where we show 50% of people the old version and 50%
of the people the new version. We can use the bootstrap to understand
how much the button color improves responses and give us the error bars
associated with the test - this will give us lower and upper bounds on
how good we should expect the change to be!The Gist - Mean of a SampleGiven a sample of data - we can generate a bunch of new samples by
‘re-sampling’ from what we have gathered. We calculate the mean for each
generated sample. We can use the means from the generated samples to
understand the variation in the larger population and can construct
error bars for the true mean.bootstrapped - BenefitsEfficient computation of confidence intervalsFunctions to handle single populations and a/b testsFunctions to understandstatistical
powerMultithreaded support to speed-up bootstrap computationsDense and sparse array supportExample Usageimportnumpyasnpimportbootstrapped.bootstrapasbsimportbootstrapped.stats_functionsasbs_statsmean=100stdev=10population=np.random.normal(loc=mean,scale=stdev,size=50000)# take 1k 'samples' from the larger populationsamples=population[:1000]print(bs.bootstrap(samples,stat_func=bs_stats.mean))>>100.08(99.46,100.69)print(bs.bootstrap(samples,stat_func=bs_stats.std))>>9.49(9.92,10.36)Extended ExamplesBootstrap
IntroBootstrap A/B
TestingMore notebooks can be found in theexamples/directoryRequirementsbootstrappedrequires numpy. The power analysis functions require
matplotlib and pandas.InstallationpipinstallbootstrappedHow bootstrapped worksbootstrappedprovides pivotal (aka empirical) based confidence
intervals based on bootstrap re-sampling with replacement. The
percentile method is also available.For more information please see:Bootstrap confidence
intervals(good intro)An introduction to Bootstrap
MethodsThe Bootstrap, Advanced Data
AnalysisWhen the bootstrap dosen’t
work(book)An Introduction to the
Bootstrap(book)Bootstrap Methods and their
ApplicationSee the CONTRIBUTING file for how to help out.ContributorsSpencer Beecher, Don van der Drift, David Martin, Lindsay Vass, Sergey
Goder, Benedict Lim, and Matt Langner.Special thanks to Eytan Bakshy.Licensebootstrappedis BSD-licensed. We also provide an additional patent
grant.
Package: bootstrap-py
Thebootstrap-pyprovides generating the Python packages.StatusRequirementsPython 3.9 overFeaturesGenerating Python package.Checking Python package name existence at PyPI.Test and conde checking environment is configured with theTox,Pytest, and others.pycodestylepytest-covpytest-flake8pydocstyleGenerating documentation automatically with theSphinx.Configuration Git repository, initial commit.Generate sample code using--with-samplesoption.UsageInstall bootstrap-pyInstall bootstrap-py:$ python -m venv .venv
$ python -m pip install bootstrap-pyGenerate Python packageGenerate your Python package.:$ .venv/bin/bootstrap-py create -a 'Your author name' -e 'your-author-email@example.org' \
-u 'https://your-package-website.example.org' -o '/path/to/package-dir' \
-l 'select-the-license' 'your-package-name'
$ ls -a package-dir
.  ..  .coveragerc  .git  .github  .gitignore  MANIFEST.in  README.rst  docs  pyproject.toml  src  tox.ini  utilsList license choicesList license description choices.:$ .venv/bin/bootstrap-py list -l
GPL        : GNU General Public License (GPL)
CPL        : Common Public License
IOSL       : Intel Open Source License
GPLv3+     : GNU General Public License v3 or later (GPLv3+)
(omit)Using toxRunning test with tox:$ pip install --user tox
$ tox
(omit)
_______________________________________ summary ______________________________________
py311: commands succeeded
flake8: commands succeeded
pycodetyle: commands succeeded
pydocstyle: commands succeeded
docs: commands succeeded
congratulations :)
Package: bootstrap_vi
bootstrap_viBootstrap Virtualenv on system without pip or easy_installIt isn’t terribly hard to get virtualenv installed, but often times it would be
much easier to be able to just use a single quick command to get a virtualenv up and
running.Also, putting the instructions on how to setup a virtualenv in installation
instructions is a bit redundant and not concise enough for novice python users.This project allows you to very easily use a single command to bootstrap a
virtualenv to solve these issues.Bootstrapping a virtualenvThe idea is to be as simple as possible on a single line you can include in your
installation instructions$>wgethttps://raw.githubusercontent.com/necrolyte2/bootstrap_vi/master/bootstrap_vi.py-O-|python-This would setup a virtualenv in the current directory under the venv directory.
You can supply any of the virtualenv’s arguments after the word python and they will
be passed on to the virtualenv call.If you want to change the virtualenv’s directory and prompt for example:$>wgethttps://raw.githubusercontent.com/necrolyte2/bootstrap_vi/master/bootstrap_vi.py-O-|python-envdir--prompt="(myenv)"This would then create the virtualenv in the envdir directory and set the prompt
for the environment to(myenv)$PS1Bootstrapping using setuptoolsYou can also bootstrap virtualenv in a python project by leveraging setup.py.You just have to include the following inside yoursetup.pysetup_requires=['bootstrap_vi']Now you can simply put in your installation docs the following to easily bootstrap
virtualenv for your project.This would be very similar to the first wget call above.$>pythonsetup.pybootstrap_virtualenvSimilarity to the second wget example above, you can pass any virtualenv arguments$>pythonsetup.pybootstrap_virtualenvenvdir--prompt="(myenv)"This would also create a virtualenv in the envdir directory and set the prompt to(myenv)$PS1
Package: bootstrapy
A bootstrap application that takes away the pain of setting up a python application for production and lets you focus on coding instead.Usage#Sets up environment and installs dependencies
$ make env

#Activate the environment
$ . env/bin/activate

#Shows the list of commands available
$ make help

  env         create a development environment using virtualenv
  deps        install dependencies
  clean       remove unwanted stuff
  lint        check style with flake8
  test        run all your tests using nose
  production  run test suite and do a release
  release     package and upload a release
  sdist       packageSetup NotesIn order to be able to generate PDF and PS files from reStructuredText using LaTeX on Debian-based systems you need to install the following software:sudoapt-getinstall wgetbuild-essentialpython2.5-devtexlive-fullUsewebhooksto update docs atRead The Docswhenever you make a commit to your project.Licensebootstrapy is BSD licensed, so feel free to use it as you like.
Package: boot-synth
![synth logo](assets/synth_logo.png)# synth - a Docker bootstrapping toolRead more about the creation of synth [here](https://medium.com/@jackgindi/the-making-of-synth-4045b1a746f4)Inspired by how long it can take to set up the development of web apps, synth is a tool to help you build modular sets of Docker, Docker Compose, and CI/CD pipeline config files, as well as directory trees and wireframed files for different front- and backend web frameworks. synth can help you set up your next web application project in seconds, allowing you to start coding your idea with zero hassle.## :warning: Dependenciesdockeranddocker-composepython3## :arrow_down: InstallationInstall via pip:` pip3 installboot-synth`## :triangular_flag_on_post: Usage` synth create [OPTIONS] `![synth demo](assets/synth_basic_demo.gif)## :hammer_and_wrench: Options`--frontend,-f`Your frontend framework. Currently supported options arestatic,dynamic,react.`--backend,-b`Your backend framework. Currently supported options areflask,node,django.`--database,-d`Your choice of database. Currently supported options aremysql,postgres,mongo,mariadb.`--cache,-c`Your choice of caching tool. Currently supported options areredisandmemcache.`--pipeline,-p`Your choice of CI/CD pipeline.travisis currently the only supported option.## :books: Coding Style TestsStrictly followedpep8style guide. To install:### Regular Ubuntu 14.04 installWith apt-get` $ sudoapt-getinstallpython3-pep8`With pip3` $ pip3 install pep8 `### Check The Version` $ pep8--version1.7.1 `## :pencil: Version1.1.0## ContributingSee [CONTRIBUTING.md](CONTRIBUTING.md)## :blue_book: AuthorsBennett Dixon- [@BennettDixon](https://github.com/BennettDixon) - [Portfolio](https://bencodesit.com)Jack Gindi- [@jmgindi](https://github.com/jmgindi) - [Portfolio](https://jackgindi.com)## :mag: LicenseThis project is licensed under the MIT License - see the [LICENSE](LICENSE.txt) file for details## :mega: Acknowledgments[Holberton School](https://github.com/holbertonschool) (providing guidance)[Julian Gindi](https://github.com/JulianGindi) (project mentor)
Package: bootwrap
Python BootwrapBootwrapis a Python library for rapid developing of web-based user interfaces (WebUI). It helps creating WebUI using Python code only and can be used in conjunction with different web-development frameworks such asFlask. Under the hood, this library wraps one of the most popular front-end toolkitBootstrap.This library would be useful for developers and data scientists who wish to build interactive web-application without crafting HTML, CSS and Javascript.As a showcase of what this library is capable of please check the documentation. The entiredocumentationweb interface is created using theBootwrap.InstallingInstall and update usingpip:~$pipinstallbootwrapA Simple ExampleThe following code will care three pages application with the top-level menu bar for navigations.fromflaskimportFlask,MarkupfrombootwrapimportPage,Menu,Image,Anchor,Button,Text# Both 'logo.png' and 'favicon.ico' are stored in 'docs' folderapp=Flask(__name__,static_folder='docs',static_url_path='')classMyMenu(Menu):def__init__(self):super().__init__(logo=Image('logo.png',width=32,alt='Logo'),brand=Text('Bootwrap').as_strong().as_light().ml(2),anchors=[Anchor('Home').link('/'),Anchor('About').link('/about')],actions=[Button('Sign In').as_outline().as_light().link('/signin')])classMyPage(Page):def__init__(self,container):super().__init__(favicon='favicon.ico',title='Hello World Application',menu=MyMenu(),container=container)@app.route('/')defhome():returnMarkup(MyPage(container=[Text('Home').as_heading(1)]))@app.route('/about')defabout():returnMarkup(MyPage(container=[Text('About').as_heading(1)]))@app.route('/signin')defsignin():returnMarkup(MyPage(container=[Text('Sign In').as_heading(1)]))if__name__=='__main__':app.run(debug=True)Use the following command to launch the application.$flaskrun*Runningonhttp://127.0.0.1:5000/(PressCTRL+Ctoquit)The result should look like.ContributingFor information on how to set up a development environment and how to make a contribution to Bootwrap, see thecontributing guidelines.LinksDocumentation:https://mmgalushka.github.io/bootwrap/PyPI Releases:https://test.pypi.org/project/bootwrap/Source Code:https://github.com/mmgalushka/bootwrapIssue Tracker:https://github.com/mmgalushka/bootwrap/issues
Package: booty
This code base is for creating a bootloader to be utilized for programming microcontroller flash
memory.  This program takes a hex file as the input and will parse that hex file and send it to
the microcontroller bootloader over a serial UART (commonly referred to as a “serial port” or
“COM port”).The companion project to this is thebootypicproject.
Hopefully, more will follow.  GUI provided atbootycontrol.Please see thedocumentationfor further guidance!
Package: boox-annotation-parser
Parse annotation file exports from your Onyx Boox device, and transform them into JSON, YAML, or whatever you want.Free software: MIT licenseInstallationpip install boox-annotation-parserYou can also install the in-development version with:pip install https://github.com/coddingtonbear/boox-annotation-parser/archive/master.zipDocumentationTo use the project from the command-lineOptions:--input: (Default: ‘stdin’) The path to your Boox annotation export file.  If unspecified, reads from stdin.--output: (Default: ‘stdout’) The path to where you’d like your output written to.  If unspecified, writes to stdout.--format: (Default: ‘yaml’) The format you’d like your annotations written out in.  Options include:
-yaml-json-nljsonboox-annotation-parser --input=/path/to/boox/export.txt --output=/path/to/write/output/to --format=yamlTo use the project as a libraryfromboox_annotation_parserimportparserwithopen('/path/to/boox/export.txt','r')asinf:parsed=parser.get_annotations(inf)DevelopmentTo run the tests run:pytestChangelog0.1.0 (2021-08-19)First release on PyPI.
Package: boox-annotations-to-anki
Create cards in Anki from your Onyx Boox annotations.Free software: MIT licenseInstallationpip install boox-annotations-to-ankiYou can also install the in-development version with:pip install https://github.com/coddingtonbear/boox-annotations-to-anki/archive/master.zipRequirementsAnkiConnect:https://ankiweb.net/shared/info/2055492159DocumentationYou can import your cards into Anki by running:boox-annotations-to-anki MY_DECK_NAME --input=/path/to/boox/annotations.txtChangelog0.1.0 (2021-08-19)First release on PyPI.
Package: booyah
Booyah FrameworkBooyah is a MVC web framework written in Python that aims to be easy to use while setting up projects quickly. It follows the convention over configuration paradigm introduced by Ruby on Rails.DocumentationThe following links detail the process of installing and setting up your first project.1. Installation2. Getting Started3. The Booyah Console4. The Booyah ORM5. The MVC architecture6. Database Operations7. Generators8. JobsCollaboratingYou can collaborate with Booyah by forking the repo and submitting a pull request. We are very early stage so any help is welcome. We do try to follow the Rails way in Python though, so please keep that in mind.You can visit the issues tab in github for some ideas.