Saravanan666/pypi-org huggingface dataset:
Package: busypie
Easy and expressive busy-waiting for PythonAlthough you wouldn't want to do much busy waiting in your production code,
testing is a different matter. When testing asynchronous systems,
it's very helpful to wait for some scenario to finish its course.
busypie helps you perform busy waiting easily and expressively.QuickstartMost typical usage will be in test, when we have a scenario
that require us to wait for something to happen.frombusypieimportwait,SECONDdeftest_create_user():create_user_from(USER_DETAILS)wait().at_most(2,SECOND).until(lambda:is_user_exists(USER_DETAILS))DocumentationInstallationUsage GuideLinksThis project drew a lot of inspiration fromAwaitility.
Package: busywork
BusyworkDependency group support usingpyproject.toml. Dependency groups are dependencies
that are used in development that the user does not need to install, such as black
or mypy.UsageAdd this to your pyproject.toml[tool.busywork.groups.linting]# PEP 440 dependency specification.requires=["black","isort"][tool.busywork.groups.typing]requires=["mypy","types-toml"][tool.busywork.groups.dev]# Nested groupsrequires=["nox"]requires-groups=["linting","typing"]Now install the group:busywork install --group group-nameGroups can be separated by a comma for multiple groups.busywork install --group group-one,group-two,group-threeOther options:busyworkinstall--all# Install all dependency groups.busyworkinstall--dependencies# Install the current project's dependencies.busyworkinstall--this# Install the current project. This is the equivalent to `pip install .`# arguments can also be combined, for example:busyworkinstall--this--all# Install the current project and all dependency groups.CreditsDependency groups are inspired by poetry.The CLI visuals are inspired by nox.
Package: but
Your Bunny WroughtYour Bunny Wrought(butfor short) is a collection of scripts
related to file formats and compression.InstallationpipinstallbutCLI Usagebut serve_static [-h HOST] [-p PORT] [DIR]

    Alias: server

but render_template [-t TEMPLATE_DIR] PATTERN [PATTERN ...]

    Alias: template

but run_script SCRIPT

    Alias: runWatching for Changesbut watch_files DEF_FILE

    Alias: watchDEF_FILE is a JSON definition file with the following structure:{"paths":["."],"handlers":[{"patterns":["**/*.py"],"script":["echo","File changed:","{file}"]}]}External Executablesbut ffmpeg -- [ARGS]
but tsc -- [ARGS]Working on Your Bunny Wroughtpython3.12-mvenvvirtual
../virtual/bin/activate
python-mpipinstall-Upip
pipinstall-rrequirements.txtUploading to PyPIpipwheel--no-deps.
twineuploadbut-*-py3-none-any.whl
rm-rbuild/but.egg-info/but-*-py3-none-any.whl
Package: butchertableau
Butcher TableauCalculates theButcher Tableaufor a given order.Calculates A, B, and C for a given order and method.This class calculates the transformation matrix, T, and its respective inverse. (However small errors may exist in the method used)It can also calculate the interpolator coefficients, P, for a cubic polynomial spline.butchertableau.pyExampleCodeimportbutchertableauasbtorder=7X=bt.butcher(order,15)A,B,C=X.radau()Ainv=X.inv(A)T,TI=X.Tmat(Ainv)P=X.P(C)print(P)
Package: buteo
Buteo - Geospatial Analysis Meets AIButeo is a toolbox designed to simplify the process of working with geospatial data in machine learning. It includes tools for reading, writing, and processing geospatial data, as well as tools for creating labels from vector data and generating patches from geospatial data. Buteo makes it easy to ingest data, create training data, and perform inference on geospatial data.Please note that Buteo is under active development, and its API may not be entirely stable. Feel free to report any bugs or suggest improvements.When using, please pin the version of Buteo you are using to avoid breaking changes.For documentation, visit:https://casperfibaek.github.io/buteo/Fibaek, Casper. (2023). Buteo: Geospatial Data Analysis Framework for AI/EO. (v0.9.8). Zenodo.https://doi.org/10.5281/zenodo.7936577Dependenciesnumba(https://numba.pydata.org/)gdal(https://gdal.org/)InstallationUsing pip:pip install gdal
pip install buteoUsing conda:conda install gdal
pip install buteoQuickstartReproject (and other functions) to references. (Vector and raster)importbuteoasbeoOUTDIR="path/to/output/dir"vector_file_correct_projection="path/to/vector/file.gpkg"raster_files_wrong_projection="path/to/raster/files/*.tif:glob"paths_to_reprojected_rasters=beo.reproject_raster(raster_files_with_wrong_projection,vector_file_with_correct_projection,out_path=outdir)paths_to_reprojected_rasters>>>[path/to/output/dir/file1.tif,path/to/output/dir/file2.tif,...]Align, stack, and make patches from rastersimportbuteoasbeoSRCDIR="path/to/src/dir/"paths_to_aligned_rasters_in_memory=beo.align_rasters(SRCDIR+"*.tif:glob",)stacked_numpy_arrays=beo.raster_to_array(paths_to_aligned_rasters_in_memory,)patches=beo.array_to_patches(stacked_numpy_arrays,256,offsets_y=1,# 1 overlap at 1/2 patch size (128)offsets_x=1,# 1 overlap at 1/2 patch size (128))# patches_nr, height, width, channelspatches>>>np.ndarray([10000,256,256,9])Predict a raster using a modelimportbuteoasbeoRASTER_PATH="path/to/raster/raster.tif"RASTER_OUT_PATH="path/to/raster/raster_pred.tif"array=beo.raster_to_array(RASTER_PATH)callback=model.predict# from pytorch, keras, etc..# Predict the raster using overlaps, and borders.# Merge using different methods. (median, mad, mean, mode, ...)predicted=predict_array(array,callback,tile_size=256,)# Write the predicted raster to diskbeo.array_to_raster(predicted,reference=RASTER_PATH,out_path=RASTER_OUT_PATH,)# Path to the predicted raster>>>"path/to/raster/raster_pred.tif"Example ColabsCreate labels from OpenStreetMap dataScheduled cleaning of geospatial dataClip and remove noise from rastersSharpen nightlights dataFilters and morphological operationsThe toolbox is being developed by ESA-Philab, NIRAS, and Aalborg University.Dependenciesgdal
numbaoptional:
orfeo-toolbox
esa-snap
Package: butiran
butiran-pyPython package for simulation of grain-based system using molecular dynamics method and agent-based modelmodulevect3Vect3()Initialize with 3 arguments or nonestrval()Get string value of Vect3 instanceadd()Add two Vect3 instancessub()Substract two Vect3 instancesdot()Dot product of two Vect3 instancescross()Cross product of two Vect3 instancesmul()Multiply a scalar with a Vect3 instance (or vice versa)div()Divide a Vect3 instance with a scalarlen()Get length or magnitude of a Vect3 instanceunit()Get unit vector of a Vect3 instanceneg()Set a Vect3 instance to opposite directionusagefrombutiran.vect3importVect3a=Vect3(100.001,-0.5,2021)print("a = "+a.strval())b=a.neg()print("b = "+b.strval())a = (100.001, -0.5, 2021)
b = (-100.001, 0.5, -2021)
Package: butler
Thebutlerframework aims to make building reliable and high-performance service-oriented
systems easy. The internal communication is done via ØMQ sockets.UsageStart a Router:import butler

router = butler.Router()
router.frontend.bind("tcp://*:5555")
router.backend.bind("tcp://*:5556")
router.run()Register a Service Worker by name:worker = butler.Service('tcp://127.0.0.1:5556', 'api.images')Expose a function on the Service Worker:def resize_image(name, size):
    return 'resized image'

worker.register_function(resize_image)
worker.run()Or expose all the methods of an object:class RPCService(object):
    def resize_image(self, name, size):
        return 'resized image'

worker.register(RPCService())
worker.run()Send a request to a registered Service and receive its response:client = butler.Client('tcp://127.0.0.1:5555').rpc('api.images')
client.resize_image('test.jpeg', '150x180')
client.close()
Package: butler-connect
Access libraries and data definitions for the Buttler project.
For more information ask Oliver Birkholz.Versions0.4.0Adding vhost Support0.4.3Adding sending automatic heartbeats in publisher-function0.4.4update sending automatic heartbeatshandling error0.5.0adding group-types0.5.2adding signalOptionType and groupOptionType with forwardingMQTT0.5.5Solving problems on publishing data without connecting dest0.5.5Solving Problems with createConsumer with same topic an different routing_keys0.6.0add new signal  types f.ex. system_state or set_temperature.heater0.7.0change signal Description and add ext. !!! No backwards compatibility! if Using Schmema for parsing0.7.1Bugifx in Signal().init(.. ext was missing)0.7.2create SignalDirection()add convertFrom to SignalOptionTypeadd different consumptionSignals to SignalType0.7.3add buildingHardware to signalOptionType and groupOptionTypeadd agent_building to groupOptionType0.7.4add SignalType co2add SignalType consumption*Current0.7.5add SignalOptionType SignalOptionType.buildingHardware.energy0.8.0add analytics to GroupOptionTypecreate GroupOptionType_labels0.8.1bugfix in GroupOptionType_labels0.8.2add GroupOptionType_labels toinit0.8.3bugfix in GroupOptionType.agent.building0.8.4bugfix automatic destory of connection on BasicBrokerThreadingConnection on class destroy0.8.5add heating_sub_system to signal and group
Package: buttercms-django
Butter CMS for Djangohttps://www.buttercms.comWhy Butter?Butter makes setting up a company blog on Django insanely easy. It’s built for Django developers to save us from hosting, DB setup, themeing, maintaining yet another Wordpress install. It’s designed to be quickly integrated to an existing Django project.Butter provides a marketing friendly blogging UI, hosted on buttercms.com, and exposes blog content created via an API.This package provides thin wrapper that interacts with the Butter API and a quick start blog application.SetupRequires Python 2.7.9 or newer. If you’re on an older version of 2.7 please take a few seconds to upgrade:https://www.python.org/downloads/$pipinstallbuttercms-django# In settings.py# Add buttercms to INSTALLED_APPSINSTALLED_APPS=(...'buttercms-django',)Grab your API token fromhttps://buttercms.com/profile/# In settings.py# Add your BUTTER_CMS_TOKENBUTTER_CMS_TOKEN='<your_api_token>'Define your blog path# In urls.py# Add your new blog pathurlpatterns=patterns('',...url(r'^blog/',include('buttercms-django.urls')),)Now generate your blog templates into one of your existing Django apps with our handy butter_templates command$pythonmanage.pybutter_templates<app_name>Open up<app_name>/templates/blog/blog_base.htmland update the{% block content %}to match your existing site’s main body content block. Also, if your root parent file is not calledbase.html, updateblog/blog_base.htmlto extend whichever template makes sense for your app.Now check it out:http://localhost:8000/blogLog intohttps://buttercms.com/to start blogging!Sitemap + FeedsYour new blog comes built-in with a sitemap.xml, RSS + Atom feeds for maximum SEO benefit. You can use these to syndicate your content to social networks, improve search engine indexation, and more.Sitemap:http://localhost:8000/blog/sitemap.xmlRSS:http://localhost:8000/blog/rss/(named route blog_rss)Atom:http://localhost:8000/blog/atom/(named route blog_atom)Customizing your blogCustomize the blog templateWe’ve provided you with a pre-scaffolded blog. Simply crack open any of the files in the newly created /blog/ directory you created in the step above and modify those Django template as you would any other. Customizing your blog is now the same flow as the rest of your site! No longer do you have to hack Wordpress PHP scripts.
Package: buttercms-python
buttercms-pythonPython Library for ButterCMS API.DocumentationFor a comprehensive list of examples, check out theAPI documentation.Jump to:PagesCollectionsPostsAuthorsCategoriesFeedsInstallationInstall from PyPi usingpip, a
package manager for Python.pip install buttercms-pythonUsageGetting started with the ButterCMS API couldn't be easier. Use your authorization token to create a ButterCMS client.frombutter_cmsimportButterCMSauth_token="XXXXXXXXXXXXXXXXXXX"client=ButterCMS(auth_token)All methods return dictionaries. The data can be navigated using keys and indexes like the example below.response=client.posts.all()posts=response['data']# posts now contains a list of post dictionariesmy_post=posts[0]# my_post contains the first returned postprint(my_post)# {#   "url": "http://www.example.com/blog/this-is-a-blog-post",#   "created": "2015-06-12T13:59:32.441289Z",#   "published": "2015-06-12T00:00:00Z",#   ...# }PagesThe Page's.all(),.get()and.search()methods accept an optionalparamsto add additional data to the response.client.pages.all('news')# Optional paramsclient.pages.all('news',{'foo':'bar'})client.pages.get('news','hello-world')# Optional paramsclient.pages.get('news','hello-world',{'foo':'bar'})client.pages.search('enter search query',{'page':1,'page_size':10})To TopCollectionsFor a list of params see theAPI documentation.get() method accepts an optionalparamsdict to add additional data to the response.client.content_fields.get(['collection_key'],{'locale':'en'})To TopBlog EnginePostsclient.posts.all({'page_size':3,'page':1,'exclude_body':'true'})client.posts.get('hello-world')client.posts.search('query',{'page':1,'page_size':10})AuthorsThe Author's.all()and.get()methods accept an optionalparamsto add additional data to the response.{'include':'recent_posts'}: Adds each author's posts under therecent_postskey in that author's returned dictionaryclient.authors.all()client.authors.all({'include':'recent_posts'})client.authors.get('jennifer-smith')client.authors.get('jennifer-smith',{'include':'recent_posts'})To TopCategoriesThe Category's.all()and.get()methods accept an optionalparamsto add additional data to the response.{'include':'recent_posts'}: Adds posts tagged with that category under therecent_postskey in that category's returned dictionaryclient.categories.all()client.categories.all({'include':'recent_posts'})client.categories.get('product-updates')client.categories.get('product-updates',{'include':'recent_posts'})To TopTagsThe Tag's.all()and.get()methods accept an optionalparamsto add additional data to the response.{'include':'recent_posts'}: Adds posts tagged with that tag under therecent_postskey in that tag's returned dictionaryclient.tags.all()client.tags.all({'include':'recent_posts'})client.tags.get('product-updates')client.tags.get('product-updates',{'include':'recent_posts'})To TopFeedsclient.feeds.get('rss')client.feeds.get('atom')client.feeds.get('sitemap')To TopOtherView PythonBlog engineandFull CMSfor other examples of using ButterCMS with Python.TestsTo run tests:python-munittestbutter_cms/unit_tests.py
Package: butterflask
ButterFlask-UI is a modern lightweight Python framework for creating highly responsive websites using widgets. It provides a seamless development experience, similar to Flutter, by leveraging the power of widgets to build interactive user interfaces. With ButterFlask-UI, you can easily create elegant and dynamic web applications by composing reusable widgets. The framework simplifies the process of handling API requests in the front-end through AJAX, enabling efficient data exchange and seamless user interactions.Key Features:
- Intuitive widget-based approach for building web interfaces
- Highly responsive design for optimal viewing across different devices
- Effortless handling of API requests
- Simplified widget composition and customization
- Effortlessly handle HTML, CSS, JS in one single Python code
- Increased reusability
- Increased redability (Not like simple HTML/CSS/JS)ButterFlask-UI empowers developers to rapidly create feature-rich web applications with smooth user experiences. Whether you’re building a personal project or a professional web application, ButterFlask-UI offers the flexibility and simplicity you need to bring your ideas to life.
Package: butterfree
ButterfreeA tool for building feature stores. Transform your raw data into beautiful features.SourceDownloadsPageInstallation CommandPyPiLinkpip install butterfreeBuild statusDevelopStableDocumentationSonarMade with :heart: by theMLOpsteam fromQuintoAndarThis library supports Python version 3.7+ and meant to provide tools for building ETL pipelines for Feature Stores usingApache Spark.The library is centered on the following concetps:ETL: central framework to create data pipelines. Spark-basedExtract,TransformandLoadmodules ready to use.Declarative Feature Engineering: care aboutwhatyou want to compute andnot howto code it.Feature Store Modeling: the library easily provides everything you need to process and load data to your Feature Store.To understand the main concepts of Feature Store modeling and library main features you can checkButterfree's Documentation, which is hosted by Read the Docs.To learn how to use Butterfree in practice, seeButterfree's notebook examplesRequirements and InstallationButterfree depends onPython 3.7+and it isSpark 3.0 ready:heavy_check_mark:PyPI hosts reference to a pip-installable module of this library, using it is as straightforward as including it on your project's requirements.pipinstallbutterfreeOr after listingbutterfreein yourrequirements.txtfile:pipinstall-rrequirements.txtDev Package are available for testing using the .devN versions of the Butterfree on PyPi.LicenseApache License 2.0ContributingAll contributions are welcome! Feel free to open Pull Requests. Check the development and contributingguidelinesdescribedhere.
Package: butter-hopper
butter-hopperbutter-hopper is a collection of tools to help making switching between multiple distros easier
butter-hopper allows you to try out  multiple distros on a single large BTRFS partition using BTRFS subvolumes.Features:butter-hopper usessystemd-nspawntoch-bootinto your desired distroAnd if you wanna take it for an actual spin on your hardware you can do that toobutter-hopper will extract boot entries in the OS and present them in GRUB/rEFInd menu.Installing and upgrading various common packages on many distros can be time consuming.butter-hopper usesnixas package management/cache which can be used from any distroSupport installing tools like IDE, VSCode which don't need to be packaged to/commonPLANNED: Using qemu/KVM to boot GUI to another distroPre:requestiesMake sure all of distro use systemd and support btrfs which should be any recent distroFor the boot to hardware you want to make sure you have a recent version of GRUB2 installed withsupport for btrfs.butter hopper toolsbtrhop: The main command line interface to btrhop which allows managing snapshots, running, upgrading distrosbootcrap: Command line tools to bootstrap a distro from rootfs or docker images.mntem: A tool to help manage multiple mount points of BTRFS subvolumes/ shared storage.nixy: Helper script to installnixand setup packages on common/nix/storeSharing:While using a default user of1000and allowing any user to change stuffs in/homeworks.butter-hopperrecommends using differnt user ids of your primary user and supports in the config.To take a sample config ofbutter-hopper.conflook at my dotfile in your preferred Git hosting.Tips:Change your default subvol to "/staging" so you dont accidentally mount the real rootTo do that simply runbtrhop populate-distros --stagingWorkingThis is the layout pulled directly from my current install.
butter-hopper installs distros to/distrosin your btrfs partitiondistros
       ├── arch
       │   ├── @
       │   └── home
       ├── debian-buster
       │   ├── @
       │   └── home
       ├── fedora
       │   ├── @
       │   └── home
       ├── tumbleweed
       │   ├── @
       │   └── home
       ├── ubuntu-latest
       │   ├── @
       │   └── homeLicenseCopyright (c) 2019 Cswl Coldwindcswl1337@gmail.comLicensed under the MIT License. See LICENSE.txt
Package: butterknife
IntroductionButterknife command-line utility can be used to serve snapshots via HTTP;
send and receive snapshots over SSH and multicast;
list local and remote snapshots.InstallationInstall dependencies:sudoapt-getinstalllxcpython3-devcython3python3-pippigzbtrfs-progssudoapt-getinstallpython3-lxc# Ubuntu 14.04 or oldersudopip3installjinja2clickfalconInstall Butterknife:sudopip3installbutterknifeListing templatesList local templates at /var/lib/butterknife/pool:butterknifelistList local templates in a particular directory:butterknifelistfile:///path/to/directoryList templates at /var/lib/butterknife/pool on a remote machine via SSH:butterknifelistssh://hostnameList templates at remote machine via HTTP:butterknifelisthttp[s]://hostname[:port]Pushing/pulling templatesCurrently pull over SSH is working. Following replicates
/var/lib/butterknife/pool from machinehostnameto local pool
at /var/lib/butterknife/pool:butterknifepullssh://hostnameYou can also pull via HTTP:butterknifepullhttp://butterknife.koodur.comNote that symmetric push/pull requires patched btrfs-progs which has additional -p and -C flags for btrfs receive.MulticastSending local template via multicast:butterknifemulticastsend@template\:com.koodur.butterknife.Ubuntu\:x86_64\:snap7You can even multicast a remote subvolume:butterknifemulticastsend@template\:com.koodur.butterknife.Ubuntu\:x86_64\:snap7--poolssh://hostnameReceiving to local pool at /var/lib/butterknife/pool:butterknifemulticastreceivesystemd-nspawn workflowCreate a btrfs subvolume for your butterknife image under /var/lib/machines.
Replace ArchLinux with your image name you want to use.sudobtrfssubvolumecreate/var/lib/machines/ArchLinuxInstall base system in theresudopacstrap-i-c-d/var/lib/machines/ArchLinuxbaseNspawn into it and customize your containersudosystemd-nspawn-MArchLinux# do your thingYou will also need some scripts that will be ran on snapshot creation and when
doing deployments with provision image.look into thepuppet-butterkniferepository for scripts and files you should addCreate butterknife config file in
/var/lib/machines/ArchLinux/etc/butterknife/butterknife.conf[template]name=ArchLinuxAlso make sure that you have something like this on your host
etc/butterknife/butterknife.conf config file[global]namespace=org.example.butterknifeendpoint=https://butterknife.example.orgTake a snapshot of your imagebutterknifenspawnreleaseArchLinuxAnd now you should be ready to serve that image to your clients
Package: butter.mas-api
Butter MAS Python APIHTTP client python API for Butter MAS platform.Getting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.PrerequisitesYou will need to install python 3 in order to use this softwareInstallingpip install -U butter.mas-apiUsagefrombutter.mas.apiimportHttpClientbutterHttpClient=HttpClient('192.168.0.111')# use you robot ip hereresult=butterHttpClient.playAnimation('welcome')print(result.json())Coding style testsLintingPlease use PyLint (Default linter on VS Code) to lint your code before submitting a PR.TestsPlease test your code before submitting a PR.python -m unittest tests/api_test.pyDocumentation👨🏼‍💻API,🖋LicenceContributingPlease readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull requests to us.In order to install along with test tools you will need to run:pipinstall-e.[dev]VersioningWe useSemVerfor versioning. For the versions available, see thetags on this repository.AuthorsBenny MegidishSee also the list ofcontributorswho participated in this project.LicenseThis project is licensed under the Apache 2.0 License - see theLICENSE.mdfile for details