Saravanan666/pypi-org huggingface dataset:
Package: kayobe
Kayobe enables deployment of containerised OpenStack to bare metal.Containers offer a compelling solution for isolating OpenStack services, but
running the control plane on an orchestrator such as Kubernetes or Docker
Swarm adds significant complexity and operational overheads.The hosts in an OpenStack control plane must somehow be provisioned, but
deploying a secondary OpenStack cloud to do this seems like overkill.Kayobe stands on the shoulders of giants:OpenStack bifrost discovers and provisions the cloudOpenStack kolla builds container images for OpenStack servicesOpenStack kolla-ansible delivers painless deployment and upgrade of
containerised OpenStack servicesTo this solid base, kayobe adds:Configuration of cloud host OS & flexible networkingManagement of physical network devicesA friendly openstack-like CLIAll this and more, automated from top to bottom using Ansible.FeaturesHeavily automated using AnsiblekayobeCommand Line Interface (CLI) for cloud operatorsDeployment of aseedVM used to manage the OpenStack control planeConfiguration of physical network infrastructureDiscovery, introspection and provisioning of control plane hardware usingOpenStack bifrostDeployment of an OpenStack control plane usingOpenStack kolla-ansibleDiscovery, introspection and provisioning of bare metal compute hosts
usingOpenStack ironicandironic inspectorVirtualised compute usingOpenStack novaContainerised workloads on bare metal usingOpenStack magnumBig data on bare metal usingOpenStack saharaControl plane monitoring usingPrometheusandGrafana.Log aggregation usingOpenSearchandOpenSearch Dashboards.Documentationhttps://docs.openstack.org/kayobe/latest/Release Noteshttps://docs.openstack.org/releasenotes/kayobe/Bugshttps://bugs.launchpad.net/kayobeCommunityOFTC’sIRC channel: #openstack-kollaLicenseKayobe is distributed under theApache 2.0 License.
Package: kayvee
kayvee-pythonKayvee translates an dictionary into a human and machine parseable string, with a "json" format.UsageThe Kayvee formatter can be used directly or through thekayvee.loggerkayvee formatterimportkayveeaskvprint(kv.format(source="logger-test",level=kv.INFO,title="informational-log-title",dict(id=name_id,context=context_str))kayvee.loggerimportkayvee.loggerasloggerlog=logger.Logger("logger-test")log.info("information-log-title",dict(id=name_id,context=context_str))# Pass global variables:log=logger.Logger("logger-test",default_fields=dict("query"=query))log.info("msg-title")Other functions supported for structured logging:Logger.debugLogger.infoLogger.warnLogger.errorLogger.criticalSupported metrics:Logger.counterLogger.gaugeReleasing a new versionWhen you merge changes for a new version:bump theVERSIONupdateCHANGELOG.mdexplaining the changesafter merging, runpublish.shrequires you to install twine and wheel viapip install <package>creates a git tag associating the version with the commitpublishes the versioned package to pypi (Python package store)If you have any issues, please work with#oncall-infra.3.0.0 (2022-04-18)Upgrade to python 3.9.122.0.2 (2016-05-20)Fix broken Logger. Adddefault_fieldssupport to logger.2.0.1 (2016-01-21)Allow pip installing for all Pip versions. Avoidparse_requirementserror.2.0.0 (2015-10-06)add kayvee.logger support for direct logging; metrics support1.0.1 (2014-01-05)Support for Pip version >= 6, because using parse_requirements1.0.0 (2014-01-05)Change ouput of format to be JSON instead of key=val0.0.2 (2014-10-21)Fix imports to allow pip installation.0.0.1 (2014-09-29)Initial release.
Package: kaz
KazThis cli lets you easily store binary and text items and retrieve them later.Installationpip install kazUsageBasic usage:kazsetkeyvalue
kazgetkey# valueYou can also use stdin and stdout:kazsetlicense<license.txt
kazgetlicense>license2.txtwhich works with binary files:kazset"profile pic"<profile-picture.pngCommands$kaz--help
Usage:kaz[OPTIONS]COMMAND[ARGS]...Simplelocalstoragecli

Options:-h,--helpShowthismessageandexit.-v,--versionShowtheversionandexit.

Commands:getPrintthevalueofanitem.listShowallitemsthatmatch`pattern`.removeRemoveanitem.setBindanametoavalue.AutocompletionTo enable autocompletion, source the script in theautocompletedirectory that corresponds to your terminal. Currently bash, fish and zsh are supported.For bash, this would bewgethttps://raw.githubusercontent.com/clabe45/kaz/master/autocomplete/kaz-autocomplete-bash.sh
.kaz-autocomplete-bash.shAdd the second command to your ~/.bashrc to enable it automatically.ContributingFork this repo!Clone:git clone https://github.com/YOUR-USERNAME/kaz.gitPick an issueoropen a new oneif you have a feature idea.Preferably check out a new feature branch:git checkout -b feature/my-feature(makes the PR process easier)Implement your change.Try to keep your commitsatomic.Push:git push origin FEATURE-BRANCHCreate anew pull request.LicenseLicensed under GNU GPL v3.
Package: kazam
Kazam - Linux Desktop Screen Recorder and BroadcasterKazam is a simple screen recording program that will capture the content of your screen and record a video file that can be played by any video player that supports VP8/WebM video format. Optionally you can record sound from any sound input device that is supported and visible by PulseAudio.InstallationpipinstallkazamKazam need some dependency libraries likedbus,cairoto work, in Ubuntu 22.04, you can use the following command to install them:sudoaptinstallbuild-essentiallibpython3-dev\libdbus-1-devlibcairo2-devlibgirepository1.0-dev-yScreenshotRunning KazamFrom Source CodeIf you want to run Kazam from the source tree, there are a few limitations that you have to take into account. Every icon has to be taken from currently installed icon theme. Toolbars will not show any icons and you will not see Unity AppIndicator.To run Kazam simply execute te following commands in the source tree:$pipinstall-rrequirements.txt$cdbin$./kazamFrom Command LineMake sure~/.local/bininto you PATH, and runkazamin your terminal should work.From GUIIf you already have Kazam installed then Kazam icons will be displayed properly.Keyboard shortcutsSUPER-CTRL-Q-QuitSUPER-CTRL-W-Show/HidemainwindowSUPER-CTRL-R-StartRecordingSUPER-CTRL-F-FinishRecordingIn a normal logitech keyboard, SUPER-CTRL is Ctrl+CMD.Recording TipsChoose small numerb of framerate. My personal setup is framerate equal to 3. Framerates above 20fps are unlikely to work well because of software and hardware limitations. If you increase framerate and framerate in resulting video drops, that is because encoder can’t keep up.Always do a sound check. Especially if you are recording a live commentary with background sound. I got the best results when I used earphones to listen to the audio while recording. This way your mic will not pick up any audio coming from speakers.If you _really_ want loss-less quality, then you will have to record in RAW format. This is possible, but without an SSD with a lot of free space your results will be terrible. 1920x1080 at 15 frames per second will need around 45 MB of disk space per second. Most people will want to record at 20 or 25 frames per second. Most disk will not handle that and your
system will start to crawl.Your next best bet is HUFFYUV format, which is a little bit friendlier on disk bandwidth with 28 MB per second at 15 frames per second. The problem? Not many video editors and players can handle HUFFYUV, let alone video sharing services.Debugging & reporting problemsIf you encounter a bug or any kind of unexpected behavior please try to reproduce it while you run Kazam from standard terminal with –debug option. Please report bugs at (https://github.com/henrywoo/kazam/issues) and include generated output.
Package: kazane
Kazane: simple sinc interpolation for 1D signal in PyTorchKazane utilize FFT based convolution to provide fast sinc interpolation for 1D signal when your sample rate only needs to change by an integer amounts; If you need to change by a fraction amounts, checkoutjulius.Installationpip install kazaneorpip install git+https://github.com/yoyololicon/kazanefor latest version.Usageimportkazaneimporttorchsignal=torch.randn(8,2,44100)# downsample by an amount of 3decimater=kazane.Decimate(3)resampled_signal=decimater(signal)# upsample by an amount of 2upsampler=kazane.Upsample(2)resampled_signal=upsampler(signal)# you can also control number of zeros, roll-off frequency of the sinc interpolation kerneldecimater=kazane.Decimate(3,num_zeros=24,roll_off=0.9)# use other types of window function for the sinc kernelupsampler=kazane.Upsample(2,window_func=torch.blackman_window)Benchmarks on CUDAUsing the benchmark scripts atbench, you can see that FFT can gives some speed improvements when the sample rate changes with some common integer numbers.[---------- Down sample ----------]
               |  julius  |  kazane
2 threads: ------------------------
      rate: 2  |   52.2   |   52.4 
      rate: 3  |   66.5   |   36.1 
      rate: 5  |   94.8   |   30.0 
      rate: 7  |  121.7   |   42.3 

Times are in milliseconds (ms).

[----------- Up sample -----------]
               |  julius  |  kazane
2 threads: ------------------------
      rate: 2  |   48.8   |   39.0 
      rate: 3  |   68.1   |   51.6 
      rate: 5  |  112.5   |   78.9 
      rate: 7  |  159.4   |  108.0 

Times are in milliseconds (ms).
Package: kaze
What is Kaze?Have you downloaded a supervised-learning code-block base, and wish someone
has specified how to download the dataset needed?Kazeis a CLI for managing dataset dependencies in ML projects. It
is a simple, yet powerful tool for managing dataset dependencies in your
project. It is designed to be used in a way that is similar toyarnornpm, but with a focus on thedatasetdependency.InstallationpipinstallkazeExample UsageTo download a dataset, you can use the following command:kazeadd-nflowershttps://www.robots.ox.ac.uk/\~vgg/data/flowers/102/102flowers.tgz--images$DATASETS/jpgthis will populate your.kaze.ymlfile with the following:datasets:-name:flowerssource:https://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgzimages:$DATASETS/jpgYou can also add a dataset from a local file:kaze add mnist.zipUsage and Examples❯kaze--helpUsage:kaze[OPTIONS]COMMAND[ARGS]...Options:--helpShowthismessageandexit.Commands:addlistkaze list❯kazelistmnistat/Users/ge/kaze_debug/mnistkaze add ...❯kazeadd--helpUsage:kazeadd[OPTIONS][SOURCE]Options:-n,--nameTEXT-o,--pathTEXTtargetlocationforthedataset-i,--imagesTEXTimagepath--labelsTEXTlabelpath--voiceTEXTvoicepath--videoTEXTvideopath-q,--quietVerbosemode-z,--unzipDecompressthedataset-v,--verboseVerbosemode--helpShowthismessageandexit.
Package: kaze-boa
===========================================Python compiler for the kaze Virtual Machine===========================================Overview--------The ``kaze-boa`` compiler is a tool for compiling Python files to the``.avm`` format for usage in the `kaze VirtualMachine <https://github.com/kaze-project/kaze-vm/>`__ which is used toexecute contracts on the `kazeBlockchain <https://github.com/kaze-project/kaze/>`__.The compiler supports a subset of the Python language ( in the same waythat a *boa constrictor* is a subset of the Python snake species)What does it currently do^^^^^^^^^^^^^^^^^^^^^^^^^-  Compiles a subset of the Python language to the ``.avm`` format foruse in the `kaze VirtualMachine <https://github.com/kaze-project/kaze-vm>`__-  Works for Python 3.6+-  Adds debugging map for debugging in kaze-python or other kaze debuggersWhat will it do^^^^^^^^^^^^^^^-  Compile a larger subset of the Python languageGet Help or give help^^^^^^^^^^^^^^^^^^^^^-  Open a new`issue <https://github.com/KAZEBLOCKCHAIN/kaze-boa/issues/new>`__ if youencounter a problem.-  Or ping **@localhuman** on the `kaze official communitychatroom <https://discord.gg/R8v48YA>`__.-  Pull requests welcome. New features, writing tests and documentationare all needed.Installation------------Make sure you are using a Python 3.6 or greater virtual environmentPip^^^::pip install kaze-boaDocker^^^^^^This project contains a Dockerfile to batch compile Python smartcontracts. Clone the repository and navigate into the docker subdirectory of the project. Run the following command to build thecontainer:::docker build -t kaze-boa .The kaze-boa Docker container takes a directory on the host containingpython smart contracts as an input and a directory to compile the .avmfiles to as an output. It can be executed like this:::docker run -it -v /absolute/path/input_dir:/python-contracts -v /absolute/path/output_dir:/compiled-contracts kaze-boaThe -v (volume) command maps the directories on the host to thedirectories within the container.Manual^^^^^^Clone the repository and navigate into the project directory. Make aPython 3 virtual environment and activate it via:::python3 -m venv venvsource venv/bin/activateor, to install Python 3.6 specifically:::virtualenv -p /usr/local/bin/python3.6 venvsource venv/bin/activateThen, install the requirements:::pip install -r requirements.txtUsage-----The compiler may be used like in the following example:::from boa.compiler import CompilerCompiler.load_and_save('path/to/your/file.py')Docs----You can `read the docshere <http://kaze-boa.readthedocs.io/en/latest/>`__.Tests-----All tests are located in ``boa_test/test``.  Tests can be run with the following command ``python -m unittest discover boa_test``License--------  Open-source `MIT <LICENSE.md>`__.-  Main author is `localhuman <https://github.com/localhuman>`__.Donations---------Accepted at **ATEMNPSjRVvsXmaJW4ZYJBSVuJ6uR2mjQU**
Package: kaze-python-rpc
==============kaze-python-rpc==============.. image:: https://img.shields.io/pypi/v/kaze-python-rpc.svg:target: https://pypi.python.org/pypi/kaze-python-rpc.. image:: https://img.shields.io/travis/KAZEBLOCKCHAIN/kaze-python-rpc.svg:target: https://travis-ci.org/KAZEBLOCKCHAIN/kaze-python-rpc.. image:: https://readthedocs.org/projects/kaze-python-rpc/badge/?version=latest:target: https://kaze-python-rpc.readthedocs.io/en/latest/?badge=latest:alt: Documentation Status.. image:: https://coveralls.io/repos/github/KAZEBLOCKCHAIN/kaze-python-rpc/badge.svg?branch=master:target: https://coveralls.io/github/KAZEBLOCKCHAIN/kaze-python-rpc?branch=master:alt: Coverage StatusA Python RPC Client for the kaze Blockchain* Free software: MIT license* Documentation: https://kaze-python-rpc.readthedocs.io.Features--------A lightweight RPC Client for the kaze Blockchain
Package: kazuki-help
kazuki_helpFeature Descriptionnew     = 新しいファイルを作成\n
delete  = 指定したファイルを削除\n
edit    = 指定したファイルをEmacsで編集\n
version = ファイルのバージョンを表示\n
init    = プログラムおよび環境を初期化およびセットアップ\n
list    = ファイルを一覧表示\n
command = コマンドの説明を表示\n
git     = Gitを使用した操作\n
set     = 設定を行う\n
Package: kazurator
A python port of theShared Reentrant Read Write Lockrecipe fromcurator. This package dependends onkazoofor
handling the zookeeper bits.At this point, you’re probably wondering why I didn’t just use the existing locks recipe fromkazoo. The original
goal was to makecuratorandkazoorespect each other’s locks such that:Either could acquire a read lock, so long as neither held a write
lockEither could acquire a write lock, and block either from acquiring
either read/write locksI first attempted to patch the locks recipe inkazoo, but the internals are a bit different. (READ:I wasn’t
able to make it work right).The reason this is necessary (at least for me), is that some code was running Scala and usingcurator, and other code
was running Python usingkazoo.Installationpip install kazurator==0.2.0UsageThere are two main use cases for this package. Both of them relate to
creating an inter-process critical region.Example of Interop with CuratorSee theexampledirectory.Inter Process MutexTo start, let’s take a look at how we could implement a simple shared
(across process) mutex:fromkazoo.clientimportKazooClientfromkazuratorimportMutexdefmain():client=KazooClient(hosts="YOUR_ZK_CONNECT_STRING_HERE")client.start()mutex=Mutex(client,"/some/path")withmutex:# do your thread-safe thing hereclient.stop()This example assumes you want a single thread to be in the critical
region. In order to support simultaneous multi-threaded access, you can
set themax_leaseskwargto a higher number. For example:mutex=Mutex(client,"/some/path",max_leases=2)# 2 thread at a timeAlso, if you’d rather not use the content management protocol, you can
callacquireandreleasedirectly.Inter Process Read Write LockIn some cases, you’ll need to support an unlimited number of read locks,
but only a single write lock. For example, suppose you were processing
some HDFS paths by altering the format and replacing the data (totally
hypothetical of course :smile:).You’d want any consumers of the data to acquire a read lock. This would
prevent the altering process from acquiring a write lock until the
consumer(s) are finished. Similarly, the consumers wouldn’t be able to
acquire read locks until the altering process removes the write lock.Consumers will block until the lock is available, or timeout after the
specifiedtimeout(default is1s), at which point akazoo.LockTimeoutis raised.fromkazoo.clientimportKazooClientfromkazuratorimportReadWriteLockdefmain():client=KazooClient(hosts="YOUR_ZK_CONNECT_STRING_HERE")client.start()# can optionally supply `timeout` kwarg as welllock=ReadWriteLock(client,"/some/path")withlock.write_lock:# block until write_lock is available# do your thread-safe thing herewithlock.read_lock:# block until write_lock is gone# do your thread-safe thing hereclient.stop()DevelopmentClone this repo andpip install-rrequirements.txtRun testsscript/test nosetestsRunning the tests will spawn a docker container to run zookeeper in. It
will be shutdown automatically at the end of the run
Package: kb4.py
kb4.py: A Python wrapper for the KnowBe4 APIkb4.py is a simple Python wrapper for the KnowBe4 reporting API.For more information on the API, please visit theKnowBe4 API documentation.Installationpipinstallkb4.pyUsagefromkb4importKnowBe4KB4_API_KEY='<Secret key obtained from KnowBe4 website.>'kb4=KnowBe4(KB4_API_KEY)kb4.account()kb4.users()kb4.user(USER_ID)kb4.groups()kb4.group(GROUP_ID)kb4.group_members(GROUP_ID)kb4.phishing_campaigns()kb4.phishing_campaign(CAMPAIGN_ID)kb4.phishing_security_tests()kb4.phishing_campaign_security_tests(CAMPAIGN_ID)kb4.phishing_campaign_security_test(PST_ID)kb4.phishing_campaign_security_test_recipients(PST_ID)kb4.phishing_campaign_security_test_recipient(PST_ID,USER_ID)kb4.store_purchases()kb4.store_purchase(STORE_PURCHASE_ID)kb4.policies()kb4.policy(POLICY_ID)kb4.training_campaigns()kb4.training_campaign(CAMPAIGN_ID)kb4.training_enrollments()kb4.training_enrollment(ENROLLMENT_ID)ContributingAll contributions are welcome and appreciated.ForkFeature branchPull requestMetaJosh Thomas -josh@joshuadavidthomas.comDistributed under the MIT license. SeeLICENSEfor more information.https://github.com/elderlydoofus/kb4.py
Package: kba
This is the Translator Knowledge Beacon Aggregator web service application programming interface (API) that provides integrated access to a pool of knowledge sources publishing concepts and relations through the Translator Knowledge Beacon API. This API is similar to that of the latter mentioned API with the addition of some extra informative endpoints plus session identifier and beacon indices. These latter identifiers are locally assigned numeric indices provided to track the use of specific registered beacons within the aggregator API itself.
Package: kbase-jsonrpcbase
JSONRPCBaseNOTE: This is a fork oflevel12/jsonrpcbasewith changes maintained by KBaseSimple JSON-RPC service without transport layerThis library is intended as an auxiliary library for easy an implementation of JSON-RPC services with Unix/TCP socket
like transport protocols that do not have complex special requirements. You need to utilize some suitable transport
protocol with this library to actually provide a working JSON-RPC service.FeaturesEasy to use, small size, well tested.Supports JSON-RPC v2.0. Compatible with v1.x style calls with the exception of v1.0 class-hinting.Optional argument type validation that significantly eases development of jsonrpc method_data.ExampleExample usage:importjsonrpcbasechat_service=jsonrpcbase.JSONRPCService()deflogin(username,password,timelimit=0):# (...)returnTruedefreceive_message(**kwargs):# (...)returnchat_messagedefsend_message(msg):# (...)passif__name__=='__main__':# Adds the method login to the service as a 'login'.chat_service.add(login,types=[basestring,basestring,int])# Adds the method receive_message to the service as a 'recv_msg'.chat_service.add(receive_message,name='recv_msg',types={"msg":basestring,"id":int})# Adds the method send_message as a 'send_msg' to the service.chat_service.add(send_message,'send_msg')# Receive a JSON-RPC call.jsonmsg=my_socket.recv()# Process the JSON-RPC call.result=chat_service.call(jsonmsg)# Send back results.my_socket.send(result)DevelopmentInstallpoetryand runpoetry install.Run tests withmake test.Deploy withpoetry buildandpoetry publish.CreditsThis project was originally developed by Juhani Åhman.
Package: kbastroutils
KBAstroUtilsThis is a collection of modules for analyses related to astrophysics by Dr. Kornpob Bhirombhakdi. Contact:kbhirombhakdi@stsci.eduTasks include:- dqmask.py = handle data quality (DQ) arrays

- container.py = handle declaring attibutes given keys and values

- segmentation.py = wrapper for making a segmentation map (used to be source.py in version < 1.2.0)

- sub2full.py = mapping a subarray image back to its full frame

- gnd.py = wrapper, and main class for grism reduction

- grismconf.py = read conf file for grism reduction

- grismsens.py = read sens file for grism reduction

- grismapcorr.py = read aperture correction table, and calculate aperture correction factor

- grismmeta.py = construct meta info

- make_sip.py = calculate Simple Imaging Polynomial (SIP)

- photapcorr.py = read photometric calibration table, and prepare calibration factors (i.e., encircled energy correction, and photometric AB zeropoint)

- mag2flux.py = converting ABmag to flamKnown issues:- ACS photometric zeropoints are set to 0.

- Memory

- No description/documentation

- Handling other telescopes

- flux2mag.py

- AB2Vega.py

- Vega2AB.py

- WFC3 subarray

- drz gets brighter about 10%

- test new code with ACS-WFC, and other WFC3-IRNext- Combine 1D spectra

- Use assertion for error handling (http://swcarpentry.github.io/python-novice-inflammation/09-defensive/index.html)

- HST-ACS-WFC flux calibration

- Fix drz getting brigtherv.2.5.0- Implement ACS subarray

- Implement ('HST','ACS','WFC') as identifier

- Implement grismcrclean.py, grismmodel.py, grismcalibpath.py, grismdrz.py

- Change grismconf.py, grismsens.py, gnd.py, dqmask.pyv.2.4.0- Implement grismmeta.py

- HST-ACS-WFC is available for wavelength calibration, but not flux calibration

- HST-ACS-WFC subarray is applicable

- Change the construction of GrismCONF, and GrismSensv.2.3.0- GND.make_pairs(pairs=None) can automatically make pairs

- GND.make_xyd(xyd=None, init=init, inittype='radec') can automatically find a centroid given init

- GND.show(method='meta', output=True) returns the table instead of displaying it

- GND.make_drz() is deactivatedv.2.2.0- GND.save takes filename

- Fix GND.show(method='flam') handling xmin, xmax

- GND.show(method='stamp') takes dqonv.2.1.0- Add functionalities in GND.show(method='clean', traceon=True, zoom=True)

- Implement: photapcorr.py, mag2flux.pyv.2.0.0- New APIs: gnd.py

- Demo provided

- Implement: grismapcorr.py, make_sip.pyv.1.3.1- Fix importv.1.3.0- Implement: grismconf.py, gnd.py, grismsens.pyv.1.2.0- Implement: sub2full.py

- Change: source.py to segmentation.py

- Fix description: segmentation.pyv.1.1.0- Implement: container.py, source.py

- Fix description: dqmask.pyv.1.0.2- Fix description of dqmask.pyv.1.0.1- Fix import modules in dqmask.pyv.1.0.0- Implement dqmask.py