Saravanan666/pypi-org huggingface dataset:
Package: kasami
A library for training and applying probabilistic context-free grammars totext.* Kasami, T. (1965). An efficient recognition and syntax analysis algorithmfor context-free languages. (No. Scientific-2). Hawaii University, Dept. ofElectrical Engineering.# Example use```python>>> from bllipparser import RerankingParser>>>>>> from kasami import TreeScorer>>> from kasami.normalizers import bllip>>>>>> # Loading WSJ-PTB3 treebank into bllip's RerankingParser... bllip_rrp = RerankingParser.fetch_and_load('WSJ-PTB3')>>> bllip_parse = lambda s: bllip.normalize_tree(bllip_rrp.parse(s)[0].ptb_parse)>>>>>> tree = bllip_parse("I am a little teapot")>>> print(tree)(S1 (S (NP (PRP 'I')) (VP (VBP 'am') (NP (DT 'a') (JJ 'little') (NN 'teapot')))))>>> print(tree.format(depth=1))(S1(S(NP(PRP 'I'))(VP(VBP 'am')(NP(DT 'a')(JJ 'little')(NN 'teapot')))))>>>>>> for production in tree:...     print(str(production))...(S1 S)(S NP VP)(NP PRP)(PRP 'I')(VP VBP NP)(VBP 'am')(NP DT JJ NN)(DT 'a')(JJ 'little')(NN 'teapot')>>> sentences = ["I am a little teapot",...              "Here is my handle",...              "Here is my spout",...              "When I get all steamed up I just shout tip me over and pour me out",...              "I am a very special pot",...              "It is true",...              "Here is an example of what I can do",...              "I can turn my handle into a spout",...              "Tip me over and pour me out"]>>>>>>>>> teapot_grammar = TreeScorer.from_tree_bank(bllip_parse(s) for s in sentences)>>>>>> teapot_grammar.score(bllip_parse("Here is a little teapot"))-9.392661928770137>>> teapot_grammar.score(bllip_parse("It is my handle"))-10.296301543090733>>> teapot_grammar.score(bllip_parse("I am a spout"))-10.40166205874856>>> teapot_grammar.score(bllip_parse("Your teapot is gay"))-12.96352974967269>>> teapot_grammar.score(bllip_parse("Your mom's teapot is asldasnldansldal"))-19.424997926026403```# Author* Aaron Halfaker -- https://github.com/halfak... and substantially informed by https://github.com/aetilley
Package: kasane
Kasanekasane [ 重ね ] (n.) pile; heap; layersThis is not an official Google productKasane is a layering tool for kubernetes. It allows you to use the officially published YAML documents and extend them further with your local configuration.Kasane can utilise Jsonnet for deep object modification and patching.InstallationKasane requires Python 3+. Install via pip:pipinstallkasaneInstallation via Homebrew:brewtapgoogle/kasanehttps://github.com/google/kasane.git
brewinstallgoogle/kasane/kasaneRunning from a Docker containerYou can run kasane from a docker container, the official image isgcr.io/kasaneapp/kasane. The image is based on alpine and comes pre-packaged with bash, curl, git and kubectl in addition to kasane itself. The workdir is set to/appand the default command iskasane showso you can quickly examine your local Kasanefiles like this:$dockerrun--rm-ti-v$PWD/examples/03-environment:/appgcr.io/kasaneapp/kasane
config:defaultFlag:UNRESOLVED_ENV_VAR__DEFAULT_VALUEdefaultFromKasanefile:valuejsonnetEnv:UNRESOLVED_ENV_VAR__OTHER_VALUE
kind:VendoredObject
metadata:name:PreconfiguredObjectTagged builds for versions starting with 0.1.4 are also available as e.g.gcr.io/kasaneapp/kasane:0.1.4.ExamplesSimple Layersis an introduction to kasane features.Jsonnet Transformationsshows how to use Jsonnet to transform objects.Environmentexplains how to use the external environment for customized pipelines.Complex Serviceshows all the features together by using the upstream configuration for kubernetes dashboard, adding an ingress, and optionally enabling istio sidecar.Similar toolsHelmHelm is fully-featured package management solution for kubernetes. Compared to it, kasane is a swiss army knife. It's simple, lightweight, doesn't install helper code into your production. Kasane allows you to use original YAML files written by application authors, modifying them to your local needs. If you see akubectl apply -f http://example you can turn it into a Kasane deployment with a single line of code and then extend it to your needs.Kasane doesn't do any templating, relying on Jsonnet for data manipulation. You won't ever need to count number of spaces to make sure your yaml go template is rendered correctly.KsonnetKasane is similar to Ksonnet but is much simpler to use. Kasane allows to re-use original YAML files and minimizes amount of custom Jsonnet code you need to write. Most of the time your Kasane project would consist of a Kasanefile and single yaml or jsonnet file. Still, Kasane allows runtime flexibility with conditional layers and custom environment.LicenseKasane is distributed under Apache-2license. See thecontributing guidelineson how you can contribute to the project.
Package: kashgari
KashgariOverview|Performance|Installation|Documentation|Contributing🎉🎉🎉 We released the 2.0.0 version with TF2 Support. 🎉🎉🎉If you use this project for your research, please cite:@misc{Kashgari
  author = {Eliyar Eziz},
  title = {Kashgari},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/BrikerMan/Kashgari}}
}OverviewKashgari is a simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS), and text classification tasks.Human-friendly. Kashgari's code is straightforward, well documented and tested, which makes it very easy to understand and modify.Powerful and simple. Kashgari allows you to apply state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS) and classification.Built-in transfer learning. Kashgari built-in pre-trained BERT and Word2vec embedding models, which makes it very simple to transfer learning to train your model.Fully scalable. Kashgari provides a simple, fast, and scalable environment for fast experimentation, train your models and experiment with new approaches using different embeddings and model structure.Production Ready. Kashgari could export model withSavedModelformat for tensorflow serving, you could directly deploy it on the cloud.Our GoalAcademic usersEasier experimentation to prove their hypothesis without coding from scratch.NLP beginnersLearn how to build an NLP project with production level code quality.NLP developersBuild a production level classification/labeling model within minutes.PerformanceWelcome to add performance report.TaskLanguageDatasetScoreNamed Entity RecognitionChinesePeople's Daily Ner Corpus95.57Text ClassificationChineseSMP2018ECDTCorpus94.57InstallationThe project is based on Python 3.6+, because it is 2019 and type hinting is cool.Backendkashgari versiondescTensorFlow 2.2+pip install 'kashgari>=2.0.2'TF2.10+ with tf.kerasTensorFlow 1.14+pip install 'kashgari>=1.0.0,<2.0.0'TF1.14+ with tf.kerasKeraspip install 'kashgari<1.0.0'keras versionYou also need to installtensorflow_addonswith TensorFlow.TensorFlow Versiontensorflow_addons versionTensorFlow 2.1pip install tensorflow_addons==0.9.1TensorFlow 2.2pip install tensorflow_addons==0.11.2TensorFlow 2.3, 2.4, 2.5pip install tensorflow_addons==0.13.0TutorialsHere is a set of quick tutorials to get you started with the library:Tutorial 1: Text ClassificationTutorial 2: Text LabelingTutorial 3: Seq2SeqTutorial 4: Language EmbeddingThere are also articles and posts that illustrate how to use Kashgari:基于 Kashgari 2 的短文本分类: 数据分析和预处理基于 Kashgari 2 的短文本分类: 训练模型和调优基于 Kashgari 2 的短文本分类: 模型部署15 分钟搭建中文文本分类模型基于 BERT 的中文命名实体识别（NER)BERT/ERNIE 文本分类和部署五分钟搭建一个基于BERT的NER模型Multi-Class Text Classification with Kashgari in 15 minutesExamples:Neural machine translation with Seq2SeqContributors ✨Thanks goes to these wonderful people. And there are many ways to get involved.
Start with thecontributor guidelinesand then check these open issues for specific tasks.
Package: kashida
KashidaInsert Kashidas typographically correct into Arabic text via a nifty Python package:
Only once per word, and following the rules as layed out in Khtt.net’sKashida Secret, which defines rules for typesetting Kashidas in Naskh script.The implementation is in its infancy because letters are currently hard-coded into the code and should be replaced by a more dynamic Unicode-based recognition. Nonetheless, it should work for most Arabic-script-based languages.If you find that a word isn’t getting the right amount of Kashida love (according to the Kashida Secret rules), consider to raise an issue athttps://github.com/yanone/kashida/issuesInstallationObtain it from PyPI:pip install kashidaUsageimportkashida# Obtain kashida for single word>>>kashida.word("كشيدة")>>>كشـيدة# Obtain kashidas for a whole text, once per word>>>kashida.text("انا بحب الكشيدة")>>>انـابحـبالكشـيدة
Package: kashmiri
Kaeshir DictionaryA free, open source database of Kashmiri words which is my own work and I'm opening it for everyone. You can use, modify and distribute it for any purpose.pip install kashmiriYou can check my appKaeshir DictionaryPlay StoreApp StoreUse thefind()fucntion to get the Kashmiri meaning of corresponding English word as apython dictionary.>>>fromkashmiriimportfind>>>frompprintimportpprint>>>>>>find("admire"){'title':'Admire','pos':'/ əd-ˈmī(-ə)r/, verb','englishMeaning':'Khosh karun ','kashmiriMeaning':'خۄش کران','englishExample':'I admire Manika.','kashmiriExample':'Winni chai kaem chalan'}>>>>>>x=find('about')>>>pprint(x){'englishExample':'What do you think about me?','englishMeaning':'Mutalik','kashmiriExample':'Tsai kya ba:sa:n mai mutalik?','kashmiriMeaning':'مُتعلِق','pos':'/ ə-ˈbau̇t/, adverb','title':'About'}>>>If the given word is not found, a string sayingNot Foundwill be displayed.>>>find('grep')NotFound>>>Search isCase Insensitive>>>find('account'){'title':'Account','pos':'/ ə-ˈkau̇nt/, noun','englishMeaning':'Hisa:b','kashmiriMeaning':'حِساب','englishExample':'She was asked to give an account of her actions.','kashmiriExample':'Winni chai kaem chalan'}>>>>>>find('aCcOuNt'){'title':'Account','pos':'/ ə-ˈkau̇nt/, noun','englishMeaning':'Hisa:b','kashmiriMeaning':'حِساب','englishExample':'She was asked to give an account of her actions.','kashmiriExample':'Winni chai kaem chalan'}>>>AssertionErrorwill be thrown if you search for some gibberish>>>find("osama bin laden")Traceback(mostrecentcalllast):File"<stdin>",line1,in<module>File"/home/izan/Documents/Kaeshir-Database/kashmiri/kashmiri.py",line5,infindassertword.isalpha(),"You might be a Haput, else you could have entered a correct word."AssertionError:YoumightbeaHaput,elseyoucouldhaveenteredacorrectword.>>>Get access to whole database in the form of tuple usingdatavariable indatabasefile>>>fromkashmiri.databaseimportdata>>>data[0]{'title':'Abandoned','pos':'/ ə-ˈban-dənd/, adjective','englishMeaning':'Trovmut (m.)','kashmiriMeaning':'ترٛومُت','englishExample':'An abandoned factory.','kashmiriExample':'Akh traivmich factry.'}>>>
Package: kasi
KASI (Korea Astronomy and Space Science Institute) Open API Python WrapperFree software:MIT LicenseDocumentation:https://kasi.readthedocs.io.FeaturesProvides simple python wrappers forKASIOpenAPIs described inthis page.UsageCheckthis notebookfor example usage.InstallUsepipfor install:$pipinstallkasiIf you want to setup a development environment, usepoetryinstead:#Installpoetryusingpipx$python-mpipinstallpipx$python-mpipxensurepath$pipxinstallpoetry#Clonerepository$gitclonehttps://github.com/elbakramer/kasi.git$cdkasi/#Installdependenciesandhooks$poetryinstall$poetryrunpre-commitinstallCreditsThis package was created withCookiecutterand theelbakramer/cookiecutter-poetryproject template.
Package: kaskada
Kaskada TimestreamsKaskada'stimestreamslibrary makes it easy to work with structured event-based data.
Define temporal queries on event-based data loaded from Python, using Pandas or PyArrow and push new data in as it occurs.
Or, execute the queries directly on events in your data lake and/or as they arrive on a stream.With Kaskada you can unleash the value of real-time, temporal queries without the complexity of "big" infrastructure components like a distributed stream or stream processing system.Under the hood,timestreamsis an efficient temporal query engine built in Rust.
It is built on Apache Arrow, using the same columnar execution strategy that makes ...Install PythonUsepyenvand install at least3.8(most development occurs under3.11).
If multiple versions are installed,noxwill test against each of them.Building and TestingTo build this package, first installmaturin:poetryshell
poetryinstall--no-root
maturindevelop
pytestAlternatively, install nox and run the tests inside an isolated environment:noxPreviewing DocsInstallquarto-clion your machine. Also consider installing an IDE extension.See:https://quarto.org/docs/get-started/Generate reference docsnox-sdocs-genYou should re-run this after making any updates to thepysrcdocstrings.
IfPreview Docsis running in another shell, the system should auto-refresh with your changes.Preview docs (with auto-refresh on edit)nox-sdocsCleanup generated and cached docsnox-sdocs-cleanTry this if you see something unexpected (especially after deleting or renaming).Builds docs todocs/_sitenox-sdocs-buildThis is primarily used in CI.
Package: kaskuspy
I make this wrapper to improve my python and reverse engineering skills, and to learn more about how to work with git and python packaging.>>> from kaskuspy import Kaskus
>>> kaskus = Kaskus()
>>> kaskus.getHotThreads()
MultipleHotThreadResponse(...)InstallationDownload or clone this repository and run:python setup.py installor via pip:pip install kaskuspyFeaturesKaskus without scrapingFetch the latest hot threadsSearch for threadsand more..ContributingIf you would like to contribute, please check for open issues or open a new issue if you have an idea or a bug.
Follow the code style of the project and PEP8.LicenseThe code in this project is licensed under MIT license.
Package: kaslcred
KASLCred : KERI and ACDC Schema Linker for CredentialsComputes self addressing identifiers (SAIDs) for KERI and ACDC schema parts and links them together by SAID.Usagepython-mkaslcred[schema_dir][output_dir][schema_map_file.json]# Example:# Requires having pre-written and pre-copied all of the schema files into ${KASL_HOME}/schemasexportKASL_HOME=${HOME}/.kasl
python-mkaslcred${KASL_HOME}/schemas${KASL_HOME}/results${KASL_HOME}/schemas/schema_map.jsonInstallationDockerManual Local InstallationInstall PythonInstall the Rust Toolchain:https://www.rust-lang.org/tools/install\This is for theBlake3crypto library built when installing KERI.Install KERI:pip install keri==0.6.8Install KASLcredDependenciesKASLCred depends on the following libraries being installed:Rust toolchain (for Blake3 dependency)LibsodiumThe Homebrew installation of Libsodium is not sufficient, or did not work for me. I had to do the following instructions like stated in Libsodium's GitbookInstallation documentationDownload a tarball of libsodium, preferably the latest stable version, then follow the ritual:./configure
make&&makecheck
sudomakeinstallKERIpyversion 1.0.0python-mpipinstallkeri=1.0.0KERIpy further depends on the following set of dependencies being installed:Rustv1.60+This is required for Blake3 dependency in KERI to be able to build.# the "-s -- -y" options are for a silent, unattended install. Omit them if you want to configure the install.curl--proto'=https'--tlsv1.2-sSfhttps://sh.rustup.rs|sh-s---y# Remember to set the PATH variable to include the Cargo binary directory like so:  PATH="$HOME/.cargo/bin:$PATHDevelopmentMake sure to create a local environment with Pipenv.Then install the Python build tool withpip install buildand the release tool withpip install twine.Installing from the root repo directory:python3-mpipinstall-e./
Package: kasperdb
kasperdbУпрощённая в использовании база данных для python. ЛОКАЛЬНАЯ БАЗА ДАННЫХпример кода:from kasperdb import db, config
import random
confi.debug = True#логи в консоли о действиях дб можно убрать.db.create("t")#создать базу данных под названием tdata = db.get("t")#получаем то что у нас в базе
print(data)#выводим базуf = ["you gay!", "you not gay!"]#список хрени
data["you gay?"] = random.choice(f)#записуем в переменную базы данных хандом херню из списка
db.set("t", data)#записуем новые данныеprint(db.get("t"))#выводим результат добавления данныхdb.delete("t")#удаляем базу данных t
Package: kaspy
kaspyPython implementation of a kaspa-grpc client.work in progress...Breaking changes will occur without prior notice!Installaton:pipinstallkaspyBasic Documentaion:Connecting to a RPC server withconnect()orauto_connect():# Import the kaspa clientfromkaspy.kaspa_clientsimportRPCClient#Initialize a client instanceclient=RPCClient()#Connect to a predefined hostclient.connect(host='<ip>',port='<port>')#OR#Connect to a a publicaly broadcasted node from the dns_seed_servers.client.auto_connect()#note:# 1) it may take a while to find a responsive nodes, timeout should be issued to not get stuck searching# 2) min node requirements for auto-connect is v0.12.2Sending arequest():continued...#define the command you want to sendcommand='getInfoRequest'#build the payload for the commandpayload={}#in our case we don't need to send additional information#send the request to the server and retrive the responseresp=client.request(command=command,payload=payload)print(resp)# print responseSubscribing to a stream withsubscribe():continued...command='notifyVirtualSelectedParentChainChangedRequest'payload={}defcallback_func(notification:dict)# create a callback function to process the notificationsprint(notification)#send the request to the server and retrive the responseresp=client.subscribe(command=command,payload=payload,callback=callback_func)importtimetime.sleep(5)# do stuffclient.unsubscribe(command)#unsubscribe to the streamDisenganging the service withclose()ordisconnect()continued...client.disconnect()# finishes sending all requests and responses in Que, halts all operations, but keeps the channel open.client.close()# closes the channel completelyfor more references on commands and payloads see:https://github.com/kaspanet/kaspad/blob/master/infrastructure/network/netadapter/server/grpcserver/protowire/rpc.mdfor conversions to KaspaMessage command names reference:https://github.com/kaspagang/kaspy/blob/master/kaspy/protos/messages.protoknown issues:GLIBC_2.33' not found:https://github.com/kaspagang/kaspy/issues/4[Workaround]To Do:Fix breaking issueClean up, lots of unused code left in place.Implement error handling and timeoutsImplement Streaming Callback handling of notification messagesadd to pipImplement auto-reconnect options to clientImplement P2P communication2nd cleanupDocumentationAllow for commandline use...Async ImplementationRequirementsgrpcContributefeel free to open a pull request.Authors@D-StacksDonations welcome @ kaspa:qzyjckdvgyxgwqj8zztw7qkqylsp864fyquzg8ykmmwkz58snu85zlk0mfy89
Package: kassalappy
kassalappy: Python Client for Kassal.app APIkassalappy is a Python client library designed to interact with the Kassalapp API, providing a convenient way to access its features programmatically. It also includes a command-line interface (CLI) for easy interaction with the API from the terminal.FeaturesAsynchronous design usingaiohttpfor non-blocking API calls.Pydantic models for data validation and serialization.Custom exceptions for handling API errors.CLI with commands for health checks, managing shopping lists, searching products, and more.Tabulated output for CLI commands for better readability.InstallationTo install kassalappy, you can use pip:pipinstallkassalappyUsageAs a LibraryTo use kassalappy as a library, you need to create an instance of theKassalappclass with your API access token:fromkassalappyimportKassalappclient=Kassalapp(access_token='your_access_token_here')You can then use the client to perform various operations, such as:# Check API healthhealth_status=awaitclient.healthy()# Get shopping listsshopping_lists=awaitclient.get_shopping_lists()# Search for productsproducts=awaitclient.product_search(search='milk')As a CLIkassalappy also provides a CLI for interacting with the Kassalapp API. Here are some examples of CLI commands:# Check API healthkassalappyhealth--tokenyour_access_token_here# Get shopping listskassalappyshopping-lists--tokenyour_access_token_here# Search for productskassalappyproduct"milk"--tokenyour_access_token_hereDocumentationFor more detailed information about the API endpoints and data models, refer to the official Kassalapp API documentation:Kassal.app API DocsContributingContributions to kassalappy are welcome! Please follow the standard GitHub flow for submitting pull requests.Licensekassalappy is released under the MIT License. See the LICENSE file for more details.This README provides a general overview of the kassalappy client. For specific details on the API methods and CLI commands, please refer to the source code and the official API documentation.
Package: kassandra
KassandraDescription.I'm open to all feedback, commentary, and suggestions as long as they are constructive and polite.AuthorsJames Montgomery-Initial work-jamesmontgomery.usLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for detailsGetting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes.InstallingBasic ExamplesRegression ExamplesMulti Layer PerceptronGaussian ProcessBayesian Neural Network with MCDropoutBayesian Neural Network with Variational InferenceClassification Examples (TBD)Multi Layer PerceptronGaussian ProcessBayesian Neural Network with MCDropoutBayesian Neural Network with Variational InferenceTestingTesting is an important part of creating maintainable, production grade code.Running the unit testsRunning the style testsAcknowledgments
Package: kasserver
kasserver - Manage domains hosted on All-Inkl.com through the KAS server APIThis project consists of the Python librarykasserverand a few command line
utilities to manage domains of the German webhosterAll-Inkl.comthrough
theirKAS server API.At the moment the main focus is managing DNS record as this allows to automate
the creation ofLet's Encrypt(wildcard) certificates with theACME DNS-01 challenge.Installationkasserver(and its dependencies) can be installed from PyPI with:pip3 install kasserverAuthenticationBoth library and command line utilities require access to the KAS credentials.
Username and password are read from theKASSERVER_USERandKASSERVER_PASSWORDenvironment variables or from the~/.netrcfile:machine kasapi.kasserver.comlogin USERNAMEpassword PASSWORDThe file must be accessible only by your user account:chmod 600 ~/.netrc.Scriptskasserver-dnsA generic program to manage DNS records.DNS records can be listed with:$kasserver-dnslistexample.comID C Zone        Name Type  Data               Aux1 Y example.com      A     X.X.X.X            00 N example.com      NS    ns5.kasserver.com. 00 N example.com      NS    ns6.kasserver.com. 00 N example.com www  CNAME example.com        0A new DNS record is added with:kasserver-dns add test.example.com CNAME example.comAn existing DNS record is removed with:kasserver-dns remove test.example.com CNAMEkasserver-dns-*The following programs are designed to be used together with ACME clients to
automate DNS record creation/removal as it is required by a Let's EncrytACME DNS-01 challengefor automatic certificate renewal.kasserver-dns-certbotThis program is designed to be used withCertbot:certbot certonly -d foo.exmaple.com --preferred-challenges dns \--manual --manual-auth-hook kasserver-dns-certbot \--manual-cleanup-hook kasserver-dns-certbot \-m invalid@example.comkasserver-dns-legoThis program is designed to be used withlego:EXEC_PATH=kasserver-dns-lego lego --dns exec \--domains foo.example.com --email invalid@example.com runLicenseThis projected is licensed under the terms of the MIT license.