Saravanan666/pypi-org huggingface dataset:
Package: kiwi-blockchain
kiwi-blockchain (KIK)Kiwi(KIK) is a modern cryptocurrency built from scratch, designed to be efficient, decentralized, and secure. Here are some of the features and benefits:Proof of space and timebased consensus which allows anyone to farm with commodity hardwareVery easy to use full node and farmer GUI and cli (thousands of nodes active on mainnet)Simplified UTXO based transaction model, with small on-chain stateLisp-style Turing-complete functionalprogramming languagefor money related use casesBLS keys and aggregate signatures (only one signature per block)Pooling protocolthat allows farmers to have control of making blocksSupport for light clients with fast, objective syncingA growing community of farmers and developers around the worldCombining Proof-of-Work and Proof-of-Stake SecurelyInstallingPlease visit our wiki for more information:wiki.Resource LinksChiaHubChia Forks CalculatorChiaforks BlockchainTokenDockHow to stakingQuery the staking addresses according to your farming plot list:$ kiwi farm summary
...
Staking addresses:
  kik197tq6ehsn2xayddxuna26jp47t6gt6a9jngzf6xkjz0jscwl9mqs620ka5 (balance: 0, plots: 27)
...Deposit coins to the staking address:$ kiwi wallet send -t kik197tq6ehsn2xayddxuna26jp47t6gt6a9jngzf6xkjz0jscwl9mqs620ka5 -a 1Wait for the transaction get confirmed, query staking balance again:$ kiwi farm summary
...
Staking addresses:
  kik197tq6ehsn2xayddxuna26jp47t6gt6a9jngzf6xkjz0jscwl9mqs620ka5 (balance: 1, plots: 27)
...Withdraw coins from the staking address:$ kiwi wallet send_from -s kik197tq6ehsn2xayddxuna26jp47t6gt6a9jngzf6xkjz0jscwl9mqs620ka5 -t $RECEIVER -a 10Do a transaction to transfer the coins from the staking address to any receive address.Make sure to choose the wallet that contains the plot farmer key.
Package: kiwi-booster
Kiwi BoosterPython utils and classes for KiwiBot AI&Robotics teamMake a Pull Request·Report Bug·Request FeatureTable of contentsAbout The ProjectGetting startedInstallationUsageContributingLicenseContactAbout The ProjectThis library contains utility functions and classes from Python that are commonly used in the AI&Robotics team. It is divided into 5 main sections:common_utils: Some common utils that are normally used in most of the projects.kiwi_booster.loggers
This module contains GCP and local loggers with a predefined format.kiwi_booster.mixed
This module contains miscellaneous utils from multiple objectives.kiwi_booster.requests
This module contains utils for working with HTTP requests.gcp_utils: Utils that are related to the Google Cloud Platform.kiwi_booster.gcp_utils.bigquery
This module contains utils for working with BigQuery.kiwi_booster.gcp_utils.kfp
This module contains utils for working with Vertex (Kubeflow) Pipelines.kiwi_booster.gcp_utils.secrets
This module contains utils for working with Google Cloud Secrets Manager.kiwi_booster.gcp_utils.storage
This module contains utils for working with Google Cloud Storage.ml_utils: Utils that are related to Machine Learning.kiwi_booster.ml_utils.benchmarks
This module contains utils for benchmarking machine learning models.kiwi_booster.ml_utils.prediction
This module contains utils to handle the prediction of the semantic segmentation model.decorators: Decorators that are used to improve the codebase.slack_utils: Utils that are related to Slack.(back to top)Getting startedInstallationTo install the package, simply run the following command:pipinstallkiwi-boosterUsageTo use the package, we recommend using relative imports for each function or class you want to import to improve readability. For example, if you want to use theSlackBotclass, you can import it as follows:fromkiwi_booster.slack_utilsimportSlackBotslack_bot=SlackBot(SLACK_TOKEN,SLACK_CHANNEL_ID,SLACK_BOT_IMAGE_URL,image_alt_text="Bot description",)Or any decorator as follows:fromkiwi_booster.decoratorsimporttry_catch_log@try_catch_logdefmy_function():# Do somethingAs well, we recommend importing them in a separate section from the rest of the imports.(back to top)ContributingIf you'd like to contribute to Kiwi Booster, please feel free to submit a pull request! We're always looking for ways to improve our codebase and make it more useful to a wider range of use cases. You can also request a new feature by submitting an issue.LicenseKiwi Booster is licensed under the GNU license. See the LICENSE file for more information.ContactSebastian Hernández Reyes - Machine Learning Engineer -Mail contactCarlos Alvarez - Machine Learning Engineer Lead -Mail contact(back to top)
Package: kiwicalc
KiwiCalcAbstractThe project is intended to help integrating mathematical
expressions in python with greater ease and provide powerful
tools along them.
Python's syntax can be cumbersome when it comes to 
more sophisticated mathematical notations, so wouldn't it be easier to
use them in a more intuitive "math-like" syntax?SourcesOfficial website:kiwicalc.comOfficial DocumentationGoogle Colab.GitHub.InstallationYou can install the library withpip, by executing the commandpip install kiwicalcin your command prompt.Basic ExamplesFirst, import kiwicalc:from kiwicalc import *Plotting and scattering functionsmy_function = Function("f(x) = x^2")
my_function.plot()my_function = Function("f(x) = sin(x) * cos(y)")
my_function.plot()my_function = Function("f(x,y) = sin(x) * cos(y)")
my_function.scatter3d()
Package: kiwiclient
ISC KiWi-ClientAbstractThis module connectsISC KiwiMS-Excel Workbooks with the online version
of the stability engine.Requirements50 MB free diskspaceMS Excel 2003 or higher installedPython 3.4 or higher installedRequired modulesargparsexlrdxlsxwriterrequestsThey should be installed automatically if the installation is made viapip:pipinstallkiwiclient
Package: kiwigrad
KiwigradDespite lacking the ability to fly through the skies likePyTorchandTensorFlow, the Kiwigrad is still a formidable bird that is teeming with untapped potential waiting to be uncovered.:wink:KiwigradInstallFunctionalitiesExamplesRunning testKiwigrad? yes, it is another version ofmicrogradthat was created just for fun and experimentation.InstallTo install the current release,pip install kiwigradFunctionalitiesKiwigrad is a modified version of themicrogradand theminigradpackages with additional features. The main features added to Kiwigrad are:Training is faster due to the C implementation of the Value object.Tracing functionalities like the originalmicrogradpackage were added. An example of this can be seen in theopsnotebook.Methods for saving and loading the weights of a trained model.Support for RNN(1) feedforward neural networks.ExamplesIn theexamplesfolder, you can find examples of models trained using the Kiwigrad library.A declaration example of an MLP net using Kiwigrad:fromkiwigradimportMLP,LayerclassPotNet(MLP):def__init__(self):layers=[Layer(nin=2,nout=16,bias=True,activation="relu"),Layer(nin=16,nout=16,bias=True,activation="relu"),Layer(nin=16,nout=1,bias=True,activation="linear")]super().__init__(layers=layers)model=PotNet()Kiwigrad likemicrogradand theminigradcomes with support for a number of possible operations:fromkiwigradimportValue,draw_dota=Value(-4.0)b=Value(2.0)c=a+bd=a*b+b**3c+=c+Value(1.)c+=Value(1.)+c+(-a)d+=d*Value(2)+(b+a).relu()d+=Value(3.)*d+(b-a).relu()e=c-df=e**2g=f/Value(2.0)g+=Value(10.0)/fprint(f'{g.data:.4f}')# prints 24.7041, the outcome of this forward passg.backward()print(f'{a.grad:.4f}')# prints 138.8338, i.e. the numerical value of dg/daprint(f'{b.grad:.4f}')# prints 645.5773, i.e. the numerical value of dg/dbdraw_dot(g)Running testcd testpytest .
Package: kiwi-gtk
Kiwi is a framework and a set of enhanced PyGTK widgets designed to
make building programs with graphical interfaces both easy to write
and easy to maintain.Kiwi consists of a set of classes and wrappers for PyGTK that were
developed to provide a sort of framework for applications. Fully
object-oriented, and roughly Smalltalk’s MVC, Kiwi provides a simple,
practical way to build forms, windows and widgets that transparently
access and display your object data.Kiwi is inspired by Allen Holub’s Visual Proxy.
Package: kiwii
KiwiiKiwi (/ˈkiːwi/ KEE-wee) or kiwis are flightless birds native to New Zealand, in the genus Apteryx and family Apterygidae. Approximately the size of a domestic chicken, kiwi are by far the smallest living ratites (which also consist of ostriches, emus, rheas, and cassowaries).kiwii, on the other hand, is a home IOT ecosystem.
Package: kiwi-json
Kiwi JSONPurposeAt the time of creating this lib, there were (at least) three implementation ofdefault_encoder()copy-pasted from one place, evolving its own way and about to being copy-pasted further.
If you have read the story about AI and paperclips, you have an idea where this would lead us to.To prevent this from happening, this library should unify all the implementation, and provide reusable
implementation of the JSON encoding.InstallationAddkiwi-jsoninto your requirements.in filekiwi-jsonUsageIf you use your own JSON encoder as a class, usedefault_encoder()in there.importsimplejsonfromkw.jsonimportdefault_encoder,mask_dictclassOurJSONEncoder(simplejson.JSONEncoder):defdefault(self,obj):returndefault_encoder(obj,mask_dict)kiwi-jsonprovides a simple implementation for masking dictionary values withkw.json.mask_dict.
Or you can create a masking function for it by yourself. It supports customizing placeholder, blacklist and whitelist:fromkw.jsonimportmask_dict_factorymask_dict=mask_dict_factory(placeholder='0_0',blacklist={'secret'},whitelist={'not-so-secret'})If you want to usejson.dumpsdirectly, you can do it the following way:importsimplejsonfromkw.jsonimportdefault_encoderdumps=partial(simplejson.dumps,default=default_encoder)If you havesimplejsoninstalled, you can use Decimal as JSONnumbertype:fromdecimalimportDecimalfromkw.jsonimportdumps,loadsassertdumps({"num":Decimal("1.234")},use_decimal=True)=='{"num": 1.234}'assertloads('{"num": 1.234}',use_decimal=True)=={"num":Decimal("1.234")}Flask-based application could utilize the extension:fromkw.json.flaskimportJSONExtensiondefcreate_app():...JSONExtension(app)...Extension will install an encoder to given app.If you want to make sure that the encoder dumps classes, you can use theraw_encoder:fromkw.jsonimportraw_encoder,dumpsdumps(data,default=raw_encoder)To dump dates and datetimes as unix time, usedate_as_unix_time=True:importarrowfromdatetimeimportdatetimefromkw.jsonimportdumpsdumps({1:datetime.now(),2:arrow.now()},date_as_unix_time=True)If you want to combine the powers ofdate_as_unix_timeandraw_encoder,
you can create your own encoder using partial:fromkw.jsonimportdumps,raw_encoderfromfunctoolsimportpartialmy_encoder=partial(raw_encoder,date_as_unix_time=True)dumps(obj,default=my_encoder)Running testsTo run the tests we use tox. Before you can run the tests please make sure you have postgres database running and the DATABASE_URI env variable setexportDATABASE_URI='postgres://[username]:[password]@[host]:[port]/[database]'Once you have this set up just execute:tox
Package: kiwi-keg
keg is a command line tool that creates akiwiimage description based on
description snippets in a given GIT repository.User documentation.Contributingkeg is written in Python, it usestoxto setup a development environment for the desired Python version. Make
sure the Python development headers are installed (e.g.python36-devel).
KIWI usesjingfor detailed error reporting in case schema validation fails.
This cannot be installed by pip, so you may want also make sure this is
installed on your system.Currently, there are 5 targets for tox:check: for code quality and integritydevel: for developmentdoc: for building man pagesunit_py3_10: to run unit tests with Python version set to3.10unit_py3_6: to run unit tests with Python version set to3.6The following procedure describes how to create the development environment:Let tox create the virtual environment(s):$tox-edevelActivate the virtual environment$source.tox/3/bin/activateInstall requirements inside the virtual environment:$pipinstall-Upipsetuptools$pipinstall-r.virtualenv.dev-requirements.txtLet setuptools create/update your entrypoints$./setup.pydevelopOnce the development environment is activated and initialized with
the project required Python modules, you are ready to work.In order to leave the development mode just call:$deactivateTo resume your work, change into your local Git repository and
runsource .tox/3/bin/activateagain. Skip step 3 and 4 as
the requirements are already installed.
Package: kiwilite
Install:pip install kiwiliteUsage:import kiwilitewith kiwilite.Open(‘<path-to-db-file>’) as storage:# Set map data of key and value and save storage file.
storage.Set({‘<key>’: ‘<value>’})# Get value bytes of key.
<value> = storage.Get(‘<key>’)# Log: a list of timestones.
[<list>, <of>, <timestones>] = storage.Log(<start-timestamp>, <end-timestamp>)# Rollback to the last timestone before timestamp,
# and clear all data after timestamp.
storage.Rollback(<timestamp>)# Lighten to the first timestone after timestamp,
# and clear all invalid data before timestamp.
# Data will not lose, but cannot rollback before timestamp.
storage.Lighten(<timestamp>)
Package: kiwi-menu
kiwi menu'kiwi menu' is a python library that you can use to create simple menus in the terminal screen.Installpip install kiwi_menuExamplefromkiwi_menuimportMenufruits=["apple","banana","kiwi"]menu=Menu("Choose a fruit",fruits)selected=menu.show_menu()print("You choosed",fruits[selected])Result:> apple
  banana
  kiwi

You choosed appleObservation: You optionaly can define thecolor,symbol, and thepre-selected number. And you can put some 'texts' for center, like a description with the argumentcentered_texts, with the texts inside a list.
Package: kiwiml
kiwi v0.0.5A tiny, simple, instructional machine learning library in python, written by Ori Yonay.To install:pip3 install kiwimlCurrent Features:autodiff: A simple automatic numpy-compatible multidimensional differentiatorMachine Learning Models:KNNLinear RegressionLogistic RegressionNaive BayesPerceptronSingle Dimensional AnalysisUtilities:Accuracy score function for calculating model accuracytrain_test_splitA function to plot cost historiesPCA Decomposition (will be in its own dimensionality reduction class in the future)Error Functions:Mean-Squared ErrorCross-Entropy LossDataset Loaders:Boston datasetBreast cancer datasetMNIST datasetTODO:Error Functions: add mean absolute deviationMachine Learning Models:SVMDecision TreeRandom ForestK-Means ClusteringNeural Network LibraryFully-Connected layerConvolutional LayerResidual Layer...More dataset importersAutolearn
Package: kiwi-platform
kiwi-platform-pyCompany standards as code for Kiwi.comInstallationInstall withpip install kiwi-platform.DocumentationCheck the documentation onreathedocs.io.ContributingCheck for open issues or open a fresh issue to start a discussionForkthe repositoryon GitHub.Send a pull request with your code!Merging will require a test which shows that the bug was fixed,
or that the feature works as expected.
Feel free to open a draft pull request though without such a test
and ask for help with writing it if you're not sure how to.
Package: kiwisolver
Kiwi is an efficient C++ implementation of the Cassowary constraint solving
algorithm. Kiwi is an implementation of the algorithm based on theseminal Cassowary paper.
It isnota refactoring of the original C++ solver. Kiwi has been designed
from the ground up to be lightweight and fast. Kiwi ranges from 10x to 500x
faster than the original Cassowary solver with typical use cases gaining a 40x
improvement. Memory savings are consistently > 5x.In addition to the C++ solver, Kiwi ships with hand-rolled Python bindings for
Python 3.7+.
Package: kiwis-pie
Python library for querying WISKI via KiWIS (KISTERS Web Interoperability Solution). See:http://www.kisters.net/NA/products/wiski/options/web-services/DependenciesRequires the Python libraries requests, pandas and tabulate.InstallationInstall from PyPI:pip install kiwis-pieor install directly from github:pip install git+https://github.com/amacd31/kiwis_pie.gitUsageExample fetching some data from the KiWIS service that backs:http://kna.kisters.net/sjr/kiwidgets/tests/SanJoaquinRiver/from datetime import date
from kiwis_pie import KIWIS
k = KIWIS('http://kna.kisters.net/grassland/KiWIS')

# Get station ID for 'SAN JOAQUIN RIVER NEAR PATTERSON'
station_id = k.get_station_list(station_name = 'SAN JOAQUIN RIVER NEAR PATTERSON').station_id.values[0]

# Get timeseries ID for 3-Hourly streamflow (Q) at SAN JOAQUIN RIVER NEAR PATTERSON
ts_id = k.get_timeseries_list(station_id = station_id, parametertype_name = 'Q', ts_name = '3-Hourly').ts_id.values[0]

# Read one day of 3-Hourly flow data for SAN JOAQUIN RIVER NEAR PATTERSON
k.get_timeseries_values(ts_id = ts_id, to = date(2016,1,1), **{'from': date(2016,1,1)})Example using the KiWIS service that backs the Australian Bureau of Meteorology’s Water Data Online service:http://www.bom.gov.au/waterdata/from datetime import date
from kiwis_pie import KIWIS
k = KIWIS('http://www.bom.gov.au/waterdata/services')

# Get station ID for 'Cotter River at Gingera'
station_id = k.get_station_list(station_name = 'Cotter R. at Gingera').station_id.values[0]

# Get timeseries ID for daily mean streamflow (Q) at Cotter River at Gingera
ts_id = k.get_timeseries_list(station_id = station_id, ts_name = 'DMQaQc.Merged.DailyMean.24HR', parametertype_name = 'Water Course Discharge').ts_id.values[0]

# Read 31 days of daily water course discharge data for Cotter River at Gingera
k.get_timeseries_values(ts_id = ts_id, to = date(2016,1,31), **{'from': date(2016,1,1)})

# Optionally use the `keep_tz` option to return in local timezone instead of UTC
k.get_timeseries_values(ts_id = ts_id, to = date(2016,1,31), **{'from': date(2016,1,1)}, keep_tz=True)DocumentationThe methods on the KIWIS class all have docstrings detailing the keyword arguments they take.
Rendered documentation is available on Read the Docs:http://kiwis-pie.readthedocs.io/en/latest/
Package: kiwitcms
IntroductionKiwi TCMS is the leading open source test management system for both manual and
automated testing. It features bug tracker integration, search pages,
powerful access control, test automation framework plugins, visual reports and
rich API layer.Our mission is to transform the testing process by making it more organized,
transparent & accountable for everyone on your team; to improve engineering
productivity and participation in testing.Brief historyFeb 2009 - Project created by Red Hat, Inc. under the name NitrateNov 2014 - Source code published on GitHub without previous historyMar 2016 - Mr. Senko starts contributing to upstreamJan 2017 - First private release on MrSenko.com including updates to
Django 1.8.x and a working automated test suiteMay 2017 - Upstream appears to be unresponsive, sofork;
first release which removes hard-coded bug-tracker specifications and
makes it possible to integrate with external systemsAug 2017 - Support for Django 1.11.x; commit to keeping up to
date with the latest versions of DjangoSep 2017 - Project name changed toKiwi TCMS; support for Python 3.5,
started migrating to modern UI using PatternflyOct 2017 - Launchedhttp://kiwitcms.organdhttps://public.tenant.kiwitcms.org;
first bug report from external contributorNov 2017 - Pushedkiwitcms/kiwito Docker Hub; merged upstream API client
sources and modified them to work with the current code baseJan 2018 - External contributions are now a fact: German translation by
@xbln; new team member Anton SankovMar 2018 - First pull request from non-team memberApr 2018 - Enabled pylint and fixed 700 issues in the same release; commit to
eradicate all of the remaining 3000+ issues and improve code qualityMay 2018 - First public appearance at OSCAL Tirana, DjangoCon Heidelberg and
PyCon CZ PragueNov 2018 - Project info booth at OpenFest SofiaDec 2018 - GitLab integration support - first big code contribution by
non-team member; more than 5 different external contributors in 2018 aloneFeb 2019 - Celebrating 10th anniversary with Kiwi TCMS info booth at
FOSDEM BrusselsMay 2019 - became the first open source TCMS system onGitHub MarketplaceJun 2019 -OpenAwards winnerin ‘Best Tech Community’ categoryFeb 2020 - Start co-hosting theTesting and Automation devroom at FOSDEMMar 2020 -Mozilla Open Source Award winnerMay 2020 - Reached 100K+ pulls on Docker HubMar 2021 - BecameVerified PublisheronGitHub MarketplaceJul 2021 - Reached 1M+ pulls on Docker HubCheck it outhttps://public.tenant.kiwitcms.orgDocumentationhttp://kiwitcms.readthedocs.org/InstallationSeeRunning Kiwi TCMS as a Docker container.Language supportSupported languagesRequest new languageTranslation guideHelp us improve Kiwi TCMSClick theStarbutton athttps://github.com/kiwitcms/Kiwi/stargazersClick the star icon athttps://hub.docker.com/r/kiwitcms/kiwi/Follow @KiwiTCMS athttps://twitter.com/KiwiTCMSSubscribe to ournewsletterSend us testimonials and feedback about how your team is using Kiwi TCMSDonate €5 or more viahttps://opencollective.com/kiwitcms#section-contributeBecome acontributorSupportCommercial support for Kiwi TCMS is also available.
For more information seehttp://kiwitcms.org.
Package: kiwitcms-django-plugin
This package provides a Django test runner that reports the test results toKiwi TCMS.Installationpip install kiwitcms-django-pluginConfiguration and environmentMinimal config file~/.tcms.conf:[tcms]
url = https://tcms.server/xml-rpc/
username = your-username
password = your-passwordFor more info seetcms-api docs.UsageInsettings.pyadd the following:TEST_RUNNER = 'tcms_django_plugin.TestRunner'When you run./manage.py testDjango looks at theTEST_RUNNERsetting
to determine what to do.Changelogv12.7 (10 Dec 2023)Update tcms-api from 11.4 to 12.7Build & test with Python 3.11Refactor issues found by newer pylintRefactor issues found by CodeQLReformat source code with Blackv11.3 (15 Jul 2022)Update tcms-api from 11.2 to 11.4Small linter updatesv11.2 (16 May 2022)Update tcms-api from 11.0 to 11.2Annotate plugin with name & version informationv11.1 (05 Dec 2021)Forward compatible with upcoming Kiwi TCMS v11.0Update tcms-api from 10.0 to 11.0Use f-stringsUpdates in testing environmentsv10.0 (02 Mar 2021)Compatible with Kiwi TCMS v10.0Update tcms-api to 10.0v9.0 (13 Jan 2021)Compatible with Kiwi TCMS v9.0Update to tcms-api v9.0Specify valid DJANGO_SETTINGS_MODULE for running pylint-django in CIv1.1.3 (28 October 2020)Update to tcms-api v8.6.0v1.1.2 (25 June 2020)Update to tcms-api v8.4.0v1.1.1 (25 June 2020)Initial release, thanks to Bryan Mutai
Package: kiwitcms-pytest-plugin
This is apytestplugin forKiwi TCMS.InstallationYou can install “kiwitcms-pytest-plugin” viapipfromPyPI:$ pip install kiwitcms-pytest-pluginConfiguration and environmentMinimal config file~/.tcms.conf:[tcms]
url = https://tcms.server/xml-rpc/
username = your-username
password = your-passwordFor more info seetcms-api docs.
Further documentation and behavior specification can be foundhere.Usage# define environment variables
pytest -p tcms_pytest_plugin --kiwitcmsChangelogv12.8.1 (22 Dec 2023)Update tcms-api from 12.7 to 12.8.1Relax dependency requirement for tcms-api for easier upgradesv12.7 (11 Dec 2023)Follow versioning of other plugins for Kiwi TCMSUpdate tcms-api from 11.3 to 12.7Build and test with Python 3.11Add code scanning with CodeQLv0.1.0 (05 Jul 2022)First release on PyPIBased on tcms-api v11.3Compatible with Kiwi TCMS v11.3 or laterLicenseDistributed under the terms of theGNU GPL v3.0license,
“kiwitcms-pytest-plugin” is free and open source softwareAuthorsDmitry DygaloBryan MutaiAlex TodorovIssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.