Saravanan666/pypi-org huggingface dataset:
Package: kll
kll - keyboard layout languageVisit our Discord ChannelIf you're trying to compile keyboard firmware, you wantTHISKLL CompilerMost current version of theKLL Spec.UsesfuncparserlibDependenciesDependencies can be installed manually, or by using a pipenv.layouts-python->pippipenvinstall
pipenvrunkll/kll--versionorpipinstalllayoutsUsageGeneral Usagekll<kllfiles>Kiibohd Controller Usagekll<misckllfiles>--config<config/capabilitykllfiles>--base<basemapkllfiles)--default<defaultlayerkllfiles>--partial<partiallayer1kllfiles>--partial<partiallayer2kllfiles>Seekll --helpfor the most up to date documentationUnit TestsUnit tests can be found in thetestsdirectory.
They are run by Travis-CI, but can be useful when testing your own changes.Remember to add new tests when adding new features/changes.Code Organizationkll/common- Main portion of KLL compiler.kll/emitters- Various output formats of the KLL compiler.kll/examples- Example kll files, often used for test cases.kll/extern- External libraries, copied into git repo for convenience.kll/layouts- Layout kll files used for various keyboards and tests.kll/templates- Templates used by emitters when generating output.tests- Unit tests for the KLL compiler.Patches/Features/BackendsCompletely welcome :D
Package: kllr
IntroductionLinear regression of the simple least-squares variety has been a canonical method used to characterize
the relation between two variables, but its utility is limited by the fact that it reduces full
population statistics down to three numbers: a slope, normalization and variance/standard deviation.
With large empirical or simulated samples we can perform a more sensitive analysis
using a localized linear regression method (see, Farahi et al. 2018 and Anbajagane et al. 2020).
The KLLR method generates estimates of conditional statistics in terms of the local the slope, normalization,
and covariance. Such a method provides a more nuanced description of population statistics appropriate
for the very large samples with non-linear trends.This code is an implementation of the Kernel Localized Linear Regression (KLLR) method
that performs a localized Linear regression described in Farahi et al. (2018). It employs
bootstrap re-sampling technique to estimate the uncertainties. We also provide a set of visualization
tools so practitioners can seamlessly generate visualization of the model parameters.If you use KLLR or derivates based on it, please cite the following papers which introduced the tool:Localized massive halo properties in BAHAMAS and MACSIS simulations: scalings, lognormality, and covariance.Stellar Property Statistics of Massive Halos from Cosmological Hydrodynamics Simulations: Common Kernel Shapes.A list of other publications that employedKLLRin their data analysis.D. Anbajagane, A. Evrard, A. Farahi,Baryonic Imprints on DM Halos: Population Statistics from Dwarf Galaxies to Galaxy Clusters, MNRAS, (2022).D. Anbajagane et al.,Galaxy Velocity Bias in Cosmological Simulations: Towards Percent-level Calibration, MNRAS, (2022).A. Nachmann, W. K. Black,Intra-cluster Summed Galaxy Colors, arXiv preprint, (2021).Dependenciesnumpy,scipy,matplotlib,pandas,sklearn,tqdmReferences[1]. A. Farahi, et al. "Localized massive halo properties in BAHAMAS and MACSIS simulations: scalings, lognormality, and covariance." Monthly Notices of the Royal Astronomical Society 478.2 (2018): 2618-2632.[2]. D. Anbajagane, et al. Stellar Property Statistics of Massive Halos from Cosmological Hydrodynamics Simulations: Common Kernel Shapes. No. arXiv: 2001.02283. 2020.AcknowledgmentA.F. is supported by the University of Texas at Austin.
D.A. is supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE 1746045.InstallationRun the following to install:pip install kllrQuickstartTo start using KLLR, simply usefrom KLLR import kllr_modelto
access the primary functions and class. The exact requirements for the inputs are
listed in the docstring of the kllr_model() class further below.
An example for using KLLR looks like this:from kllr import kllr_model                                       

    lm = kllr_model(kernel_type = 'gaussian', kernel_width = 0.2)     
    xrange, yrange_mean, intercept, slope, scatter, skew, kurt = lm.fit(x, y, bins=11)IllustrationIllustration of theKLLRfit with varying kernel size.
Package: k-load
K-Loada light weight website content downloader.Installationpipinstallk-loadUsagek-load"example.com/link-to-the-content"Supported SitesWebsiteURLKSong 全民K歌kg.qq.comBilibili 哔哩哔哩bilibili.comLEGAL ISSUESThis software is distributed under theMIT license.In particular, please be aware thatTHE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.In human words:In case your use of the software forms the basis of copyright infringement, or you use the software for any other illegal purposes, the authors cannot take any responsibility for you. How you are going to use it is left to your own discretion.
Package: klocmod
klocmod -- Kozalo's Localization ModuleScrew you, gettext! I don't wanna bother of compiling strings into binary files!This module provides a very simple, suboptimal way for localizing your scripts, bots or applications. The advantage is
its simplicity: to supply some sets of different string literals for different languages, you just need a simple JSON,
YAML or INI file (or even a dict) fed to the library. After that, the only thing you should take care of is to get an
instance of the dictionary for a specific language and extract messages from it by key values.All you mostly want is theLocalizationsContainerclass. In particular, its static methodLocalizationsContainer.from_file()that reads a localization file and returns an instance of the factory. The factory
is supposed to produce instances of theLanguageDictionaryclass. Most likely, you will encounter instances of its
subclass -- theSpecificLanguageDictionaryclass (the base class is only used as a fallback that returns passed key
values back).Installation# basic installationpipinstallklocmod# or with YAML files support enabledpipinstallklocmod[YAML]Examples of localization filesJSON (language first){"en":{"yes":"yes","no":"no"},"ru-RU":{"yes":"да","no":"нет"}}JSON (phrase first){"yes":{"en":"yes","ru-RU":"да"},"no":{"en":"no","ru-RU":"нет"}}INI[DEFAULT]yes=yesno=no[ru-RU]yes=даno=нетYAMLRequires an extra dependency:PyYAML.# language firsten:yes:yesno:noru-RU:yes:даno:нет---# phrase firstyes:en:yesru-RU:даno:en:noru-RU:нетCode examplefromklocmodimportLocalizationsContainerlocalizations=LocalizationsContainer.from_file("localization.json")ru=localizations.get_lang("ru")# oren=localizations.get_lang()# get default language# thenprint(ru['yes'])# output: да# alternative ways to get a specific phrase:localizations.get_phrase("ru-RU","no")localizations['ru-RU']['no']
Package: klodlibrary
About
This is a simple library that provides
extra content and functionality to python.
for now there is just extra math functions.change logv0.0.1 (9:23 PM, 01/01/2023)first release for this library.v0.0.2 (11:10 PM, 02/01/2023)added a function for binary logic gates.v0.0.3 (9:06 PM, 03/01/2023)fixed binary logic gates function for any amount of inputs.
Package: kloggpro
kloggproThis is a library to query "TFA KlimaLogg pro" weather stations via its
included USB transceiver. It is used for an Integration into Home
Assistant.AcknowledgementThis is derived from the code ofweex-klimalogg by matthewwall.
Thanks alot to the authors of that project!Status experimental!kloggprocan be installed via PyPI on Raspberry Pi OS.It needs usb access to work properly, maybe you need to grant usb access
to the USB-Transceiver byadding the user to plugdev groupsudo adduser <username> plugdevadd following rule to/etc/udev/rules.d/50-usb-perms.rules:SUBSYSTEM=="usb", ATTRS{idVendor}=="6666", ATTRS{idProduct}=="5555", GROUP="plugdev", MODE="0660"To check for success:lsusbBus001Device004:ID6666:5555PrototypeproductVendorID# should show the klimlogg receiver, here bus 001 device 004ls-l/dev/bus/usb/001/004# expected output would becrw-rw-rw-1rootplugdev189,3Dec2121:52/dev/bus/usb/001/004# now group plugdev can access the device and everything should work(I had an error:The device has no langid (permission issue, no string descriptors supported or device error), so i followedthese
instructions)UsageAfter successful installation and usb configuration start python andimport kloggpro.klimaloggNow you can create a KlimaLoggDriver-Objectkldr = KlimaLoggDriver()To start communication, you need to clear wait statekldr.clear_wait_at_start()Now it is possible to either access the current values object directlykldr._service.current.values['Temp0']or you can receive packets:forpacketinkldr.genLoopPackets():print("Time:{}-{}".format(packet['dateTime'],packet))time.sleep(5)After finishing:kldr.shutDown()
Package: klokke
klokkeklokke is a small library for high level measurement of code execution time. klokke keeps track of running timers, and nests them according to execution order. This allows you to keep track of which sub-sections of code contributes the most to your overall time spent.
In addition, a decorator@timedis provided for timing function execution.ExamplesTiming a snippet of code:>>>fromtimeimportsleep>>>fromklokkeimportTimer>>>withTimer("Something expensive")astimer:...sleep(5)...>>>print(timer)Somethingexpensive:5.004069805145264secondsNesting timers:>>>fromtimeimportsleep>>>fromklokkeimportTimer>>>withTimer("outer")asouter:...withTimer("inner")asinner:...sleep(1)...sleep(2)...>>>print(outer)outer:3.0084269046783447secondsOfwhich:inner:1.0031757354736328secondsUsing a decorator:>>>fromtimeimportsleep>>>fromklokkeimportTimer,timed>>>@timed...deffoo():...sleep(2)...return5...>>>withTimer("bar")ast:...x=foo()+foo()...>>>print(t)bar:4.010395050048828secondsOfwhich:__main__.foo:4.010124683380127secondsSetting up timers to automatically print on completion:>>>fromtimeimportsleep>>>fromklokkeimportTimer>>>withTimer("Something expensive",autoprint=print)astimer:...sleep(5)...Somethingexpensive:5.000231981277466secondsAll keyword arguments toTimercan also be passed through the@timeddecorator:>>>fromtimeimportsleep>>>fromklokkeimporttimed>>>importlogging>>>logger=logging.getLogger("foo")>>>@timed(name="foo_log",autoprint=logger.warning)...deffoo():...sleep(1)...>>>foo()foo_log:1.0042669773101807seconds
Package: klondbar
KlondBar \m/(><)_\m/coded by dAriushwhat is klondbar?klondbar is a simple progress bar package with lots of customizations!
here are some of this customizable features:adding header to progress barchanging bar widthchanging the color of progress barhow to install klondbar?use pip like any other package:pip install klondbar

or: python3 -m pip install klondbarHow to use:at this version of klondbar there is two different types of progress bar available:Mega BarMicro BarFast Usage GuideJust wrap for loop sequence withmicro_bar()ormega_bar()mega_bar Fast Usage Sample Code:fromklondbar.megabarimportmega_barimporttimeforiinmega_bar(range(10)):# place your calculations heretime.sleep(0.1)# just an exapmle of calculationsHere's example of mega_bar output:micro_bar Fast Usage Sample Code:fromklondbar.microbarimportmicro_barimporttimeforiinmicro_bar(range(10)):# place your calculations heretime.sleep(0.1)# just an exapmle of calculationsHere's example of micro_bar output:for more usage guide use following jupyter notebook:More informationcode repository:https://gitlab.com/dariush-bahrami/klondbar_project.gitPyPI page:https://pypi.org/project/klondbar/dAriush email address:dariush.bahrami@ut.ac.ir
Package: kloppy
kloppy: standardizing soccer tracking and event dataklop·pen (klopte, heeft geklopt) - juist zijn; overeenkomen, uitkomen met:dat klopt, dat kan kloppenis juist;dat klopt als een zwerende vingerdat is helemaal juistWhat is it?Each vendor of soccer data uses its own unique format to describe the course of a game. Hence, software written to analyze this data has to be tailored to a specific vendor and cannot be used without modifications to analyze data from other vendors. Kloppy is a Python package that addresses the challenges posed by the variety of data formats and aims to be the fundamental building block for processing soccer tracking and event data. It provides (de)serializers, standardized data models, filters, and transformers which make working with tracking and event data from different vendors a breeze.Main featuresHere are just a few of the things that kloppy does well:Loading dataLoadpublic datasetsto get started right awayUnderstandablestandardized data modelsfor tracking and event dataOut-of-the-box(de)serializingtracking and event data from different vendors into standardized models and vice versaProcessing dataFlexibly transform a dataset'spitch dimensionsfrom one format to another (e.g., from OPTA's 100x100 to TRACAB meters)Transform theorientationof a dataset (e.g., from TRACAB fixed orientation to "Home Team" orientation)Pattern matchingSearch forcomplex patternsin event dataUsekloppy-queryto export fragments to XML fileWhere to get itThe source code is currently hosted on GitHub at:https://github.com/PySport/kloppy.Installers for the latest released version are available at thePython package index.pipinstallkloppyInstall from github (dev version)pipinstallgit+https://github.com/PySport/kloppy.gitDocumentationThe official documentation is hosted on pysport.org:https://kloppy.pysport.org.Contributing to kloppyAll contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.An overview on how to contribute can be found in thecontributing guide.If you are simply looking to start working with the kloppy codebase, navigate to the GitHub "issues" tab and start looking through interesting issues.Current contributorsMade withcontrib.rocks.
Package: kloppy-spark
kloppy-sparkSpark Helpers for KloppyRunning the example notebookRequirementsApache Spark 3.0.1Jupyter LabStarting Jupyter Lab in Apache SparkexportPYSPARK_DRIVER_PYTHON=jupyterexportPYSPARK_DRIVER_PYTHON_OPTS=lab

pyspark--packagesgraphframes:graphframes:0.8.1-spark3.0-s_2.12A browser window should open, and you can open and run the example notebook.
Package: klops
Klops: Koin MLOpsBuild your end-to-end machine learning projects within one tool.Getting Started•Documentation•Support•ContributionKlops is an End-to-End machine learning development pipeline ops. Its build on top of Seldon Core, MLflow and DVC. The goal of this project is to make easier for Data Scientist to develop, maintain, log their experiments and deploy their machine learning projects.PrerequisitesSeldon Core Installed on your Kubernetes cluster. GuidancehereMLflow Tracking Server deployed. Recommended to use the schenario 5hereLocal or remote storage such as Amazon S3 or Google Cloud Storage (GCS) or similar.InstallationFrom PyPIUsing pip is the best option if you want to get the stable version and easier way to install.$pipinstallklopsBuild From SourceThis is the best way to get the lastest published library, including the experimental version.Clone this repo$gitclonehttps://gitlab-engineering.koinworks.com/data-team/klops.gitChange your directory to your klops folder:$cdklopsInstall$pythonsetup.pyinstallBasic UsagesKlops consists of three modules. Versioning, Experiment and Deployment.Klops ExperimentKlops Experiment is a class that wraps theMLflow Tracking. Below are the simple example to begin with.fromklops.experimentimportExperimentfromsklearn.datasetsimportload_irisfromsklearn.naive_bayesimportGaussianNBexperiment=Experiment(name="your-experiment-name",tracking_uri="http://<your-mlflow-host>:<port>")X,y=load_iris(return_X_y=True)HYPERPARAMETERS={"n_estimators":20,"max_depth":10,"min_samples_split":5}experiment.start(GaussianNB,x_train_data=X,y_train_data=y,tuner_args=HYPERPARAMETERS)You would see your experiment result in your Mlflow Tracking UI like below:For the complete tutorials could be seen through thisdocumentation.Klops VersioningKlops Versioning is a kind of version control based on DVC. This module wrapped the commandline and python APIs fromDVC.fromklops.versioningimportVersioningversioning=Versioning()# Track your file into dvcversioning.add("myfile.csv")# Add your DVC repository storageversioning.add_remote("gs://your-bucket-name/your-path/")# Push your changes to DVCversioning.push()Complete examples could be seen on thistutorialpage.Klops DeploymentKlops Deployment is a module to deploy the development machine learning projects into Seldon Core instance. Below is the example on how to done with.fromklops.seldon_coreimportSeldonDeploymentfromklops.seldon_core.authimportGKEAuthenticationgke=GKEAuthentication(project_id="your-project-id",zone="your-project-region",cluster_id="your-cluster-id")deployment=SeldonDeployment(gke,"seldon")## Config file could be json or yaml/yml file.config=deployment.load_deployment_configuration("<deployment-config>.json")deployment.deploy(config)Now you can access your API through this dochttp://<ingress_url>/seldon/<namespace>/<model-name>/api/v1.0/doc/. Example result:DevelopmentWe are open for anyone who wants to contribute! Please read theCONTRIBUTING.mdguide.
Package: klotan
klotanDescriptionKlotan is a Python library used to match a structure (tuples, lists, dicts) with a template.
A template is made of a set of criteria.InstallationYou just have to clone this repository and install it using the commandpipinstall.Or you can also install it from PyPipipinstallklotanYou can test the installation by running the following command :python-c"import klotan; print(klotan.match.match([klotan.criteria.equals(22)], [33]).is_valid())"which should printFalseUsageKlotan can be used to validate some form results for exampleperson_template={# The gender can be either M, F or N/A"gender":criteria.is_in("M","F","N/A"),# Expects a name starting with a capital letter, followed by 1 to 15 letters"name":criteria.regex("[A-Z][a-z]{1,15}"),# Jeanne Calment still holds the world record"age":criteria.between(0,122),# Matches all emails"email":criteria.is_email(),# Optional field, matches if value is an URLmatch.optional("personal_website"):criteria.is_url(),# Matches valid IBANs"iban":criteria.is_valid_iban(),# Matches valid credit card numbers"credit_card":criteria.is_valid_credit_card_number(),# We only accept people without debts !"amount_of_money":criteria.is_positive(),# This criteria will accept anything"bonus":criteria.accept(),}# Those are of course, fake informationsperson={"gender":"M","name":"Bob","age":18,"email":"bob.dupont@gmail.com","personal_website":"https://bob.dupont.fr/","iban":"FR68 1009 6000 7018 7224 1164 C75","credit_card":"4929609419559701","bonus":lambda:0,}print(match.match(person_template,person).to_string())The code above prints the following output :{"gender":Criteria<is_in>("M","F","N/A")<<("M")=>True,"name":Criteria<regex>("[A-Z][a-z]{1,15}")<<("Bob")=>True,"age":Criteria<between>(0,122)<<(18)=>True,"email":Criteria<is_email><<("bob.dupont@gmail.com")=>True,"personal_website":Criteria<is_url><<("https://bob.dupont.fr/")=>True,"iban":Criteria<is_valid_iban><<("FR68 1009 6000 7018 7224 1164 C75")=>True,"credit_card":Criteria<is_valid_credit_card_number><<("4929609419559701")=>True,"amount_of_money":Criteria<expected>(Criteria<is_positive>,False)<<(None)=>False,"bonus":Criteria<accept><<(<function<lambda>at0x000001C4767330D0>)=>True}LicensingKlotan uses the MIT license, you can find more details about ithereContributingYou can find more details about contributionshere
Package: klotty
Klotty Python SDKInstallationTo install Klotty Python SDK, simply execute the following command in a terminal:pip install klottySetupFirst, you need to get an API key, which is available in theKlotty Dashboard.fromklottyimportKlottyclient=Klotty(api_key="kl_123")ExamplefromklottyimportKlottyclient=Klotty(api_key=os.environ["KLOTTY_API_KEY"])client.send_email(to="to@email.com",sender="from@email.com",subject="hi",html="<strong>hello, world!</strong>")Example sending email using ReactComing soon.
Package: kloudi
# kloudikloudi, because you know you can't get enough of the cloud.# SummaryFor those of us who like to ensure the provisioning process is cheap, repeatable, and scalable. Kloudi is a collection of tools and libaries for AWS that is very easy and very repeatable.# Getting startedMake sure you are using the secret sauce of zambi before using kloudi.see, https://github.com/CityGrid/zambi```pip install kloudi```# UsageWe like to ensure the provisioning process is very repeatable. So we suppliment the CLI tools we have home grown with ini config files that we create and store in git for revision contorl management. See the examples directory for a set of config examples on our github page, https://github.com/jesco39/kloudi/tree/master/tests/confsFor a list of commands and options```C3EC2Provision.py -h```# Optional packagesThe userdata.pl script is also used for provisioning, it requires the cg-cloud-init RPM.A userdata.pl exampl script can be found in https://github.com/jesco39/kloudi/tree/master/tests/binIf the userdata.pl script starts with sha-bang '#!' it will then be run on frist boot.The userdata.pl script can be set via the AWS_BASE_DIR system variable```Changelog=========Current Release (0.2)------------------* kloudi: Manages automation of AWS accounts and services.0.2~~~~~~~* feature: initial public release, S3 provisioning, name change to kloudi0.1~~~~~~~* feature: initial public release, EC2 provisioning
Package: kloutpy
_    _             _| |  | |           | || | _| | ___  _   _| |_ _ __  _   _| |/ / |/ _ \| | | | __| '_ \| | | ||   <| | (_) | |_| | |_| |_) | |_| ||_|\_\_|\___/ \__,_|\__| .__/ \__, || |     __/ ||_|    |___/The Klout API is designed to return influence metrics generated by Klout. This is a python wrapper for the klouy api.Providing easy access function to all the api calls.Klout makes the following information available:What metrics Klout API returns:1. Klout Score - is the measurement of your overall online influence.The scores range from 1-100 with higher scores representing a wider andstronger sphere of influence.2. Network Influence - is the influence level of your engagedaudience. Capturing the attention of influencers is no easy task, andthose who are able to do so are typically creating spectacular content.3. Amplification Probability - is the likelihood that your content will beacted upon. The ability to create content that compels others to respondand high-velocity content that spreads into networks beyond your own isa key component of influence.4. True Reach -  is the size of your engaged audience. We eliminate inactive andspam accounts, and only include accounts that you influence.5. Influential About - The topics you are most influential about based not onwhat you talk about the most but on the subjects you get the mostengagement on.6. Influencer Of/Influenced By: These are the people who you influence and whoyou are influenced by based on social actions.To install use:pip install kloutpyOr to get the latest version:pip install git+git://github.com/rogeliorv/kloutpy.gitOr download the source and run:python setup.py installHow to contribute a patch:1. Fork kloutpy on github.com2. Create a new branch3. Commit changes to this new branch4. Send a pull request