Saravanan666/pypi-org huggingface dataset:
Package: kallisto
Table of ContentsFull Author ListIntroductionInstallationTesting suiteReferenceFull Author ListDeveloperEike CaldeweyherDeveloperRocco MeliDeveloperPhilipp PrachtIntroductionWe developed thekallistoprogram for the efficient and robust calculation of atomic features using molecular geometries either in axmolor aTurbomoleformat.
Furthermore, several modelling tools are implemented, e.g., to calculate root-mean squared deviations via quaternions (including rotation matrices), sorting of molecular geometries and many more. All features ofkallistoare described in detail within ourdocumentation(GitBook repository).Main dependenciesclick7.1.2Composablecommandlineinterfacetoolkit
numpy1.20.1NumPyisthefundamentalpackageforarraycomputingwithPython.
scipy1.6.0SciPy:ScientificLibraryforPython
└──numpy>=1.16.5For a list of all dependencies have a look at the pyproject.toml file.Installation from PyPITo installkallistoviapipuse our published PyPI packagepipinstallkallistoInstallation from SourceRequirements to installkallistofrom sources:poetrypyenvorcondapython >=3.7First check thatpoetryis running correctly (v1.0.10 at the time of writing)>poetry--version
Poetryversion1.0.10Create a virtual environment (viapyenvorconda) and activate it. Afterwards, clone thekallistoproject from GitHub and install it usingpoetry>gitclonegit@github.com:AstraZeneca/kallisto.git
>cdkallisto
>poetryinstallTesting suiteThekallistoproject usesnoxas an automated unit test suite, which is therefore an additional dependency.Default nox sessionThe default session includes: linting (lint), type checks (mypy, pytype), and unit tests (tests).>noxWhen everything runs smoothly through, you are ready to go! After one successful nox run, we can reuse the created virtual environment via the-rflag.>nox-rDifferent unit test sessions are implemented (check the noxfile.py). They can be called separately via the run session-rsflag.TestsRun all unit tests that are defined in the /tests directory.>nox-rstestsLintkallistouses theflake8linter (check the .flake8 config file).>nox-rslintBlackkallistouses theblackcode formatter.>nox-rsblackSafetykallistochecks the security of dependencies viasafety.>nox-rssafetyMypykallistochecks for static types viamypy(check the mypy.ini config file).>nox-rsmypyPytypekallistofurthermore usespytypefor type checks.>nox-rspytypeCoverageUnit testcoveragecan be checked as well.>nox-rscoverageReferenceAlways cite:Eike Caldeweyher, J. Open Source Softw.,2021, 6, 3050. DOI:10.21105/joss.03050@article{Caldeweyher2021,
  doi = {10.21105/joss.03050},
  url = {https://doi.org/10.21105/joss.03050},
  year = {2021},
  volume = {6},
  number = {60},
  pages = {3050},
  author = {Eike Caldeweyher},
  title = {kallisto: A command-line interface to simplify computational modelling and the generation of atomic features},
  journal = {J. Open Source Softw.}
}
Package: kallithea-auth-google
kallithea-auth-googleGoogle auth plugin for Kallithea source code management system.Installationpip install kallithea-auth-googleIn the KallitheaAuthenticationsettings, addkallithea_auth_google.auth_google, save, then set the settings.In your config (my.ini):## google auth middleware
[filter:google-auth]
use = egg:kallithea-auth-google#google-auth

[pipeline:main]
pipeline =
    google-auth
    kallithea

[app:kallithea]
use = egg:Kallithea#mainNote that after this change, forpastersetup-dbyou’ll need to pass the section name explicitly:paster setup-db my.ini#kallitheaContactIf you have questions, bug reports, suggestions, etc. please create an issue on
theGitHub project page.LicenseThis software is licensed under theMIT licenseSeeLicense file© 2015 Anatoly Bubenkov, Paylogic International and others.Changelog1.0.5Tolerance for existing usernames/emails (bubenkoff)1.0.2Added google apps domain setting (bubenkoff)1.0.0Initial public release (bubenkoff)
Package: kally
Kally ProjectThis is an alternative algorithm test project for clustering besides kmean and others. This project is funded by the Muhammadiyah University of Ponorogo, Indonesia. This algorithm is based on the philosophy of competence looking for allies.
Complete research results on this algorithm can be seen athttps://github.com/elangbijak4/k-ally.1.0Installing.. code-block:: bashpip install kallyUsage.. code-block:: bash>>> import kally
>>> result=kally(data,columns,k=3,min_index_data=0,iteration=10,treshold=0.05)
Package: kalman_detector
This module implements the kalman detector score along with the equivalent cumulative fourierscore. Score is designed to receive X(f), a sequence of “amplitudes” (where f is an arbitrary indexed parameter)
and decide between:
H0: X(f) = N(f)  pure gaussian noise
H1: X(f) = A(f) + N(f) where A(f) is a smooth gaussian field with a low-pass power spectra where the smoothness parameter
unknown.
Was originally written for detecting Fast Radio Bursts, but can be used to many other types of signals including
AGN light curves.
If used for scientific publication, please write tobzackay@gmail.comfor citing information
(paper would be out soon)
Package: kalmangps
KalmanGpsOverviewKalmanGps is an advanced GPS correction tool that utilizes sensor data, including magnetometer, gyroscope, accelerometer, and GPS information, to provide accurate and corrected latitude and longitude coordinates. This application is particularly useful for scenarios where precise location data is essential, such as navigation systems, robotics, or any application requiring accurate positioning.FeaturesSensor Fusion:KalmanGps integrates data from multiple sensors, including magnetometer, gyroscope, and accelerometer, to enhance the accuracy of GPS coordinates.Real-time Correction:Corrects GPS coordinates in real-time, providing continuously refined location information.Timestamp Precision:Utilizes high-resolution timestamps (timestamp_ns) for precise time synchronization.Adaptive Correction:The Kalman filter adapts to changing conditions, ensuring reliable correction under various scenarios.User-Configurable:Offers flexibility with configurable settings to adjust the correction algorithm based on specific requirements.Export Corrected Data:Easily export the corrected latitude and longitude data for further analysis or integration into other applications.Getting StartedThese instructions will help you get a copy of the project up and running on your local machine for development and testing purposes.PrerequisitesMake sure you have the following installed before proceeding:Python(version 3.x)PipVirtualenv(optional but recommended)Installationpipinstallkalmangpsor clone the repository and install the necessary dependencies:# Clone the repositorygitclonehttps://github.com/your-username/KalmanGps.git# Change into the project directorycdKalmanGps# Create a virtual environment (optional but recommended)virtualenvvenv# Activate the virtual environmentsourcevenv/bin/activate# On Windows: .\venv\Scripts\activate# Install dependenciespipinstall-rrequirements.txtUsageInput:Path to the input data file containing sensor information (magnetometer, gyro, ACC, timestamp_ns, and GPS data).Output:Path to save the corrected latitude and longitude data.mapit:Flag to visualize the corrected data on a map. If specified, the application will generate a map displaying the corrected GPS coordinates.Example command:pythonkalman_processor.py--input_path/path/to/input_csv--output_path/path/to/output.csv--mapitorfromkalmangps.kalman_processorimportKalmaninput_path=r''output_path=r''klmn=Kalman(csv_path=input_path,output_path=output_path,mapit=True)output=klmn()ContributionContributions and feedback are welcome! If you encounter any issues or have suggestions for improvements, please open an issue or submit a pull request.
Package: kalmankit
The Kalman filter is an optimal estimation algorithm: it estimates the true
state of a signal given that this signal is noisy and/or incomplete. This
package provides a multidimensional implementation of:Standard Kalman Filter: if the noises are drawn from a gaussian
distribution and the underlying system is governed by linear equations, the
filter will output the best possible estimate of the signal's true state.Extended Kalman Filter: can deal with nonlinear systems, but it does not
guarantee the optimal estimate. It works by linearizing the function locally
using the Jacobian matrix.InstallationNormal userpipinstallkalmankitDevelopergitclonehttps://github.com/Xylambda/kalmankit.git
pipinstall-ekalmankit/.-rkalmankit/requirements-dev.txtTestsTo run tests you must install the library as adeveloper.cdkalmankit/
pytest-vtests/UsageThe library provides 3 examples of usage:Moving AverageMarket Beta estimationExtended Kalman FilterArequirements-example.txtis provided to install the needed dependencies to
run the examples.ReferencesMatlab -Understanding Kalman FiltersBilgin's Blog -Kalman filter for dummiesGreg Welch, Gary Bishop -An Introduction to the Kalman FilterSimo Särkkä - Bayesian filtering and Smoothing. Cambridge University Press.CiteIf you've used this library for your projects please cite it:@misc{alejandro2021kalmankit,
  title={kalmankit - Multidimensional implementation of Kalman Filter algorithms},
  author={Alejandro Pérez-Sanjuán},
  year={2021},
  howpublished={\url{https://github.com/Xylambda/kalmankit}},}
Package: kalmus
KALMUSKALMUS is a Python package for the computational analysis of colors in films.
It provides quantitative tools to study and compare the use of film color.
This package serves two purposes: (1) various ways to measure, calculate and compare a film's colors
and (2) various ways to visualize a film's color. We have named the software KALMUS in homage to
Natalie Kalmus (1882 - 1965), a Technicolor Director who oversaw the color palettes of nearly 300
Hollywood feature films.KALMUS utilizes the movie barcode as a visualization of the film's color. It has a modularized pipeline for the
generation of barcodes using different measures of color and region of interest in each film frame. KALMUS provides
a low-level API, high-level command line, and Graphic user interface for audience from all backgrounds to take
advantage of its functionality.What is a Movie Barcode:KALMUS: tools for color analysis of films.Journal of Open Source Software, 6(61), 3156.https://doi.org/10.21105/joss.03156How do I install the KALMUS:KALMUS Installation GuideHow do I use the KALMUS:Notebook Tutorials for KALMUS's API, GUI, and CLIHow do I contribute to the KALMUS:KALMUS Contribution GuidelinesAPI DocumentationThe KALMUS API reference is now available onhttps://kalmus-color-toolkit.github.io/KALMUS/kalmus.html.Update LogThe full update log (fromv1.3.0tov1.3.14)
is now available onhttps://kalmus-color-toolkit.github.io/KALMUS/update_log.html
Package: kalna
Kalna 0.0.7DocumentationKalna is a powerful Automated Machine Learning python library.It provides the following features:Fast reading and distributed data preprocessing/cleaning/formattingInstallationTo install viapipuse:pipinstallkalnaData Detailsfromkalnaimportdata_details#showMissingValues will show the details of the missing values with perchentage.data_details.showMissingValues(data)#showCrdinality will show the cardinality of Non-Numeric Values.data_details.showCardinality(data)#Missing Values Imputation of Numerical Features.#method:['mean','median']#Return Type: Dataframedata_details.numImputer(data,missing_feat,method)#Missing Values Imputation of categorical Features.#method:['mode','new_cat']#Return Type: Dataframedata_details.catImputer(data,missing_feat,method)Data Visualizationfromkalnaimportvisualizations#x_feat: feature you want to analyze with target featurevisualizations.boxPlot(data,x_feat,target_feat)
Package: kalpa
Kalpa provides a starting point for resource traversal in Pyramid. It provides
two classes for this, aBranchand aLeaf, which allow you to
create arbitrary resource trees without the boilerplate.There is also aRootclass for added convenience that accepts a
request during initialization. This can be used to create a starting point for
Pyramid’s traversal.fromkalpaimportRoot,Node,branchUSERS={...}classRoot(Root):"""Root resource for Pyramid traversal."""users=branch('UserCollection')classUserCollection(Node):"""User collection, for listings, or loading single users."""__child_cls__='User'def__load__(self,key):"""Returns dict with attributes to create a child node from."""return{'user':USERS[key]}# Load user or raise KeyError.classUser(Node):"""User resource, a single loaded user."""gallery=branch('UserGallery',aliases=['images'])classUserGallery(Node):"""Gallery of images posted by a user.

    Reachable as `/users/:id/gallery` but also `/users/:id/images`.
    """
Package: kalpy-kaldi
kalpyPybind11 bindings for Kaldi for use inMontreal Forced Aligner.InstallationKalpy depends on Kaldi being built as shared libraries, and the easiest way to install is via conda-forge:conda install -c conda-forge kalpyKalpy is also available on pip via thekalpy-kaldipackage, but as this is only a binding library, it relies on Kaldi shared libraries being available. TheKALDI_ROOTenvironment variable must be set to locate the shared libraries and header files.  The easiest way to install the appropriately built kaldi libraries is viaconda install -c conda-forge kaldi.export KALDI_ROOT=/path/to/conda/enviornment
pip install kalpy-kaldiUsageTwo libraries are installed,_kalpywhich contains low level bindings conforming to the original C++ style, andkalpywhich is a more pythonic interface for higher level operations.  Thekalpypackage is under heavy development and expansion to expose more functionality.
Package: kalyke
kalykeSince most of my Jupyter projects require various graphing helpers and styles, this package has been created as a useful repository for this code so that it can be easily included in future projects.Free software: MIT licenseDocumentation:https://kalyke.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-08-16)First release on PyPI.
Package: kalyke-apns
kalykeA library for interacting with APNs and VoIP using HTTP/2.Installationkalyke requires python 3.9 or later.$pipinstallkalyke-apnsUsageAPNsimportasynciofromkalykeimportApnsClient,ApnsConfig,Payload,PayloadAlertclient=ApnsClient(use_sandbox=True,team_id="YOUR_TEAM_ID",auth_key_id="AUTH_KEY_ID",auth_key_filepath="/path/to/AuthKey_AUTH_KEY_ID.p8",)registration_id="a8a799ba6c21e0795b07b577b562b8537418570c0fb8f7a64dca5a86a5a3b500"payload_alert=PayloadAlert(title="YOUR TITLE",body="YOUR BODY")payload=Payload(alert=payload_alert,badge=1,sound="default")config=ApnsConfig(topic="com.example.App")asyncio.run(client.send_message(device_token=registration_id,payload=payload,apns_config=config,))LiveActivity[!NOTE]The topic suffix must be.push-type.liveactivity.LiveActivityPayload.eventdefault value isLiveActivityEvent.UPDATE.importasynciofromdatetimeimportdatetimefromkalykeimportLiveActivityClient,LiveActivityApnsConfig,LiveActivityEvent,LiveActivityPayload,PayloadAlertclient=LiveActivityClient(use_sandbox=True,team_id="YOUR_TEAM_ID",auth_key_id="AUTH_KEY_ID",auth_key_filepath="/path/to/AuthKey_AUTH_KEY_ID.p8",)registration_id="a8a799ba6c21e0795b07b577b562b8537418570c0fb8f7a64dca5a86a5a3b500"payload_alert=PayloadAlert(title="YOUR TITLE",body="YOUR BODY")payload=LiveActivityPayload(alert=payload_alert,badge=1,sound="default",timestamp=datetime.now(),event=LiveActivityEvent.UPDATE,content_state={"currentHealthLevel":100,"eventDescription":"Adventure has begun!",},)config=LiveActivityApnsConfig(topic="com.example.App.push-type.liveactivity",)asyncio.run(client.send_message(device_token=registration_id,payload=payload,apns_config=config,))VoIP[!NOTE]The topic suffix must be.voip.importasynciofrompathlibimportPathfromkalykeimportVoIPApnsConfig,VoIPClientclient=VoIPClient(use_sandbox=True,auth_key_filepath=Path("/")/"path"/"to"/"YOUR_VOIP_CERTIFICATE.pem",)registration_id="a8a799ba6c21e0795b07b577b562b8537418570c0fb8f7a64dca5a86a5a3b500"payload={"key":"value"}config=VoIPApnsConfig(topic="com.example.App.voip",)asyncio.run(client.send_message(device_token=registration_id,payload=payload,apns_config=config,))LicenseThis software is licensed under the MIT License (SeeLICENSE).
Package: kamabay-beautify
** beautify the appearance of the application in the terminal. **what can be done, give the text color, text background, change the font, choose a banner according to the category of your choice.Method Property
- font
- strings
- textArt
- list_art
- list_font
- decoration
- help_me_bro
- random_font
- url_strings
- list_ascii_art
- list_decoration** Fonts **list font 1list font 2** Art **art list 1art list 2** Decoration.**decor list 1decor list 2category list name** the color of the strings.**frombeautifyimportBeautify# Beautify(**kwargs).method_nameprint(Beautify(string="Hello World",font="scammer",color="#eaea",bg_color=None).strings)
Package: kamabayEncoder
encode your data decode, KAMABAYencode decode basedondictionaryyou can change its output encode value by changing the value in its dictionary.EncodefromkamabayEncoderimportKAMABAY_ENCODE_DECODEdata="hello world"# type data stringsave=True# type boolean save file output data.kmykamabay=KAMABAY_ENCODE_DECODE(data=data,save=save);print(kamabay.encode());output in file data.kmy and console>$dsew[KMY]fgdgf[KMY]me&yu[KMY]me&yu[KMY]p039=[KMY]4%32[KMY]^'-hd[KMY]p039=[KMY]3sdfh[KMY]me&yu[KMY]~gd!2[KMY]DecodefromkamabayEncoderimportKAMABAY_ENCODE_DECODEdata="data.kmy"save=Falsekamabay=KAMABAY_ENCODE_DECODE(data=data,save=save);print(kamabay.decode());output>hello worldShow dictionaryfromkamabayEncoderimportKAMABAY_ENCODE_DECODEshowdict=KAMABAY_ENCODE_DECODE(data="None",save=False).showDict("KAMABAY");print(showdict)#return dictionaryoutput displays the json dictionary!@copyright 21052021repository github|licenceapache 2.0 |maillexyong66@gmail.com
Package: kamabayEncoderDecoder
encode your data decode, KAMABAYencode decode basedondictionaryyou can change its output encode value by changing the value in its dictionary.EncodefromkamabayimportKAMABAY_ENCODE_DECODEdata="hello world"# type data stringsave=True# type boolean save file output data.kmykamabay=KAMABAY_ENCODE_DECODE(data=data,save=save);print(kamabay.encode());output in file data.kmy and console>$dsew[KMY]fgdgf[KMY]me&yu[KMY]me&yu[KMY]p039=[KMY]4%32[KMY]^'-hd[KMY]p039=[KMY]3sdfh[KMY]me&yu[KMY]~gd!2[KMY]DecodefromkamabayimportKAMABAY_ENCODE_DECODEdata="data.kmy"save=Falsekamabay=KAMABAY_ENCODE_DECODE(data=data,save=save);print(kamabay.decode());output>hello worldShow dictionaryfromkamabay.KAMABAY_ENCODE_DECODEimportshowDictprint(showDict(auth="KAMABAY"))output displays the json dictionary!@copyright 21052021repository github|licenceapache 2.0 |maillexyong66@gmail.com
Package: kamabay-ipwhois
KAMABAYWHOISURLhttps://github.com/ExsoKamabay/ipwhoisSimple Example>>>fromipwhoisimportipwhois>>>>>>ipwhois('0')# zero string default local IP{"asn":"AS4818","city":"Batu Pahat","completed_requests":2,"continent":"Asia","continent_code":"AS","country":"Malaysia","country_capital":"Kuala Lumpur","country_code":"MY","country_flag":"https://cdn.ipwhois.io/flags/my.svg","country_neighbours":"BN,TH,ID","country_phone":"+60","currency":"Malaysian Ringgit","currency_code":"MYR","currency_plural":"Malaysian ringgits","currency_rates":"4.146","currency_symbol":"RM","ip":"115.164.173.247","isp":"DiGi Telecommunications Sdn Bhd., Digi Internet Exchange","latitude":"1.849442","longitude":"102.9288341","org":"DiGi Telecommunications Sdn Bhd., Digi Internet Exchange","region":"Johor",\n"success":true,"timezone":"Asia/Kuala_Lumpur","timezone_dstOffset":"0","timezone_gmt":"GMT +8:00","timezone_gmtOffset":"28800","timezone_name":"Malaysia Time","type":"IPv4",}Command line typeipwhois@copyright 21052021maillexyong66@gmail.com
Package: kamaki
./kamaki========Overview--------./kamaki is a multipurpose, interactive command-line tool, and also aclient development library for managing OpenStack clouds.As a development library, it implements the OpenStack APIs, along with thecustom extensions introduced by [Synnefo](http://www.synnefo.org)../kamaki is open source and released under a 2-clause BSD License.Project Page------------Please see the [official Synnefo site](http://www.synnefo.org) and the[Synnefo documentation](http://www.synnefo.org) for more information.Copyright and license=====================Copyright (C) 2011-2017 GRNET S.A. All rights reserved.Redistribution and use in source and binary forms, with orwithout modification, are permitted provided that the followingconditions are met:1. Redistributions of source code must retain the abovecopyright notice, this list of conditions and the followingdisclaimer.2. Redistributions in binary form must reproduce the abovecopyright notice, this list of conditions and the followingdisclaimer in the documentation and/or other materialsprovided with the distribution.THIS SOFTWARE IS PROVIDED BY GRNET S.A. ``AS IS'' AND ANY EXPRESSOR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIEDWARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULARPURPOSE ARE DISCLAIMED. IN NO EVENT SHALL GRNET S.A. ORCONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOTLIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OFUSE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSEDAND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICTLIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING INANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THEPOSSIBILITY OF SUCH DAMAGE.The views and conclusions contained in the software anddocumentation are those of the authors and should not beinterpreted as representing official policies, either expressedor implied, of GRNET S.A.
Package: kame
KameAsync REST framework for tortoise-orm and starletteRequirementspython>=3.6starlettetortoise-ormInstallationpip install kameUsageHaving the following tortoise model:fromtortoise.modelsimportModelfromtortoiseimportfieldsclassTour(Model):id=fields.IntField(pk=True)name=fields.TextField()data=fields.JSONField(default=dict)def__str__(self):returnself.nameCreate a serializerfromkameimportserializersclassTourSerializer(serializers.ModelSerializer):classMeta:model=Tourfields=['id','name','description','parent','active','image','descendant_ids']Create a viewsetfromkameimportviewsetsclassTourViewSet(viewsets.ModelViewSet):queryset=Tour.allserializer_class=TourSerializerRegister a routefromkameimportroutersrouter=routers.DefaultRouter()router.register('/tours',TourViewSet,basename="tour")