Saravanan666/pypi-org huggingface dataset:
Package: knack
Knack_                     _
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|_|\_\_| |_|\__,_|\___|_|\_\A Command-Line Interface frameworkInstallation is easy via pip:pipinstallknackKnack can be installed as a non-privileged user to your home directory by adding “–user” as below:pipinstallknack--userNoteThe project is ininitial development phase. We recommend pinning to at least a specific minor version when markingknackas a dependency in your project.UsageimportsysfromcollectionsimportOrderedDictfromknackimportCLI,ArgumentsContext,CLICommandsLoaderfromknack.commandsimportCommandGroupdefabc_str(length=3):importstringreturnstring.ascii_lowercase[:length]classMyCommandsLoader(CLICommandsLoader):defload_command_table(self,args):withCommandGroup(self,'abc','__main__#{}')asg:g.command('str','abc_str')returnOrderedDict(self.command_table)defload_arguments(self,command):withArgumentsContext(self,'abc str')asac:ac.argument('length',type=int)super(MyCommandsLoader,self).load_arguments(command)mycli=CLI(cli_name='mycli',commands_loader_cls=MyCommandsLoader)exit_code=mycli.invoke(sys.argv[1:])sys.exit(exit_code)# $ python mycli.py abc str# "abc"# $ python mycli.py abc str --length 5# "abcde"# $ python mycli.py abc str --length 100# "abcdefghijklmnopqrstuvwxyz"More samples and snippets are available atexamples.DocumentationDocumentation is available atdocs.Developer SetupIn a virtual environment, install therequirements.txtfile.pipinstall-rrequirements.txtpipinstall-e.Run AutomationThis project supports running automation usingtox.pipinstalltoxtoxReal-world usesAzure CLI: The Azure CLI 2.0 is Azure’s new command line experience for managing Azure resources.VSTS CLI: A command-line interface for Visual Studio Team Services (VSTS) and Team Foundation Server (TFS). With the VSTS CLI, you can manage and work with resources including pull requests, work items, builds, and more.Service Fabric CLI: A command-line interface for interacting with Azure Service Fabric clusters and their related entities.Do you use knack in your CLI as well? Open a pull request to include it here. We would love to have it in our list.Release HistorySeeGitHub Releases.Contribute CodeThis project has adopted theMicrosoft Open Source Code of Conduct.For more information see theCode of Conduct FAQor contactopencode@microsoft.comwith any additional questions or comments.If you would like to become an active contributor to this project, please
follow the instructions provided inContribution License Agreement.LicenseKnack is licensed underMIT.
Package: knackhq
KnackHQPython wrapper forKnackHQ APIInstallationpipinstallknackhqConnect to KnackHQCreate aKnackAppinstance to begin interacting with KnackHQ. Supply an app ID, an API key, and an optional API endpoint URL to the client:importknackhqapp=knackhq.KnackApp('<app_id>','<api_key>')Alternatively, set these values in your environment with:KNACKHQ_APP_IDKNACKHQ_API_KEYKNACKHQ_ENDPOINT(optional)app=knackhq.KnackApp()Reading from KnackHQRaw requestsYou may wish to send a raw request response from the KnackHQ API:app.request('GET','objects/object_1/records')# or,app.request('GET','objects','object_1','records')Getting ObjectsUse dictionary syntax to get objects by name or key:obj=app['object_1']# or,obj=app['MyObject']Access object metadata using dictionary syntax:obj['name']obj['key']obj['fields']Getting RecordsUse theget_records()method to iterate over records in an object:forrecordinobj.get_records():print(record)Supply arguments toget_records()to filter records. Options include:record_idpagerows_per_pagesort_fieldsort_orderfiltersUsefiltersto refine your search:filters=[{field:'field_1',operator:'is',value:'test'},{field:'field_2',operator:'is not blank'}]forrecordinobj.get_records(filters=filters):print(record)If you know the ID of the record, useget_record()to return a single record:record=obj.get_record('1234567890ABCDEF')Writing RecordsTODO
Package: knackpy
KnackpyKnackpy is a Python client for interacting withKnackapplications. It is written and maintained bymunicipal tech workersat the City of Austin Transportation Department.v1.0 is Now AvailableKnackpy v1.0is a complete overhaul of the knackpy API.Get started with ouruser guide,API reference, and, for contributors, ourdeveloper guide.The legacy Knackpy v0.1 documentation is availablehere
Package: knackpy-dev
KnackpyKnackpy is a Python client for interacting withKnackapplications. It is written and maintained bymunicipal tech workersat the City of Austin Transportation Department.v1.0 is Now AvailableKnackpy v1.0is a complete overhaul of the knackpy API.Get started with ouruser guide,API reference, and, for contributors, ourdeveloper guide.The legacy Knackpy v0.1 documentation is availablehere
Package: knapsack
knapsackis a package for solving knapsack problem.
Maximize sum of selected weight.
Sum of selected size is les than capacity.
Algorithm: Dynamic Optimizationimport knapsack
size = [21, 11, 15, 9, 34, 25, 41, 52]
weight = [22, 12, 16, 10, 35, 26, 42, 53]
capacity = 100
knapsack.knapsack(size, weight).solve(capacity)See alsohttps://pypi.org/project/ortoolpy/RequirementsPython 3FeaturesnothingSetup$ pip install knapsackHistory0.0.1 (2015-6-26)first release
Package: knapsack01
Knapsack01Generic 0-1 Knapsack Problem Solver.Given a set of items, each with a weight and a value, Knapsack01determine the number of each item to include in a collection so
that thetotal weight is less than or equal to a given limit and the total
value isas large as possible.RequirementsPython 3.4 (tested)GoalThe ultimate goal of this library is to provide a generic interfacefor solving the Knapsack problem for variety of applications.Code sampleLets say, given the capacity (10) of a team and a list of Jira tickets
withstory points and priorities, I would like to know what tickets I wantto do in the next sprint. Note that you can put multiple parameters
forevaluating the importance of each ticket.fromknapsack01.knapsackimportItemfromknapsack01.knapsackimportKnapsacktickets=[Item('A',1,[1,4,8,23,6]),Item('B',3,[4]),Item('C',4,[5]),Item('E',5,[7])]k=Knapsack(10)k.items=ticketsjira_tickets_next_sprint=k.pick_items()ContributorsPaulo Kuong (@pkuong)
Package: knapsack-algorithm
Knapsack Algorithmknapsack_algorithm is a Python package that provides a simple and efficient solution for the 0/1 knapsack problem.FeaturesDynamic Programming Solution: Utilizes dynamic programming to solve the 0/1 knapsack problem efficiently.Error Handling: Provides comprehensive error handling for input validation.Easy to Use: Offers a user-friendly interface for solving knapsack problems with given values, weights, and capacity.Installation:You can install KnapsackAlgorithm using pip:pip install knapsack_algorithmUsage:Here's an example of how you can use knapsack_algorithm:from knapsack_algorithm import knapsackDocumentationFor detailed documentation and additional options, refer to the official documentation.LicenseThis project is licensed under the MIT License - see the LICENSE file for details.ContributingIf you would like to contribute or report issues, please check our contribution guidelines.Example usage:values=[60,100,120]# The values of the itemsweights=[10,20,30]# The weights of the itemscapacity=50# The maximum capacityresult=knapsack(values,weights,capacity)ifresultisnotNone:print("Maximum value in the knapsack:",result)
Package: knapsack-pip
knapsack-pip: A 0-1 knapsack solverThis is a library for solving knapsack problems.Use this solver for maximization or minimization of 0-1 knapsack problems a Branch and Bound algorithm.Non negative weights and profits can also be included.InstallationThis library can be installed via pip. Use command:pip install knapsack-pipUsageSo far, there are different knapsack solvers for different algorithms:BBKnapsack: a knapsack solver using branch and bound with a stack. To import:from knapsack01.BBKnapsack import BBKnapsackHSKnapsack: a knapsack solver using the Horowitz-Sahni branch and bound algorithm. To import:from knapsack01.HSKnapsack import HSKnapsackThe output includes the maximum or minimum profit and its corresponding solution as a list of the same length as list of items (solution[i] = 1 if ith item is included in the solution and solution[i] = 0 otherwise)Example of maximizationfrom knapsack01.BBKnapsack import BBKnapsack

capacity = 50
weights = [31, 10, 20, 20, 5, 3, -6]
profits = [70, 20, 39, 37, 7, 5, 10]

my_knapsack1 = BBKnapsack(capacity, profits, weights)
max_profit, max_solution = my_knapsack1.maximize()
# (126, [1, 0, 1, 0, 1, 0, 1])Example of minimizationfrom knapsack01.BBKnapsack import BBKnapsack

capacity = 50
weights = [31, 10, 20, 20, 5, 3, -6]
profits = [70, 20, 39, 37, 7, 5, 10]

my_knapsack2 = BBKnapsack(capacity, profits, weights)
min_profit, min_solution = my_knapsack2.minimize()
# (96, [0, 1, 1, 1, 0, 1, 0])
Package: knapsack_python
knapsack_python: Solves a variety of knapsack problemsThis package is a collection of solutions to various knapsack problems.
In particular, it has solutions to:the 01 knapsack problem,the 01 multi-knapsack problem (MKP),and potentially more in the future. A good introduction to these sorts
of problems can be found on Wikipedia
(hereandhere).
Additionally, it contains functions I’ve found useful in my work. One
such function isassign_all, which assigns all items to one or more
knapsacks while trying to adhere as best as possible to the capacities
of each knapsack. Most of the solutions are a direct translation of the
solutions given in Silvano Martello and Paolo Toth excellent book*Knapsack Problems: Algorithms and Computer
Implementations*.The implementations of these solutions are all written in C++ and
wrapped in Cython for use in Python.QuickstartInstallationpip install knapsack_pythonorgit clonehttps://github.com/python_knapsackcd python_knapsackpython setup.py installUseAll functions live in theknapsack_pythonmodule.ExampleDependenciesnumpyTODOsMore comprehensive documentationImplement other knapsack-related problems such as:0-1 Knapsack Problem (really a special case of MKP)Multiple-Choice Knapsack ProblemBounded/Unbounded Knapsack ProblemChange-Making ProblemGeneralized Assignment Problem0.1.0Initial release with implementations of:TheMTHMalgorithm for solving the multi-knapsack problemA function to force assignment of all items while trying to
respect the knapsacks’ capacities as much as possible
Package: knarrow
knarrowShoot aknarrowto the knee ;)(The lib is better than this pun, I swear.)Detect knee points in various scenarios using a plethora of methodsUsageJust plug in your values in alist,tupleor annp.ndarrayand watchknarrowhit the knee:>>>fromknarrowimportfind_knee>>>find_knee([1,2,3,4,6])# use a list3>>>find_knee((1,2,3,4,6))# or a tuple3>>>importnumpyasnp>>>y=np.array([1.0,1.05,1.15,1.28,1.30,2.5,3.6,4.9])>>>find_knee(y)# provide just the values4>>>x=np.arange(8)>>>find_knee(x,y)# or both x and y4>>>A=np.vstack((x,y))>>>Aarray([[0.  , 1.  , 2.  , 3.  , 4.  , 5.  , 6.  , 7.  ],[1.  , 1.05, 1.15, 1.28, 1.3 , 2.5 , 3.6 , 4.9 ]])>>>find_knee(A)# works with x in first row, y in the second4>>>A.Tarray([[0.  , 1.  ],[1.  , 1.05],[2.  , 1.15],[3.  , 1.28],[4.  , 1.3 ],[5.  , 2.5 ],[6.  , 3.6 ],[7.  , 4.9 ]])>>>find_knee(A.T)# also works with x in the first column, y in the second column4>>>find_knee(x,y,smoothing=0.01)# for better results use cubic spline smoothing4CLIThis library can also come with a handy CLI if you install it with thecliextra:$pipinstall"knarrow[cli]"$catdata.txt|knarrow-
<stdin>11$catdata.txt|knarrow-ovalue-
<stdin>59874.14171519781845532648
$knarrow--sort-d','-ovalueshuf_delim.txt
shuf_delim.txt20(the-for stdin is, unfortunately, mandatory)Try writingknarrow --helpfor more info.Similar projectsWhile I've come up with most of these methods by myself, I am not the only one. Here is a (non-comprehensive) list of projects I've found that implement a similar functionality and may have been an inspiration for me:mariolpantunes/kneeNote: this project was bootstrapped bypython-blueprint. Since then, it has been heavily modified, though.
Package: knave
Knave provides roles/groups and permissions based authorization
for web (WSGI) applications.
Knave assigns roles to users
(both site wide roles like ‘administrator’ and
context-sensitive roles like ‘creator’),
then uses those roles to decide whether the user has permission to carry out an
action.You can configure permissions and roles statically in your code, or
write adapters to pull authorization information from a database or other
backend.Knave doesn’t do authentication, but can hook into most authentication systems.Knave Authorization Documentation|Repository
Package: kncloudevents
kncloudeventskncloudevents is a HTTP server for listening to Knative Eventing messagesDocumentation:https://kncloudevents.readthedocs.io.Source code:https://github.com/elegantmonkeys/python-kncloudeventsBug reports:https://github.com/elegantmonkeys/python-kncloudevents/issuesDescriptionA lightweight package for doing the heavy lifting of setting up acloudeventsserver for you!
Just provide your function and it will be called on every new event with the event data.NotesUses theofficial cloudevents sdk for Pythonfor parsing the incoming dataInspired by theKnative Golang projectCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-06-03)First release on PyPI.
Package: kn-django-db-email-backend
Django DB Email BackendRecord Email Messages Sent to database , with the ability to also send them via SMTP.UsageInstallpip install kn-django-db-email-backendIn settings.py:INSTALLED_APPS += ['db_email_backend']

EMAIL_BACKEND = 'db_email_backend.backend.DBEmailBackend'
# record the email message to database

# OR
EMAIL_BACKEND = 'db_email_backend.backend.SMTPDBEmailBackend'
# Record to database and send via SMTP.
# IF errors happened you can see it in the Admin and resend it again.Migrate:$ python manage.py migrateConfigurationSMTP_EMAIL_FILTER_FUNCTION_PATH default todb_email_backend.utils.smtp_filter_email_function. a dotted path to the smtp email filter function.
A filter function for the smtp email, takes the email_message (django.core.mail.message.EmailMessage) as a parameter, and return False if this message should be filter out out and not sent via the backend.Example:def only_allow_emails_to_mahad(message):
    return True if 'mahad@kuwaitnet.com' in message.to else FalseDB_EMAIL_FILTER_FUNCTION_PATH default todb_email_backend.utils.db_filter_email_function. a dotted path to the db email filter function. same as the SMTP one but for the Database.Example:def dont_record_error_emails(message):
    return False if settings.EMAIL_SUBJECT_PREFIX in message.subject else TrueAdminPackage have and admin integration ready where you can send the email SMTP even if the EMAIL_HOST = “db_email_backend.backend.DBEmailBackend”
Package: knead
kneadis a command line tool for preprocessing, manipulating and serializing
font files for deep learning applications.Table of ContentsInstallationUsageDocumentationContributingLicenseInstallationThe latest release ofkneadcan be installed from PyPI:pipinstallkneadUsageknead--inputINPUT_FORMAT--outputOUTPUT_FORMAT--directoryPATH/TO/DATA/The--inputand--outputflags must be one of:ttf: a.ttffont file.ttx: a.ttxXML format of the font. For more details, refer to
thefonttoolsdocumentation.json: a.jsonformat of the font.pb: a.pbserialized protobuf format of each glyph in each font.npy: a.npyformat of samples from quadratic Bezier curves in each
glyph in each font.The--directorymust have the following structure:data
└── ttf
    ├── Georgia.ttf
    └── ...Refer to ourquickstartfor
more information on how to useknead.DocumentationPlease refer to ourfull documentation.ContributingContributions are always welcome! Please see ourissue
trackerfor outstanding issues,code of
conductfor community guidelines, and ourcontributing
guidefor details on how to
make a contribution.Licensekneadis licensed under theMIT
license.
Package: kneaddata
KneadData is a tool designed to perform quality control on metagenomic sequencing data, especially data from microbiome experiments. In these experiments, samples are typically taken from a host in hopes of learning something about the microbial community on the host. However, metagenomic sequencing data from such experiments will often contain a high ratio of host to bacterial reads. This tool aims to perform principled in silico separation of bacterial reads from these “contaminant” reads, be they from the host, from bacterial 16S sequences, or other user-defined sources.
Package: kneading-orbital-graph
Kneading Orbital GraphThis pacakge provides an implementation for computing and vizualizing Kneading Orbital GraphInstallationThis package works with Python 3.6-3.8You can install latest stable version fromPyPI:$ pip3 install kneading-orbital-graphTo install the latest development version:$ pip3 install -e git+https://gitlab.com/dmartinez05/kneading_orbital_graph.git@master#egg=kneading_orbital_graphDocumentationThe documentation for the package is available atManual
Package: kneebow
kneebowFind the knee of a curve or the elbow of a curve.How it workskneebow builds upon a very simple idea: if we want to find the elbow of a curve, we can simply rotate the data so that curve looks down and then take the minimum value. If we want to find the knee of the curve, we take the maximum value instead. It's as simple as that.For more info, seethis answeron the Data Science StackExchange.InstallationYou can install the package via pip:pipinstallkneebowAlternatively, you can also install the latest version from GitHub:pipinstallgit+https://github.com/georg-un/kneebow.gitUsageLet's assume, we try to find the elbow of the following data:importnumpyasnpdata=np.array([[1,1],[2,2],[3,3],[4,4],[5,5],[6,6],[7,7],[8,8],# linear until (8,8)[9,16],[10,32],[11,64],[12,128],[13,256],[14,512]])# exponential afterwardsLet's have a peak how this data looks like:To find the elbow, we create an instance of theRotorclass and use itsfit_rotatemethod:fromkneebow.rotorimportRotorrotor=Rotor()rotor.fit_rotate(data)Now we can get the index of the elbow as follows:elbow_idx=rotor.get_elbow_index()print(elbow_idx)# 11TheRotorclass also comes with plot methods to inspect the data visually together with the estimated elbow/knee:rotor.plot_elbow()CitationIf you need to cite this package, you can do so as follows:BibTeX@misc{kneebow,
  title={ {kneebow}: Knee or elbow detection for curves},
  author={Unterholzner Georg},
  year={2019},
  howpublished={\url{https://github.com/georg-un/kneebow}},
}Note:Make sure to import theurlpackage with:\usepackage{url}.APA/HarvardUnterholzner Georg. (2019). kneebow: Knee or elbow detection for curves. https://github.com/georg-un/kneebow.LicenseDistributed under the MIT License. SeeLICENSEfor more information.
Package: kneed
kneedKnee-point detection in PythonThis repository is an attempt to implement the kneedle algorithm, publishedhere. Given a set ofxandyvalues,kneedwill return the knee point of the function. The knee point is the point of maximum curvature.Table of contentsInstallationUsageInput DataFind KneeVisualizeDocumentationInteractiveContributingCitationInstallationkneedhas been tested with Python 3.7, 3.8, 3.9, and 3.10.anaconda$condainstall-cconda-forgekneedpip$pipinstallkneed# To install only knee-detection algorithm$pipinstallkneed[plot]# To also install plotting functions for quick visualizationsClone from GitHub$gitclonehttps://github.com/arvkevi/kneed.git&&cdkneed
$pipinstall-e.UsageThese steps introduce how to usekneedby reproducing Figure 2 from the manuscript.Input DataTheDataGeneratorclass is only included as a utility to generate sample datasets.Note:xandymust be equal length arrays.fromkneedimportDataGenerator,KneeLocatorx,y=DataGenerator.figure2()print([round(i,3)foriinx])print([round(i,3)foriiny])[0.0,0.111,0.222,0.333,0.444,0.556,0.667,0.778,0.889,1.0][-5.0,0.263,1.897,2.692,3.163,3.475,3.696,3.861,3.989,4.091]Find KneeThe knee (or elbow) point is calculated simply by instantiating theKneeLocatorclass withx,yand the appropriatecurveanddirection.Here,kneedle.kneeand/orkneedle.elbowstore the point of maximum curvature.kneedle=KneeLocator(x,y,S=1.0,curve="concave",direction="increasing")print(round(kneedle.knee,3))0.222print(round(kneedle.elbow,3))0.222The knee point returned is a value along thexaxis. Theyvalue at the knee can be identified:print(round(kneedle.knee_y,3))1.897VisualizeTheKneeLocatorclass also has two plotting functions for quick visualizations.Note that all (x, y) are transformed for the normalized plots# Normalized data, normalized knee, and normalized distance curve.kneedle.plot_knee_normalized()# Raw data and knee.kneedle.plot_knee()DocumentationDocumentation of the parameters and a full API reference can be foundhere.InteractiveAn interactive streamlit app was developed to help users explore the effect of tuning the parameters.
There are two sites where you can test out kneed by copy-pasting your own data:https://share.streamlit.io/arvkevi/ikneed/main/ikneed.pyhttps://ikneed.herokuapp.com/You can also run your own version -- head over to thesource code for ikneed.ContributingContributions are welcome, please refer toCONTRIBUTINGto learn more about how to contribute.CitationFinding a “Kneedle” in a Haystack:
Detecting Knee Points in System Behavior
Ville Satopa
†
, Jeannie Albrecht†
, David Irwin‡
, and Barath Raghavan§
†Williams College, Williamstown, MA
‡University of Massachusetts Amherst, Amherst, MA
§
International Computer Science Institute, Berkeley, CA
Package: kneedeepio-plugins
python-plugin-frameworkA plugin framework for applications written in Python.FIXME: Write a readme about the library, not how to build it.Commands to build and push to PyPi:Install build dependencies:pip install --upgrade -r requirements.txtRun tests and such:python ./analysis_pylint.py
python ./analysis_coverage.py
python ./analysis_radon.pyBuild and upload:rm -rf dist/*
python -m build
twine upload --config-file .pypirc dist/*