Saravanan666/pypi-org huggingface dataset:
Package: kneefinder
Knee FinderA simple tool to find the knee point of a 2-d curve.This is useful for tune the parameters in several algorithms (clustering, etc.)Installationyou can install this package with pip:pip install kneefinderDefinition of "Knee" pointThe knee point is defined as the “relative costs to increase [or decrease, NdC] some tunable parameter is no longer worth
the corresponding performance benefit” (Satopää, Albrecht, Irwin, and Raghavan, 2011, p.1)ExampleimportnumpyasnpfromKneeFinderimportKneeFinderdata_x=np.linspace(1,10,15)data_y=10*(np.exp(-a)+0.15*np.random.rand(len(a)))kf=KneeFinder(data_x=a,data_y=b)knee_x,knee_y=kf.find_knee()# plotting to check the resultskf.plot()MethodologyKneeFinder define as knee the point which has the maximum distance
from a line passing between the first and last point.As example, take the following image: in blue you can see the data,
in orange the segment which connect the first to the last data point, and
in red the distances between the data points. The big continuous red line points to our
knee point.This methodology is simpler with respect to other methods: no parameters are required,
so it's easier to use in automated processes.RobustnessSince this tool does not rely on any assumption on the curve shape,
it results as more robust with respect to other, more complicated, tools.As example, if you considerKneedwith the following data,
and simulating a common mis-configuration in the parameters:# Finding the knee with the Kneed tool (not with our one)fromkneedimportKneeLocatorx=[0.1,0.23571429,0.37142857,0.50714286,0.64285714,0.77857143,0.91428571,1.05,1.18571429,1.32142857,1.45714286,1.59285714,1.72857143,1.86428571,2.]y=[1.17585897,1.35051375,1.836304,2.20409812,2.37060316,2.46157837,3.28991099,2.9927505,3.44015722,6.33212422,6.92051422,5.28718862,6.69129098,6.67477275,10.00921042]kneedle=KneeLocator(x,y,curve="concave",direction="increasing")kneedle.plot_knee()Note that the curve is convex-like, while we configured Kneed as if the curve was concave-like.
With this configuration, the package state the knee/elbow point to be the very first point,
which is obviously wrong.While using our tool you get:Moreover, our tool is also a bit faster:%%timeitkf=KneeFinder(data_x=x,data_y=y)kf.find_knee()# 24 µs ± 268 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)%%timeitkneedle=KneeLocator(x,y,curve="concave",direction="increasing")kneedle.find_knee()# 91.8 µs ± 1.32 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
Package: knex
KNEXPython library for creating chainable data transformers.Installationpip install knexUsage>>>fromknex.parsersimport*>>>>>>input_data="""... Interface             IP-Address      OK?    Method Status          Protocol... GigabitEthernet0/1    unassigned      YES    unset  up              up... GigabitEthernet0/2    192.168.190.235 YES    unset  up              up... GigabitEthernet0/3    unassigned      YES    unset  up              up... GigabitEthernet0/4    192.168.191.2   YES    unset  up              up... TenGigabitEthernet2/1 unassigned      YES    unset  up              up... Virtual36             unassigned      YES    unset  up              up... """>>>>>>pattern=r"\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b">>>>>>end=(Start(input_data)>RegexExtractAll(pattern)>GetIndex(0)>Concat("","/24")>IpNetwork())>>>>>>print(end.result)192.168.190.0/24>>>print(json.dumps(end.history,indent=4))[{"parser":"RegexExtractAll","input":"...omitted for brevity...","args":{"pattern":"\\b\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\b"},"error":false,"output":["192.168.190.235","192.168.191.2"]},{"parser":"GetIndex","input":["192.168.190.235","192.168.191.2"],"args":{"idx":0},"error":false,"output":"192.168.190.235"},{"parser":"Concat","input":"192.168.190.235","args":{"prefix":"","suffix":"/24"},"error":false,"output":"192.168.190.235/24"},{"parser":"IpNetwork","input":"192.168.190.235/24","args":{},"error":false,"output":"192.168.190.0/24"}]>>>
Package: knexpy
KnexpyA query builder for SQLite3 based on KnexjsFeaturesTransactionsType CheckingBulk InsertJSON mappingYou can report bugs and discuss feature on theGitHub issues page.
For more detailed information checkReadthedocsExamplesCreating a TablefromKnexpyimportKnex,Fielddb=Knex("<Database File Name/Path>")db.table("c",[Field.integer("field"),],not_exists=False,)db.table("t",[Field.varchar("field"),# Default size: 255Field.varchar("field2"),Field.varchar("field3"),Field.foreign_key("field4","c","id"),],not_exists=False,# IF NOT EXISTS clause. Defaults to True)When creating a table the fieldsid,created_at,modified_atare automatically generated.
Theidfield is a hash based on the information of the RowBasic SelectfromKnexpyimportKnexdb=Knex("<Database File Name/Path>")query=(db.select("id","field","field2",["field3","test"],...)# List type on select acts as an alias.from_("t").where("field","=","12345").order_by("id"))query.query()# Returns data as JSONquery.query(False)# Returns data as tuplesSelect with SubqueryfromKnexpyimportKnexdb=Knex("<Database File Name/Path>")query=(db.select("id","field","field2",["field3","test"],...).from_("t").where("field","=","12345").where("field4","=",db.subquery().select("id").from_("c").where("field","=",12345),join_type="OR",# If attribute not present defaults to "AND".).order_by("id"))query.query()# Returns data as JSONInsert JSON DatafromKnexpyimportKnexdb=Knex("<Database File Name/Path>",type_check=True)# type_check enables type checking (duh) when inserting/updating datadb.insert_json("<table>",{"field":"1","field2":"2","field3":"3"})
Package: knic-jupyter
knic-jupyterJupyter Lab for the KNIC Project at USC ISI (Information Sciences Institute)InstallationFollow the steps below to installknic-jupyter:git clone git@github.com:usc-isi-i2/knic-jupyter.gitcd knic-jupyterconda create --name knic-jupyter python=3.8 -yconda activate knic-jupyterpip install knic-jupyterRunning the labTo run jupyter lab you can use this predefined script:./run-jupyter-lab.shAdding notebooksYou might want to copy the notebooks that you want to work on, for example:git clone git@github.com:usc-isi-i2/knic-notebooks.git notebooksFrom there, if any additional dependencies are required you can install those usingconda install <package-name>orpip install <package-name>DevelopmentKNIC Jupyter lab extension is pre-built withknic-enginelocation set tohttps://knic.isi.edu/engineIf you are runningknic-enginelocally for development purposes, i.e. onhttp://localhost:5642/knic, you could add a--developflag to therun-jupyter-lab.shcommand.
This will change the endpoint ofknic-enginefrom our production setting, to your local setting.For example:./run-jupyter-lab.sh --developNOTE: It might take a little longer to spin up jupyter lab if theknic-engineendpoint changed as we would need to rebuild our extension.
Package: knifes
https://pypi.org/project/knifes/包升级修改版本号
rm -r dist && python -m build && python -m twine upload dist/*项目更新包pip install knifes --index-urlhttps://pypi.python.org/simple-U额外需要安装三方包asecryptographysmstencentcloud-sdk-python==3.0.600lubanPillowpillow-heifalarm、auth、decorators、envconfig、misc、results、smsdjango注意谨慎使用validators.url, 该方法较严格, 如会判断https://m.toutiao.com/is/iNmDd2SG/?=为非法url
Package: knight
Knight is a command line utility for doing basic CodeChef operations like problem submission, history, etc directly from the terminal. Trust me it helps ðŸ˜€Parameters & Usageusage: knight [-h] [-n] [-l] [--select-contest contest_code] [--reset-contest] [-s problem_code solution_location] [--history problem_code] [-u] [--config] [--logout]-h or --help- Displays the standard help on usage and parameters-n or --nologin- Performs certain operations that don't require you to log in (currently only-l or --list-contests)--select-contest <contest_code>- Change the contest to the current ongoingcontest_code--reset-contest- Change the contest to PRACTISE mode-s <problem code> <solution file> or  --submit <problem code> <solution file>- Submit your solution file to the specified problem code.Change the contest to submit to an ongoing contest.--history <problem code>- Display your submission history for that problem in a prettier tabular format.Change the contest to submit to an ongoing contest-u or --user- Display the currently configured user onKnight.--config- Set another username and password for logging into Codechef.--logout- Logs out the configured user on Codechef (if logged in)Note:Knightuses sessions  so your previous login session is saved for later use.InstallationLinux & Unix systems -$ pip install knight --upgradeWindows -pip install knight --upgradeFor Windows users, you need to add installation directory toPATH. At the end of the installation, you'll be notified by a warning with a location/directory. Copy that location and add it to system'sPATHvariable.RequirementsPython 3.xIssuesIn case of some error  or exceptions, please find the log files underHOME_DIR/.knight/logs/and submit an issue on Github with the logs.
It will really be of great help improving the user experience.How to ContributeFork the repo.Clone the repohttps://github.com/<your username>/knight.gitto empty directory.mkdir knight
cd knight
git clone https://github.com/<your username>/knight.gitCreate a new branch for your changes.git branch my_branch
git checkout my_branchCommit your changes and Submit Pull Request tomasterbranch of parent project with description and commentsTestingRun the knight.py file with requried parameters and arguments that work on your changes.[Inside project root]

python3 knight/knight.py <appropriate args>
Package: knightstour
Knights Tour 0.1.xA very simple implementation of the knights tour. Intended to create a lot of data work with a little bit of code.InstallationUse poetry to install packagepoetryinstallknightstour-lucasmcgregorUsagefromknightstour.ChessboardimportChessboardfromknightstour.TourReporterimportTourReportertours=TourReporter()board=Chessboard(x_size=8,y_size=8,reporter=tours)board.solve_board()print("moves checked:{0}".format(tours.moves))print("we found{0}tours".format(tours.tours_count))LicenseMIT
Package: knij
KAMAR Notices InterfacePython EditionKNI (KAMAR Notices Interface) is a project designed to bring a way of accessing notices from
theKAMARportal software. KNIs goal is to produce usable libraries in as many
languages as possibleKNI is available on PyPI:$python-mpipinstallknijRetrieving NoticesfromkniimportKNIkni=KNI('demo.school.kiwi')notices=kni.retrieve()fornoticeinnotices.notices:print(notice)Specifying a custom date# Use leading zeros on single digits (e.g 01/01/2021)notices=kni.retrieve('DAY/MONTH/YEAR')By Jacobtread
Package: knilb
What is the Knilb package?An example implementation of anAgentfor automating end-to-end functional testing of a connectedDevice Under Testusing theCloud Serviceand a connectedTest Equipment.How to use it?You can use it directly from command line usingknilbcommand, or you can use python to access the package.  Use-hfor help.$knilb-h
$python-mknilb-h
Package: knime
Utilities for working with KNIME workflows and data.Via the Workflow class, a KNIME Workflow can be run via KNIME’s batch
executor (requires KNIME be installed on the local system).  Inputs
to the “Container Input (Table)” nodes in a KNIME Workflow can be
supplied as either Python dicts or pandas DataFrames.  Likewise,
outputs captured from “Container Output (Table)” nodes are provided
back as either Python dicts or pandas DataFrames.TODOs:add handling for setting of workflow variables
- only via Container Input (Variable) nodes?
- also via batch executor variable settings?add handling for Container Input/Output (JSON) nodessupport remote workflows
Package: knip
knipThis module aims to make python coding hassle free!Usagefromknipimportsprintsprint("hello","world",end="#",sep="$")sprint("3.14159265"*314159265)AdvantagesIt displays large data (of any type) in a neat and organized manner.Does not hang while printing, no matter how large the data may be.Syntactically similar to in-built print function.no reason not to use it <3InstallationRun the following to install:pipinstallknipAuthorEmail:nishant.aklecha@gmail.comLinkedIn:https://www.linkedin.com/in/naklechaCodeChef:https://www.codechef.com/users/naklechaPYPI:https://pypi.org/user/naklechaGitHub:https://github.com/NaklechaHelp me make this module better by adding your own appropriate functions! Thanks!
Package: knipse
knipseCLI catalog manager for pix and gThumbFeaturesknipseis a single file Python projectInstallYou can installknipseusingpipwithpip3installknipseor you can simply downloadknipse.pyand then run it usingpython3withpython3knipse.pyCreditsMain developer is luphordluphord@protonmail.com.cookiecutter-pyscriptis used as project template.History0.8.0 (2022-11-20)Drop support for Python 3.6 and 3.7Add support for Python 3.9, 3.10 and 3.11Upgrade development dependencies0.7.1 (2021-09-27)fix domain error when symlinking empty catalogprevent resolution errors in paths with symlinks0.7.0 (2021-08-03)symlinkcan prefix links with their index via-i/--index-prefixsupport loading empty catalog files0.6.0 (2020-11-25)catalogssubcommand for listing available catalogssymlinksubcommand for creating symlinks to files in catalog; also supports directory structuremigrate to travis-ci.comuse black for formattinglsiterates all available catalogs if none are given as argument0.5.0 skipped by mistake0.4.1 (2020-05-03)print help when no subcommand is given0.4.0 (2020-05-03)createsubcommand for creating catalogs by reading image file names from stdinfix loading image paths containing spaces0.3.2 (2020-05-01)upgradetwinein order to have working checks with markdow readme + history0.3.1 (2020-05-01)setlong_description_content_typetotext/markdownfor proper rendering onPyPI0.3.0 (2020-05-01)checksubcommand for checking existence of files in catalogchecksubcommand walks folder structure and checks each catalog found if no catalog is specifieddrop support for Python 3.5Cataloginstances can be serialized to xmlCataloginstances can be iterated and compared for equality0.2.0 (2020-03-22)Catalogclass for parsing catalog xml fileslssubcommand for listing files in catalog0.1.1 (2020-03-21)fix linter error0.1.0 (2020-03-21)Created usingcookiecutter-pyscript
Package: knit
Theknitlibrary provides a Python interface to Scala for interacting
with the YARN resource manager.View thedocumentationforknit.Overviewknitallows you to use python in conjunction with YARN, the most common resource
manager for Hadoop systems.
It provides to following high-level entry-points:CondaCreator, a way to create zipped conda environments, so that they can be uploaded to
HDFS and extracted for use in YARN containersYARNAPI, an interface to the YARN resource manager to get application/container statuses,
logs, and to kill running jobsKnit, a YARN application runner, which generates an instance of a scala-based YARN client,
and launches an application on YARN, which in turn runs commands in YARN containersDaskYARNCluster, launches a Dask distributed cluster on YARN, one worker process
per container.The intent is to useknitfrom a cluster edge-node, i.e.,
with YARN configuration and the CLI available locally.QuickstartInstall from conda-forge> conda install -c conda-forge knitor with pip> pip install knitIf installing from source, you must first build the java library (requires java and maven)> python setup.py install mvnTo run an arbitrary command on the yarn clusterimportknitk=knit.Knit()k.start('env')# wait some timek.logs()To start a dask cluster on YARNimportdask_yarncluster=dask_yarn.DaskYARNCluster()cluster.start(nworkers=4,memory=1024,cpus=2)
Package: knitlib
knitlib1. What is KnitlibKnitlib is a library designed to support the operation of varied knitting machines, mechanisms, and hacks. Knitlib is based on projects like AYAB, PDD, and KnitterStream to control knitting machines. Knitlib features a plugin system for knitting machines and implements an API to control machines’ operation, knitting jobs and knitting patterns. The software is based on Python. There also is a Web API. Among the primary tasks is to develop plugins based on this solution to add support for more machines.1.1 Idea behind Knitlib1.2 Technical Background of KnitlibKnitlib is implemented as a Python Library and API. Each machine is supported via a Plugin, allowing for extensibility. Each of the plugins is based on a simple Finite State Machine, with states from machine initialization to operation and knitting process. Among the aplications using the Knitlib API is Knitlib-server, implementing a Webserver and REST / WebSocket endpoints. Clients can also implement message callbacks, errors, notifications and blocking messages in order to provide a good user experience.2.Development Installationpip install -r requirements.txt
pip install knitlib3. Usage4. DevelopmentTo run the all tests run:tox5. References6. Documentation7. Contributing7.1. Bug reportsBugs can be reported via the Github issues tracker athttps://github.com/fashiontec/knitlib/issues7.2 Documentation improvements7.3 Feature requests, Issues, and FeedbackIssues, feature requests and feedback can be reported via the Github issues tracker athttps://github.com/fashiontec/knitlib/issues7.4 Pull Request Guidelines8. Applications using Knitlib9. LicenseFree software: LGPLv3+ licenseChangelog0.0.1 (2015-05-15First release on PyPI.
Package: knitout-kniterate-3D
knitout-kniterate-3DCode for producingknitoutfiles to do 3D knitting on thekniterateknitting machine (and also optionally adding stitch patterns along the 3D surface).See therelease notes on GitHubfor details about the updates/changes in this release.Graphic SpecificationsOverviewThe program uses 2D raster (or bitmap) graphics (with full opacity!) to generate knitout files for 3D shapes that can then be converted to knitting machine instructions in k-code using theknitout-to-kniterate-backend. It also allows for incorpating stitch patterns to overlay onto the shape graphic (kind of like a cookie cutter).You can use adobe photoshop orphotopea(a fantastic & free in-browser photoshop alternative) to create these graphics.For all of these graphics, an individual pixel is mapped to an individual stitch (so the height and width of the graphic respectively determine the row and needle count of the resulting knitted object). If the stitch pattern graphic is a different size from the shape graphic, it will be resized to match the latter.3D ShapesThe graphic that represents the shape should have a white background and a flat black fill as the shape. This graphic will be used for both the front and the back of the shape (currently, the program does not support variation in these).Stitch PatternsOptionally, the shape can be composed of a variety stitch patterns at different points along its 3D surface. This is acheived by processing user-provided 2D raster graphics that include blobs of unique colors to designate an area where a particular stitch pattern should be used when knitting the given shape. The specifications of each stitch pattern are based on the info you pass as arguments to theshapeImgToKnitoutfunction.
(NOTE:no two blobs separated by any white space can be the same color, even if they represent the exact stitch patterns).
Different stitch pattern graphics can be provided for the front and back of the shape.Function ParametersshapeImgToKnitoutTODOWorkflowInstallationFrom the command line, install thepackagewith pip:pip install knitout-kniterate-3DExample UsageGenerate the graphics that you will be using for the knitted object. It is recommended that you place these graphics in thegraphicssub-directory.
After installing the package, create a python file (e.g.my-file.py) and add the following code to your file:from knitout_kniterate_3D import knit3D, knitoutThen add the following code to initiate the knitout writer:k = knitout.Writer('1 2 3 4 5 6')Then, call theshapeImgToKnitoutfunction from theknit3Dlibrary and pass any relevant information (see the function docstring inknit3D.py[located in thesrcsub-directory] for more information).TODOFinally, use the knitout writer to output the file:k.write(f'{fileName}.k')
Package: knitpy
This is a port of knitr (http://yihui.name/knitr/) and rmarkdown
(http://rmarkdown.rstudio.com/) to python.To start with, you can run theexample overview
document. To convert to all defined
output formats, runknitpy--to="all"--examples\knitpy_overview.pymd. This will
produce ahtml,docxandpdfoutput (if you havepdflatexin path). You can view amarkdown
renderedand ahtml
renderedversion of this file. It’s not yet as pretty as the knitr version…For a description of the code format seehttp://rmarkdown.rstudio.com/and replace{r <r style options>}by{python <python style options>}and of course use python code
blocks…It uses the IPython kernel infrastructure to execute code, so all
kernels for IPython are (aem… can potentially be) supported.What works:code blocks and inline codeplots are shown inlineknitpy filename.pymdwill convert filenamefilename.pymdto
the defaul output formathtml.output formatshtml,pdfanddocx. Change with--to=<format>--to=allwill convert to all export formats specified in the yaml
headercode chunk argumentseval,results(apart form “hold”),includeandechoerrors in code chunks are shown in the documentuses the IPython display framework, so rich output for objects
implementing_repr_html_()or_repr_markdown_(). Mimetypes
not understood by the final output format are automatically converted
via pandoc.config files: generate an empty one withknitpy--init--profile-dir=.using it from python (-> your app/ ipython notebook):import knitpy; knitpy.render(filename.pymd,output="html")will
convertfilename.pymdtofilename.html.output=allwill
convert to all document types (as specified in the YAML header of the
document). The call will return a list of converted documents.debugging with `--debug,--kernel-debug=True,--output-debug=TrueWhat does not work (=everything else :-) ):most YAML headers are currently ignoredsome advertised command-line options are ignoredmost code chunk arguments (apart from the ones above) are ignoredprobably lots of other stuff…Todofix the above…refactor the parsing, so that it is line basederrors make more sense, because it knows the line (“block starting at
line….”)add some traits for the default pdflatex/pandoc executeable, so they
don’t have to be in paththe final output has to configure the “includeable” markup docshtml in htmllatex in html?…more arguments for code blocksmore output formats? -> make output format configurablemore unit-/outputtests…codeblocks + inlineyamlerrorspandoc caller (via mocks?)Documentationwhat works? what is not supported?differences to rmarkdown / knitr?implement more kernel engines (R…) and make it possible to
supply/change ones (for installed kernels for python2/3 or coda
environments)implement a nice default html templateTryhttps://github.com/timtylin/scholdoc-templatesimplement “code tidying”maybe usehttps://github.com/google/yapf?use metadata in keep_md output (like rmarkdown does…should output#<title>\n<author>\n<date>before the restremove the first yaml block, but keep everything else…chunk caching
Package: knitropytorch
![.github/workflows/pytest.yml](https://github.com/HighDimensionalEconLab/knitropytorch/workflows/.github/workflows/pytest.yml/badge.svg)
# knitropytorch
Pytorch support for Knitro## Getting StartedTo set up your local development environment, please use a fresh virtual environment.Then run:pip install -r requirements.txt -r requirements-dev.txtYou can now import functions and classes from the module withimport knitropytorch.### TestingWe usepytestas test framework. To execute the tests, please runpytestTo run the tests with coverage information, please usepython setup.py testcovand have a look at thehtmlcovfolder, after the tests are done.