Saravanan666/pypi-org huggingface dataset:
Package: linearSubproblemSltns
Summary:This is a research project led by Alexander Elias and Dr. John Wen at RPI.Summation of research:https://arxiv.org/pdf/2211.05737.pdfIncluded here is the Python version of the MATLAB-based canonical subproblems for inverse kinematics on robots.Additional MATLAB, C++, Rust, and Python files are located athttps://github.com/rpiRobotics/linear-subproblem-solutions/.See the project on PyPi:https://pypi.org/project/linearsubproblemsltns/Python Branch Guide:Dependencies:NumPy:https://numpy.org/install/SciPy:https://scipy.org/install/Python (version compatible w/ installed NumPy)To Use Example Code:Clone the above repoTo see timing information and a simple demo, run each sp#.py fileOtherwise, just include the pip module in your applications as needed using pip install linearSubproblemSltnsAdvice:NumPy vs MATLAB can be confusing at first, seethis articlefor some clarity.See the below note on precision/formatting.NumPy makes it extremely easy to export/import entire matrices as csv/excel files.  Make use of this if you can.Precision and FormattingIf comparing values in Python to MATLAB output, be careful with how inputs are entered.Also, note that values printed in the test files are not the exact values used in the calculations.Vectors in NumPy are of form [n n n] (0 row, 3 col).  You cannot have a vector of 3 col, 0 row.Note, the MATLAB version of this code uses vectors of 3 col, 0 row, which is why some calculations look slightly different.In Python:p1 = [0.85421456 0.9145417  0.28164908]p2 = [0.71384302 0.84785577 0.40390217]k1 = [0.53432959 0.73260445 0.42164275]k2 = [0.89871158 0.33336884 0.2849258 ]In MATLAB:p1 = [0.85421456; 0.9145417;  0.28164908]p2 = [0.71384302; 0.84785577; 0.40390217]k1 = [0.53432959; 0.73260445; 0.42164275]k2 = [0.89871158; 0.33336884; 0.2849258 ]TimingSubproblemTime With InputsTime Without InputsSp178716.896 ns73754.058 nsSp2325238.43 ns318135.293 nsSp2E409206.773 ns400298.479 nsSp3127781.537 ns122444.399 nsSp4104747.862 ns99615.851 nsSp51353110.805 ns1341531.159 nsSp6614233.523 ns596708.225 ns
Package: linearsvc
AboutThis is a simple python package of Linear support vector machine model trained on the Minerva dataset. The trained model can be used to
predict the license shortname from the code.How to useInstalling the package:pip install linearsvcImport the trained model:from linearsvc import linearsvcHow to use:By calling sklearn's predict function:classifier = linearsvc(processed_comment)predictor = classifier.classify()short_name = predictor.predict(processed_comment)By directly calling predict_shortname():classifier = linearsvc(processed_comment)short_name = classifier.predict_shortname()
Package: linearwavetheory
Linear wave theoryA package that implements results from the linear wave theory of surface ocean gravity  waves.RequirementsPython3numpynumbawheel (If developing/Contributing)Pytest (If developing/Contributing)Setuptools (If developing/Contributing)InstallationMake sure that you have the requirements listed abovepip install linearwavetheoryto your desired python environmentTest withpython3 -c 'import linearwavetheory'. If this runs successfully, chances are everything worked.
Package: linearwinvolume
linearWinVolumeA Python implementation of pycaw that doesn't function on a decibel scaleIn order to linearly interface with Windows' volume control in a manner that matches the UI's output, linearWinVolume computes a logarithmic regression from user collected sample data points. From there it optionally applies a linear correction value such that when setting and getting the volume state it is accurate to rougly ~1 unit of Windows volume at all times.InstallationWindows:pipinstalllinearwinvolumeSetup (Need to setup every sound device individually):# With desired sound device as Output Device under windowsimportlinearwinvolumelinearwinvolume.setup()# From here, follow the CLI prompts to callibrate your sound device's dB levelsIn order to complete setup:Select a sample size of data points (Windows volume units)Input Windows volume values until Windows volume is 0(OPTIONAL) Compute a linear correctionThis massively improves accuracy on some sound devicesThe setup function will count down from 100 to 0 setting your volume accordinglyEnter any letter once the guessed value matches the true Windows valueUsage examplesThis python module offers 4 functions. The first,linearwinvolume.setup(), is used to callibrate the sound device. The rest are:# Set volume to 55%linearwinvolume.set_volume(55)# Get current volume, returns integer from 0 - 100linearwinvolume.get_volume()# Change volume, to increase volume, use a positive integer, to decrease use a negative valuelinearwinvolume.change_volume()ExplanationIn order to derive an equation that accurately represents all volume values form 0 to 100, a logarithmic regression is preformed on the collected sample values.Initially, the program computes the logarithmic regression which takes the form ofy = A ln(x) + BOftentimes, this is enough to maintain ~1 unit of Windows volume unit error.In order to improve accuracy on some devices, an additional linear term is added such that the new function takes the form ofy = A ln(x) + C x + (B + D)C is defined as 100 - intersect divided by the max volume in Db - initial logarithmic regression for x =100D is defined as the difference between the max volume andy = A ln(x) + C x + Bsolved for x = 100Using Mathematica, the resultant equation,y = A ln(x) + C x + (B + D), is solved for x, to reveal a newEquation, in order to use the get_volume() function. As a result of it using the Lambert W function W(z), this package requires scipy.The configuration file where values are saved is stored inside the pip directory and is global for a python installation. It takes the form of:[Headset (Headphone adapter)]natural logarithm coeff=10.690485218963337constant offset=-54.99262202770247correction coeff=0.05761118223586133min_vol=-50.0max_vol=0.0samples=25Release History1.1.0Added linear correction algorithm that dramatically improves accuracy on some devices1.0.0Initial ReleaseCreditsAdrian Ornelas â€“afornelas@outlook.comDistributed under the MIT license. SeeLICENSEfor more information.https://github.com/That-CC
Package: lineaug
LineAugAugment line images for improving OCR datasetsCLIusage: augment.py [-h] -i [IMAGES [IMAGES ...]] [-o OUTPUT] [-gt GROUND_TRUTH] [-n N] [-bg] [-e]

Augment OCR in the form of line images.

optional arguments:
  -h, --help            show this help message and exit
  -i [IMAGES [IMAGES ...]], --images [IMAGES [IMAGES ...]]
                        Path to line image(s).
  -o OUTPUT, --output OUTPUT
                        Output path where augmented images will be saved.
  -gt GROUND_TRUTH, --ground_truth GROUND_TRUTH
                        Extension of the ground truth text files. Will create new ground truth files for the augmented line images containing the existing ground truth for the associated
                        line (optional).
  -n N                  Number of augmented line image variants to create for each input.
  -bg, --background     Whether to add noise to the background of the line image.
  -e, --enumerate       Enumerate output file names instead of using input file names.
Package: lineax
LineaxLineax is aJAXlibrary for linear solves and linear least squares. That is, Lineax provides routines that solve for $x$ in $Ax = b$. (Even when $A$ may be ill-posed or rectangular.)Features include:PyTree-valued matrices and vectors;General linear operators for Jacobians, transposes, etc.;Efficient linear least squares (e.g. QR solvers);Numerically stable gradients through linear least squares;Support for structured (e.g. symmetric) matrices;Improved compilation times;Improved runtime of some algorithms;All the benefits of working with JAX: autodiff, autoparallelism, GPU/TPU support etc.InstallationpipinstalllineaxRequires Python 3.9+, JAX 0.4.13+, andEquinox0.11.0+.DocumentationAvailable athttps://docs.kidger.site/lineax.Quick examplesLineax can solve a least squares problem with an explicit matrix operator:importjax.randomasjrimportlineaxaslxmatrix_key,vector_key=jr.split(jr.PRNGKey(0))matrix=jr.normal(matrix_key,(10,8))vector=jr.normal(vector_key,(10,))operator=lx.MatrixLinearOperator(matrix)solution=lx.linear_solve(operator,vector,solver=lx.QR())or Lineax can solve a problem without ever materializing a matrix, as done in this
quadratic solve:importjaximportlineaxaslxkey=jax.random.PRNGKey(0)y=jax.random.normal(key,(10,))defquadratic_fn(y,args):returnjax.numpy.sum((y-1)**2)gradient_fn=jax.grad(quadratic_fn)hessian=lx.JacobianLinearOperator(gradient_fn,y,tags=lx.positive_semidefinite_tag)solver=lx.CG(rtol=1e-6,atol=1e-6)out=lx.linear_solve(hessian,gradient_fn(y,args=None),solver)minimum=y-out.valueCitationIf you found this library to be useful in academic work, then please cite: (arXiv link)@article{lineax2023,title={Lineax: unified linear solves and linear least-squares in JAX and Equinox},author={Jason Rader and Terry Lyons and Patrick Kidger},journal={AI for science workshop at Neural Information Processing Systems 2023,arXiv:2311.17283},year={2023},}(Also consider starring the project on GitHub.)FinallySee also: other libraries in the JAX ecosystemjaxtyping: type annotations for shape/dtype of arrays.Equinox: neural networks.Optax: first-order gradient (SGD, Adam, ...) optimisers.Diffrax: numerical differential equation solvers.Optimistix: root finding, minimisation, fixed points, and least squares.BlackJAX: probabilistic+Bayesian sampling.Orbax: checkpointing (async/multi-host/multi-device).sympy2jax: SymPy<->JAX conversion; train symbolic expressions via gradient descent.Eqxvision: computer vision models.Levanter: scalable+reliable training of foundation models (e.g. LLMs).PySR: symbolic regression. (Non-JAX honourable mention!)DisclaimerThis is not an official Google product.
Package: line-bot-sdk-extra
Extra feature forLINE Messaging API SDK for Python.Installationpip install line-bot-sdk-extraor:python setup.py installTo use the package:>>> import linebotxFeaturesAsynchronous APIAllows you to write non-blocking code which makes your bot respond much faster with little changes.Synchronous:fromlinebotimportLineBotApi,WebhookHandlerline_bot_api=LineBotApi('YOUR_CHANNEL_ACCESS_TOKEN')handler=WebhookHandler('YOUR_CHANNEL_SECRET')Asynchronous:fromlinebotximportLineBotApiAsync,WebhookHandlerAsyncline_bot_api=LineBotApiAsync('YOUR_CHANNEL_ACCESS_TOKEN')handler=WebhookHandlerAsync('YOUR_CHANNEL_SECRET')Equivalent CounterpartlinebotxlinebotLineBotApiAsyncLineBotApiAioHttpClientHttpClientAioHttpResponseHttpResponseWebhookHandlerAsyncWebhookHandlerNOTE:Every public method is coroutine and should be awaited. For example:@app.route("/callback",methods=['POST'])asyncdefcallback():...awaithandler.handle(body,signature)...@handler.add(MessageEvent,message=TextMessage)asyncdefhandle_message(event):awaitline_bot_api.reply_message(event.reply_token,TextSendMessage(text=event.message.text))Additional MethodscoroutineLineBotApiAsync.close()Close underlying http client.coroutineAioHttpClient.close()Seeaiohttp.ClientSession.close().TimeoutTo set a timeout you can passaiohttp.ClientTimeoutobject instead of numeric value.Examplessanic-echo- Sample echo-bot usingsanic.ContributingIf you would like to contribute, please check for open issues or open a new issue if you have ideas, changes, or bugs to report.ReferencesThis project is just a small addition to the original SDK, please refer toline-bot-sdk-pythonor thedocs.
Package: line-bot-tool
line-bot-toolLINEBot用の便利ツールPIPpipからインストール可能$pipinstallline-bot-toolUsageLINEBotで使用するコマンドを生成$lb-toolgen:commandhelpGeneratesuccess!commands/help_command.pyデフォルトで./commands配下に生成される。環境変数COMMAND_OUTPUT_PATHに設定することで、生成されるファイルの保存場所を変更できる。生成されたファイル# Generated by CommandGenerator on 2021-06-23 20:55:00fromtypingimportOptional,Tuplefromline_bot_tool.base.commnadimportBaseCommandfrom.importsettingsclassHelpCommand(BaseCommand):prefixes:Optional[Tuple[str,...]]=settings.PREFIXEScmd:str='help'description:str='Please input here command description.'同じ階層のディレクトリにsettings.pyが生成される。# Generated by CommandGenerator on 2021-06-23 20:55:00fromtypingimportOptional,TuplePREFIXES:Optional[Tuple[str,...]]=('/','@')PREFIXESの値を書き換えることでコマンドの接頭辞を設定できる。接頭辞が費用な場合は、Noneを入れる。fromcommands.help_commandimportHelpCommandHelpCommand.is_executable('/help')# TrueHelpCommand.is_executable('@help')# TrueHelpCommand.is_executable('help')# False引数があるコマンドの場合例えば、/name:引数というコマンド（引数にユーザーIDを指定し、名前を返す）がある場合、以下のようにコマンドの生成、判定を行う。また、接頭辞は/のみとする。$lb-toolgen:commandname
Generatesuccess!commands/name_command.pycommands/name_command.pyを少し書き換え。# Generated by CommandGenerator on 2021-06-23 21:34:39

from typing import Optional, Tuple
from line_bot_tool.base.commnad import BaseCommand
from . import settings


class NameCommand(BaseCommand):-   prefixes: Optional[Tuple[str, ...]] = settings.PREFIXES-   cmd: str = 'name'+   prefixes: Optional[Tuple[str, ...]] = ('/')+   cmd: str = 'name:'description: str = 'Please input here command description.'fromcommands.name_commandimportNameCommandNameCommand.is_executable_with_arg('/name:UXXXXXXXXXX')# True, UXXXXXXXXXX# セイウチ演算子を使って書くif(result:=NameCommand.is_executable_with_arg('/name:UXXXXXXXXXX'))[0]:print(result[1])# UXXXXXXXXXX
Package: linecache-light
LineCache caches the line position of a file in the memory. Everytime it access a line, it will seek to the related postion and readline().Noticing that it may cost some time when you first cache lines of a file.Usage:from linecache_light import LineCachelinecache = LineCache(‘a.txt’, cache_suffix=’.cache’)num_lines = len(linecache)line_0 = linecache[0]line_100 = linecache[100]line_indexing= linecache[[2,3,4,5]]line_indexing = linecache[-1]
Package: line-chain
line-chainValue Scheduler based on Progress.InstallationNeed Python 3.6+.pipinstallline-chainUsageimportline_chainline_chain=line_chain.factory(config=[{'mode':'linear','ratio':0.01,'start':0.2,'target':0.8},{'mode':'cosine','ratio':1.0,'target':0.0}])print(line_chain)#> LineChain (#>     1.0%, linear from 0.2 to 0.8,#>   100.0%, cosine from 0.8 to 0.0,#> )# plot it!importnumpyasnpimportpylabasplx=np.linspace(0,1,1000)y=np.vectorize(line_chain.at)(x)pl.plot(x,y)
Package: linechart
MoonchartThis package provides backtest performance analysis and charting forQuantRocket.Example tear sheetsMoonshot backtest:1-dimensional single-strategy parameter scan with Moonshot:1-dimensional multi-strategy parameter scan with Moonshot:2-dimensional parameter scan with Moonshot:Installation and UsageFor installation and usage documentation, please visit:https://www.quantrocket.com/docs/LicenseMoonchart is distributed under the Apache 2.0 License. See the LICENSE file in the release for details.
Package: linecook
linecookis a command-line tool that transforms lines of text into a form
that’s pleasant to consume.Documentation:https://linecook.readthedocs.ioSource:https://github.com/tonysyu/linecookInstallThe recommended way of installinglinecookis to usepip:pip install linecookCooking up some beautiful textThe core goal oflinecookis to make it easy to create your own transforms to
parse whatever text you have. For example, take anapp.logfile that looks
like:If you want to highlight the log type and mute the dates/times, then you can
create a custom recipe in one of yourconfiguration fileslike the
following:fromlinecookimportpatternsasrxfromlinecook.transformsimportcolor_textLINECOOK_CONFIG={'recipes':{'my-logs':[color_text(rx.any_of(rx.date,rx.time),color='blue'),color_text('INFO',color='cyan'),color_text('WARN',color='grey',on_color='on_yellow'),color_text('ERROR',on_color='on_red'),],},}To use this recipe, you can just pipe the log output tolinecookwith your
new recipe as an argument:That’s all there is to it!See Alsogrc: A generic colouriser (sic;) for log
files and command output.rainbow: Colorize commands output or
STDIN using patterns.multitail: Tail multiple files at
once, with features to colorize, filter, and merge.colortail: Like the tail command
line utility but with colorsStackOverflow post on colored output
Package: linecounter
Linecounterlinecounter is a tool written in python, you can count how many lines you have in your files. This tool may be useful for developers who want to count how many lines of code they have in their projects. Because of linecounter isn’t restricted for just code files it can be used in many areas which needs line counting.FeaturesLine counting for given file/filesLine counting for list of files in given directoryLine counting for list of files in given directory recursivelyLine counting for list of files in given directory with filtering file extensionsLine counting without empty linesSourceFind the latest version on github:https://github.com/halitkarakis/line-counterFeel free to fork and contribute!InstallationThe easiest way to install is with pip:pip install linecounterOr manually:python setup.py installUsageUsage:linecounter -fd [-r] path1 [path2 ...] [--filter ext1 [ext2 ...]]options:-f         Run line-counter with file paths
-d         Run line-counter with directory paths
-r         Search directories recursively, can be used if '-d' is set
--filter   Count lines for files which extension is ext1, ext2 ...,
           can be used if '-d' is set
--help     Show this message
--version  Show version info
--noempty  Count non-empty linesAuthorI’m Muhammet Halit Karakis from Istanbul, Turkiye. You can contact me by email halit (at) halitkarakis com tr
Package: line-counter
A command line tool to analyze the amount of lines and files under
current directory.It supports ‘line.select’ rules which works like ‘.gitignore’.InstallationYou can install, upgrade, uninstall line-counter.py with these commands:$ pip install line-counter
$ pip install --upgrade line-counter
$ pip uninstall line-counterUsageUsage: line [options] [args]

Analyze the amount of lines and files under current directory following the
rules in 'line.select' or analyze all files if 'line.select' doesn't exist

Options:
  --version     show program's version number and exit
  -h, --help    show this help message and exit
  -d, --detail  show more detail in the result
  -s, --show    show rules in 'line.select'Example usage and outputAnalyze all files (when ‘line.select’ doesn’t exist) or specific files
(when ‘line.select’ exists) under current directory.$ line
Search in /Users/Morgan/Documents/Example/
file count: 4
line count: 839Analyze all files (when ‘line.select’ doesn’t exist) or specific files
(when ‘line.select’ exists) under current directory.And show results in detail.$ line -d
Search in /Users/Morgan/Documents/Example/
Dir A/file C.c                                             72
Dir A/file D.py                                           268
file A.py                                                 467
file B.c                                                   32
file count: 4
line count: 839Show the rules in ‘line.select’$ line -s
Here are the rules in 'line.select' under /Users/Morgan/Documents/Example/:
#Select rules:
*.c

#Ignore rules:
!Dir A
!*.pyCheck current version$ line --version
line 0.7.4line.selectThis file works like ‘.gitignore’. * Each line is a rule * Each line
starts with a ‘#’ means that’s a comment * Each line starts with a ‘!’
means it’s a ignore rule * Otherwise it’s a select rule * The order of
rules is irrelevant and ignore rules can always override select rules *
The rules can find all the pathnames matching a specified pattern
according to the rules used by the Unix shell. No tilde expansion is
done, but *, ?, and character ranges expressed with [ ] will be
correctly matched.RoadmapAdd order output functionAdd option to only count file amount or line amountChange Log0.7.403/25/2017Fix distribution on PyPI.0.7.303/25/2017Fix the long description format on PyPI.0.7.203/25/2017Fix a bug that file cannot be matched when there is a ‘[’ in the
path.AuthorI’m Morgan Zhang, a graduate computer science student in University of
San Francisco.Why I made itI always want to know how many lines of code that I have done, but
there isn’t a easy way to get it.So I decide to make a tool for this.Contact InformationMorganZhang100@gmail.comLicenseThe MIT license.
Package: lineExtractor
Line ExtractorLine Extractor is a Python CLI for extracting certain lines from text files given specified rules.Why you should use this CLI toolIf you're trying to read through thousands of lines of logs or just want to see every time the word "Donkey" shows up in the Shrek movie script this tool can help you do that. Instead of greping with regex you can use a more intutive CLI.I created this tool because I got tired of reading through EC2 logs finding the lines beginning with certain phrases or ending with other phrases. So if this tool helps you out feel free to let me know!InstallationUse the package managerpipto install Line Extractor.pipinstalllineExtractorUsageGeneral FormatlineExtractor[Options]source_filedestination_fileOpen the help menulineExtractor-hGet all lines that contain the word "bee" and put them into output.txtlineExtractor-cbeebeeMovie.txtoutput.txtGet all lines that start with "Hello" regardless of casing and put them into hello.txtlineExtractor-bHello-ispeech.txthello.txtContributingGithubPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.AuthorKyle Van LandinghamLicenseMIT
Package: linefit
linefitlinefit is a ground segmentation algorithm for 3D point clouds. This repo we setup a python binding for the original C++ code and push to pypi for easy installation throughpip install linefit.Author: C++ code fromLorenz Wellhausen, nanobind byQingwen Zhang.Running on macOS, Windows and Linux, with Python Version >= 3.8.0. SetupChoose one of the following options to install the package (recommended to use Option Apip install linefit):Option A: Install from pypipip install linefitOption B: Clone this repo and run following to build:cmake-Bbuild&&cmake--buildbuild
pipinstall.
python3-c'import linefit; print("success")'1. Run the exampleAfter installation, you can run the example by, it will directly show a default effect of demo data.pythonexample.pyA window will pop up and show the ground segmentation result.Parameter descriptionTL;DR: tune thesensor_heightto offset the ground point z to0. Others are optional for better performance. If you are interested in the details, please read the following.Parameters are set inassets/config.tomlThis algorithm works on the assumption that you known the height of the sensor above ground.
Therefore,you have to adjust thesensor_heightto your robot specifications, otherwise, it will not work.The default parameters should work on the KITTI dataset.Ground Conditionsensor_heightSensor height above ground.max_dist_to_linemaximum vertical distance of point to line to be considered ground.max_slopeMaximum slope of a line.min_slopeMinimum slope of a line.max_fit_errorMaximum error a point is allowed to have in a line fit.max_start_heightMaximum height difference between new point and estimated ground height to start a new line.long_thresholdDistance after which the max_height condition is applied.max_heightMaximum height difference between line points when they are farther apart thanlong_threshold.line_search_angleHow far to search in angular direction to find a line. A higher angle helps fill "holes" in the ground segmentation.Segmentationr_minDistance at which segmentation starts.r_maxDistance at which segmentation ends.n_binsNumber of radial bins.n_segmentsNumber of angular segments.Othern_threadsNumber of threads to use.Acknowledgement & CitationThe original C++ code is fromthe repo we forked: lorenwel/linefit_ground_segmentation.The original methods are described in the following paper:@inproceedings{himmelsbach2010fast,
  title={Fast segmentation of 3d point clouds for ground vehicles},
  author={Himmelsbach, Michael and Hundelshausen, Felix V and Wuensche, H-J},
  booktitle={Intelligent Vehicles Symposium (IV), 2010 IEEE},
  pages={560--565},
  year={2010},
  organization={IEEE}
}More python binding examples can be found in our other project:dufomap: a dynamic awareness mapping framework. Remove dynamic points in a raw map.dztimer: a breakout timer for python code.