Saravanan666/pypi-org huggingface dataset:
Package: lpdr
License Plate Detector & Recognizer (lpdr):heavy_check_mark: Low code:heavy_check_mark: Scalable:heavy_check_mark: Easy to modify:heavy_check_mark: IndependableLicense plate extractor with optical character recognition.:arrow_forward: If you want to understand all the underlying processes please read mypaperon it. :arrow_backward::rocket: How it works:rocket: InstallationUse the package managerpipto install lpdr.pipinstalllpdrorRecommended: Clone this repo for direct lpdr usage.gitclonehttps://github.com/szachovy/lpdr.gitWarning!Tested on Python 3.7.4:rocket: UsageCase 1: LPDR as a moduleimportlpdrIf you only want to extract license plate from an image.lpdr.LPD().Y# returns numpy.ndarrayIf you want to get license plate numbers from an image.lpdr.LPR().output# returns stringCase 2Recommended: LPDR as a microservice# in main directorypythonlpdr/lpdr.py[-h][-cCAPTURE][-iIMAGE_PATH]# returns stdout/stderrwhere:-h,--helpshow this help message and exit-c CAPTURE,--capture CAPTURECaptures image from camera after run (0/1 or True/False),Falseby default-i IMAGE_PATH,--image_path IMAGE_PATHDefines input/output path for image (relative or absolute path to the image)'image.png'or'image.jpg'by defaultExample of CLI usage:pythonlpdr/lpdr.py-cTrue-iimage.png# captures the picture from a given# optical source and saves it as image.png for lpdr program execution operations.Why it is better to use recommended choices?You may need to personalize the settings for more specified tasks, as a python package you would first need to find the package path.Here are the options available, you can change them as you wish.:Add tesseract utility for Windows (absolute path to tesseract.exe), it sometimes causes the problems on Windows machines if you do not add it.Defaults for argparser (capture / image_path)Input camera sourceWidth of the image to be capturedHeight of the image to be capturedWpod-net path (or different one if you have .h5 and .json files after training)IOU thresholdConfidence if the detected object is a license plateAlpha normalization parameterConfidence with plate sizing parametersLoss functionOEMPSMWarning!Depending on the tensorflow version there might be some issues with keras. Upgrade TF to 2.0 or higher if you can.:rocket: ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update the tests as appropriate.:rocket: ReferencesLicense Plate Detection ECCV 2018 paperSergio Montazzolli Silva, Claudio Rosito JungALPR for unconstrained scenariousOptical Character Recognition by Open source OCR Tool Tesseract: A Case StudyChirag Patel, Atul Patel, PhD, Dharmendra PatelAn Overview of the Tesseract OCR EngineRay Smith, Google Inc.Wrapper for Google's Tesseract-OCR Engine.:rocket: AuthorWiktor Jakub MajIf you use lpdr in your work please cite my paper!@article{article,
author = {Maj, Wiktor},
year = {2020},
month = {09},
pages = {1-13},
title = {Matrix operations on License Plate Detector and Recognizer (LPDR)}
}:rocket: LicenseMIT
Package: lpdraw
lpdrawThis is a simple drawing package that contains Line and Circle Drawing Algorithms.
This package can display the output on terminal as well as visually appealing grayscale image.UsageExample:fromlpdrawimportLine,Circle,Clear,DisplayTerminalfromlpythonimporti32,Constfromnumpyimportempty,int32defmain():Width:Const[i32]=100# x-axis limits [0, 99]Height:Const[i32]=40# y-axis limits [0, 39]Screen:i32[Height,Width]=empty((Height,Width),dtype=int32)Clear(Height,Width,Screen)Line(Height,Width,Screen,2,4,99,11)Line(Height,Width,Screen,0,39,49,0)Circle(Height,Width,Screen,52,20,6.0)DisplayTerminal(Height,Width,Screen)main()Output:$pythonmain.py
+----------------------------------------------------------------------------------------------------+|.||.||..||.||.||.||..||.||.||.||..||.||.||..||......||...||...||...||....||...||...||....||...||...||.....||...||.||.||........||................||...............||...............||..............||................||...............||........||.||..||.||.|+----------------------------------------------------------------------------------------------------+You can also use theDisplay()provided in this package to generate a grascale.pgmimage. You need to save the output produced on execution to a.pgmfile. For example,pythonmain.py>img.pgm
Package: l-pdseries-2-l-dfs
#Description: this function: <l_pdseries_2_l_dfs> converts a list of
#pandas series to list of pandas data frame. For further easy analysis of
#list of dfs. Many times we are faced that we have a list of list of pandas df.
# this function helps in such situations#To use:import l_pdseries_2_l_dfs as s2dfl_series_2_l_dfs = s2df.l_pdseries_2_l_dfs(l_series, ndfs)#where l_series is a list of pandas series. It must be of the shape:>>shape_of_l_series = [inner_element.shape for inner_element in l_series]
>>shape_of_l_series[(1232,),(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1232,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(1110,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(234,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(1930,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,),
(841,)]# so we have a list of series of pandas series with shapes each: [(1232,), (1110,), (234,), (1930,), #(841,)]
#So if we want to create a data frame out of these list of series we can use the above module.
#Again we have 33 series (columns) of each of the shape, so we want to make a data frame with 33. Also, #we have 5 different series so we want to have 5 dfs. hence, ndfs = 5 in this case. please choose #according to your data the value of ndfs. correct value of ndfs is imporant otherwise it will throw #error. The return value of module is a list of data frames of shape: [(1207, 33), (1086, 33), (234, #33), (1905, 33), (816, 33)]
Package: lpfics
LPFICS Lib to find incompatible constraints set in Linear ProblemsLPFICS IdeaObjective: This project aims to enable to find ONE incompatible constraint set if a
model is considered infeasible once resolved.
Note that it MAY NOT be the ONLY ONE set of constraints making your problem infeasible.This project is based on PuLP (https://github.com/coin-or/pulp) a LP modeler written
in Python. PuLP integrates the solver CBC. This solution works with CBC, and GLPK solvers
but does not work with Gurobi (as the output is always NotSolved and not Infeasible if
the problem is overconstrainted).Here is a simplified explaination:
If a problem is overconstrainted, the problem will:test to solve the problem with the first half of the constraintsif there is no solution it continues taking the first half (of the half)
of the constraintsif there is a solution, it will test the other halfif the second half has no solution it will continue dividing the second half
set of constraints to test itif the second half has a solution it will rearrange the constraints and
test againReferencesThe methodology (quickXplain) on which the program is based is described, discussed and modeled on the following references:Junker, Ulrich. 2004. Â« QUICKXPLAIN: Preferred Explanations and Relaxations
for Over-Constrained Problems Â», In:AAAI-04. San Jose.https://www.aaai.org/Papers/AAAI/2004/AAAI04-027.pdfMorriet, Lou, Benoit Delinchant, Gilles Debizet, FrÃ©dÃ©ric Wurtz. 2020. Â«
Algorithme d identification de contraintes incompatibles pour les problemes
d optimisation : application Ã  un projet energetique Â». In:soumis Ã  IBPSA
2020.Rodler, Patrick. 2020. Â« Understanding the QuickXPlain Algorithm: Simple
Explanation and Formal Proof Â». ArXiv:2001.01835, janvier.http://arxiv.org/abs/2001.01835.Verger, Guillaume, 2016. Ruby-Cbc [Logiciel]. Ruby. In :Github. Consulte
le 15 novembre 2020,https://github.com/gverger/ruby-cbc.Installation HelpPlease install LPFICS Lib with pip using the command prompt and type:pip install git+https://gricad-gitlab.univ-grenoble-alpes.fr/omegalpes/lpfics.gitPlease askomegalpes-users@groupes.renater.frif you are struggling during your installationLibrary Installation RequirementsPuLP > 1.6.10CopyrightThis project contains a derivative work based onhttps://github.com/gverger/ruby-cbc.
Project ruby-cbc is released under the MIT license, Copyright 2016 Guillaume Verger.
Modifications Copyright 2019 G2Elab / MAGE
See the LICENSE file for copyright information.Main Authors:Guillaume VERGERLou MORRIETPartners:Univ. Grenoble Alpes, CNRS, Grenoble INP, G2Elab, CEA, UniversitÃ© Paris-SudAcknowledgments:This work has been partially supported by the CDP Eco-SESA receiving fund from the French National Research
Agency in the framework of the "Investissements dâ€™avenir" program (ANR-15-IDEX-02), by the ADEME, the French Agency for Environment and Energy
Management, with the RETHINE project and by the Region Auvergne RhÃ´ne Alpes with the project OREBE.
Package: lpi-andor
LPI AndorAndor spectrometer controller built for the LPI group at EPFL.NotesLimitationsCan only be run from Windows machines at the moment.Acquiring dataBackground measurements are taken with theSpectrometer#backgroundmethod, and signals are taken with theSpectrometer#aquiremethod.
Each returns a 1D Numpy array of counts. The wavelength for each index is stored in theSpectrometer.wavelengthsproperty.
TheSpectrometer.wavelengthsproperty is reset any time theSpectrometer.center_wavelengthproperty is set, so be sure to save the
wavelength of previous scans before changing the center wavelength.Spectrograph configurationFor properties specific to your spectrograph (e.g. Filters, read out rates, etc.), define their configuration in theenumsmodule.ShutdownWhen runningSpectrometer#shutdownmethod, the instance will block until the sensor temeprature has reached -15 C to prevent the sensor from heating too quickly. This can be overriden by callingSpectrometer#shutdown( safe = False )or by setting the default exit behavior with theSpectrometer.exit_safeproperty.Modulesspectrometer:Defines Spectrometer representing high level functionality of the spectrometer.enums:Enums representing the spectrometer configuration.measurement_parameters:Measurement parameters that are used to intialize the spectrometer.Exampleimporttimeimportnumpyasnpfromlpi_andorimportSpectrometer# intialize spectrometerspec=Spectrometer()spec.temperature=-70spec.enable_cooler()# wait for temperaturewhilespec.temperature<70:time.sleep(10)# setup measurement# center: 800 nm# 10 scans, each for 1sspec.center_wavelength=800spec.acquisition_accumulate()spec.accumulation_scans=10spec.exposure_time=1# take measurementbackground=spec.background()signal=spec.acquire()# compile datadata=np.dstack((spec.wavelengths,background,signal))print(data)# shutdownspec.shutdown()
Package: lpip
LocalPIP/LPIPLocalPIP is an offline package manager for Python...It solves packages dependencies conflict issues and compiling errors.Internet access is required only for the first time of downloading LocalPIP repository.Once downloaded, you can install packages completely offline.LocalPIP/LPIP initialization:DescriptionCommandTo install LocalPIPpip install lpip -UTo download the local repo for the first timelpip downloadTo update the local repolpip updateTo delete the local repolpip deleteAbout localpiplpip aboutUsagelpip <Action> <PackageName>lpip <Action> <PackageName1> <PackageName2>DescriptionCommandActionsinstall / uninstall / upgradePackageNameone package / multiple packagePackages management (localpipcan be used instead oflpip)DescriptionCommandTo install one packagelpip install pandasTo install multiple packageslpip install pandas openpyxl KerasTo install requirement filelpip install reqTo uninstall a packagelpip uninstall pandasTo upgrade a packagelpip upgrade pipTo onlone upgrade a packagelpip onlineupgrade pipTo uninstall a packagelpip uninstall pandasTo check all installed packageslpip listCurrent available packages:LocalPIP Packages
Package: lpi-prediction
LPI-Prediction: A template library to build lncRNA-protein interaction prediction algorithmsLPI-Prediction aims to be a simple and easy-to-use template library to develop lncRNA-protein interaction prediction algorithms, or any prediction algorithm in general. LPI-Prediction provides a set of abstractions to load and process data, and a set of abstractions to train, evaluate, and execute the interaction prediction algorithms. LPI-Prediction is designed to be easily extensible, and to greatly lower the cost of developing production-level lnRNA-protein interaction prediction algorithms.InstallationLPI-Prediction is available on PyPI, and can be installed using pip as follows:user@host$python3-mpipinstalllpi-predictionPlease note that LPI-Prediction requires Python3.7 or higher.ExamplesLPI-Prediction provides a set of examples to demonstrate how to use the library. The examples are available in theexamplesdirectory of the repository. The examples are:knn.py: A simple example that shows how to load and process data, and how to train and evaluate a K-Nearest Neighbors (KNN) prediction algorithm.svm.py: A simple example that shows how to load and process data, and how to train and evaluate a Support Vector Machine (SVM) prediction algorithm.ensemble.py: A simple example that shows how to load and process data, and how to train and evaluate an ensemble of prediction algorithms (KNN + SVM).Moreover,MIRLOshows how to use LPI-Prediction to develop a production-level lncRNA-protein interaction prediction algorithm
that does automatic hyperparameter tuning, and uses third party applications (Rscript) to extract features from the lncRNA and protein sequences.LicenseLPI-Prediction is licensed under the MIT License. See theLICENSEfile for more information.AuthorsLPI-Prediction was developed byIñaki Amatria-Barral.
Package: lpips-j
LPIPS-JThis is a minimal JAX/Flax port oflpips, as implemented in:richzhang/PerceptualSimilarityTaming TransformersOnly the essential features have been implemented. Our motivation is to support VQGAN training forDALL•E Mini.It currently supports thevgg16backend, leveraging the implementation inflaxmodels.Pre-trained weights for the network and the linear layers are downloaded from the🤗 Hugging Face hub.InstallationInstall JAX for CUDA or TPU following the instructions athttps://github.com/google/jax#installation.Install this package from the repository:pip install --upgrade git+https://github.com/pcuenca/lpips-j.git
Package: lpips-jax
LPIPS-JaxJax port of the originalPyTorchimplementation ofLPIPS. The current version supports pretrained AlexNet and VGG16, and pretrained linear layers.Installationpip install lpips_jaxUsageForreplicate=False:importlpips_jaximportnumpyasnpimages_0=np.random.randn(4,224,224,3)images_1=np.random.randn(4,224,224,3)lpips=lpips_jax.LPIPSEvaluator(replicate=False,net='alexnet')# ['alexnet', 'vgg16']out=lpips(images_0,images_1)Forreplicate=Trueimportlpips_jaximportnumpyasnpimportjaxn_devices=jax.local_device_count()images_0=np.random.randn(n_devices,4,224,224,3)images_1=np.random.randn(n_devices,4,224,224,3)# replicate=True is the default settinglpips=lpips_jax.LPIPSEvaluator(net='alexnet')# ['alexnet', 'vgg16]out=lpips(images_0,images_1)# internally calls jax.pmap
Package: lpjsmin
lpjsmin for minifying js filesWe need to handle several minification goals within the Launchpad codebase.
This helps to create a single lib to help handle all of the cases and allow it
to be flexibly used in more projects.Usagecmd lineYou can pass the cmd linelpjsmineither a filename or a directory and it’ll
process the file(s) adding a $fname-min.js next to the original file. This is
used for things like minifying files in the combo directory. You can just let
it know the root of the combo directory and it’ll minify all files it finds in
there.$ lpjsmin -p static/js/
$ lpjsmin -p static/js/myapp.jspython usageYou can import the module and minify in your own build scripts either via just
filename and directory.import lpjsmin
lpjsmin.minify('static/js')
lpjsmin.minify('static/js/myapp.js')If you need to be able to customize the name or location of the minified
files, wrap the lpjsmin script in your own build script and pass it the in/out
streams.import lpjsmin
lpjsmin.minify_stream(
    open('static/js/myapp.js'),
    open('/tmp/myapp.minified.js', 'w')
)stdinYou can also just pass text at the script via stdin and it’ll minimze it back
out to you.$ cat file.js | lpjsminNews0.6Add tox testing support and drop buildout.Add Python 3 support.Use PEP 508 environment markers.0.5Release date: 11-June-2012Ignore any files that don’t end in .jsFix broken logic detecting if this file was already minified or not that
allowed multiple nested minifications (filename-min-min-min.js)0.4Release date: 21-Feb-2012Fix typo in the setup.py0.3Release date: 21-Feb-2012Add argparse as a conditional dep for older Python versions.0.2Release date: 21-Feb-2012Move the file/directory based minifying to the -p/–path cmd line flag.Support minifying off of stdin and sending to stdout if no path is
specified.Fix the ipdb dependency that should not have been there.0.1Release date: 17-Feb-2012Initial pulling out of the Launchpad utils directory.
Package: lp-lint-doctest
lp-lint-doctestis a style
checker forPython doctestsstored in text files (as
opposed to doctests stored in docstrings), optimized for use inLaunchpad
development.  It applies the following
checks:Comments should be in the narrative part of the doctest (i.e. in
unindented text) rather than in Python examples.Narrative text lines are limited to 79 characters.Source lines in examples (beginning with>>>or...) are limited
to 71 characters.Expected output lines in examples are limited to 75 characters.Source and expected output lines should be indented by exactly four
spaces.There should be no trailing whitespace.reStructuredText-style headings should be used, not MoinMoin-style.A# noqacomment in any source line in an example suppresses the
comment, line length, indentation, and trailing whitespace checks for all
lines in that example.In addition,lp-lint-doctestrunspyflakeson the accumulated source code of
each doctest file.This project was split out from Curtis Hovey’spocket-lintproject.  Since that was written, many
other excellent linters have arisen for most of the other source types
supported bypocket-lint, but there seems to be no other viable
replacement for its doctest checks.lp-lint-doctestsupportspre-commit.  To
use it, add the following to therepossection of your.pre-commit-config.yamlfile:-   repo: https://git.launchpad.net/lp-lint-doctest
    rev: ''  # pick a git tag to point to
    hooks:
    -   id: lp-lint-doctestIf you need to add a custom doctest option flag, then addargs:[--allow-option-flag,MY_CUSTOM_FLAG_NAME].NEWS for lp-lint-doctest0.5 (2022-09-09)Bump line length limit to 79 characters, which better matches
Launchpad’sblackconfiguration.0.4 (2021-10-27)Run ontypes: [rst]by default.  This is a better default for use
with repositories other than Launchpad itself (which can override this).Declare support for Python 3.10.0.3 (2021-07-27)Allow# noqacomments to suppress comment checks as well.0.2 (2021-07-25)Add--allow-option-flagcommand-line option to register customdoctestoption flags.Allow comments in source lines in examples if they containdoctestdirectives.Allow suppressing checks for an example by adding a# noqacomment
to any of its source lines.0.1 (2021-07-24)Initial release, split out from pocket-lint.
Package: lplug
InstallationNOTE:
If you have both Python 2 and 3 installed in your system,
you probably need use pip3 instead of pip.If you want lplug without external integrations:pip install lplug.DocsFor a detailed explanation of Lplug features,
API reference and other, we suggest to go to the documentation of Lplug athttps://prettyetc.gitlab.io/lplug/index.html
Package: lp-monitor
==========LP Monitor==========.. image:: https://img.shields.io/pypi/v/lp_monitor.svg:target: https://pypi.python.org/pypi/lp_monitor.. image:: https://img.shields.io/travis/askz/lp_monitor.svg:target: https://travis-ci.org/askz/lp_monitor.. image:: https://readthedocs.org/projects/lp-monitor/badge/?version=latest:target: https://lp-monitor.readthedocs.io/en/latest/?badge=latest:alt: Documentation Status.. image:: https://pyup.io/repos/github/askz/lp_monitor/shield.svg:target: https://pyup.io/repos/github/askz/lp_monitor/:alt: UpdatesA Liquidity Provider Node monitoring facilitylp_monitor is an abstract cli client designed for monitoring probes.Actually it support only two methods:* getbalance* is_syncand three currencies:* BTC* KMD* MNZ```consoleUsage: lp_monitor [OPTIONS] METHODLiquidity Provider Monitoring CLIOptions:-c, --coin TEXT       Coin selection: BTC|KMD|MNZ-H, --home DIRECTORY  Sets home directory which contains <coins>.conf files-h, --help            Show this message and exit.```Example:```console$ lp_monitor -H $HOME -c mnz is_sync0$ lp_monitor -lp_monitor -H $HOME -c mnz getbalance2233.10000000```* Free software: GNU General Public License v3Features--------* TODOCredits---------This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template... _Cookiecutter: https://github.com/audreyr/cookiecutter.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage=======History=======0.1.0 (2018-01-03)------------------* First release on PyPI.