Saravanan666/pypi-org huggingface dataset:
Package: lappy
Lappyis a fork of Sophia Lin’slazyraythat supports more operations beyond arithmetics.InLappy, everything computable is considered an array object (unlikenumpy, it does not offer separate scalar types).
An array object has six basic components: the shape, the dtype, the expression, the value, the formal argument list, and the environment (capture list).
Scalars are special arrays with shape being an empty tuple.
If the dtype is an integer type, there can be assumptions associated with the array elements represented by an integer set (for scalars) or map (for non-scalars).DocumentationPlease visithttps://xiaoyu-wei.com/docs/lappy/for the latest documentation.StatusThis is a list of things that can be achieved withlappytoday:✅ Arithmetic and scalar mathematics (lifted scalar functions / universal functions)✅ Axis reordering / transpose✅ Reshaping✅ Broadcasting (semi-dynamic)ScopeRoadmap✅ Scan and fold✅ Tensor contractions, likenp.einsum(including dot products and matrix multiply)✅ Slicing (static and dynamic)✅ Fancy indexing (static and dynamic)✅ Integration with Numpy’s dispatch mechanism like__array__and__array_ufunc__✅ Some linear algebra likesvd,qr,lstsqAnti-features❌ Another array IR like APL.lappyis not an IR and does not intend to be.It serves as a tool to build parametrized array expressions for lazy evaluation,
and strives to providenumpycompatible APIs.Lappyis licensed under theMIT licenseand free for commercial, academic,
and private use.
Package: lap-random-spanish
Random Spanish InformationWhat is that?This library is my first library created for Python (so sorry if there are any bugs).
With this library you will be able to obtain both names (for men and women) and Spanish surnamesHow can I install it?Just install with:pip install lap-random-spanishorpip3 install lap-random-spanishHow can I use it?You've got two ways:Download the CSVs locally (Recommended)I recommend you download and import the csv file locally, otherwise the library will load the CSV from my online hosting, which can slow down the speed of your program.
Just download the CSV here:MensWomensSurnamesThen, import files to your project and pass it in the third parameter of each function (files' parameter)mens_csv="./path/to/your/mans.csv"surnames_csv="./path/to/your/surnames.csv"print(getRandomMaleName(2,True,["men.csv","apellidos.csv"]))# Return an array with two full namesUse it without download CSV (Unrecommended)If you prefer, you can ignore the third parameter and then the library will load the CSV from my online hosting and then continue working.print(getRandomMaleName(2,True))# Return an array with two full namesDocumentationIn this library you've got three functionsGet random male namegetRandomMaleNameexpects three optional parameters:First oneexpects a integer with the number of the names you need. Default is 1If you pass 1, function will return a string. Otherwise, It'll return an array of stringsSecond oneexpects a boolean to return a full name or not. Default is FalseIf you pass True, function will return a full name with name and surname. Otherwise It'll return just the nameThird oneexpects a array string with the path to the CSV. Default is my website host.See theDownload the CSVs locally (Recommended)point for more infoGet random female namegetRandomFemaleNameexpects three optional parameters:First oneexpects a integer with the number of the names you need. Default is 1If you pass 1, function will return a string. Otherwise, It'll return an array of stringsSecond oneexpects a boolean to return a full name or not. Default is FalseIf you pass True, function will return a full name with name and surname. Otherwise It'll return just the nameThird oneexpects a array string with the path to the CSV. Default is my website host.See theDownload the CSVs locally (Recommended)point for more infoGet random surnamegetRandomSurnameexpects three optional parameters:First oneexpects a integer with the number of the surnames you need. Default is 1If you pass 1, function will return a string. Otherwise, It'll return an array of stringsSecond oneexpects a boolean to return a pair of surnames or not. Default is FalseIf you pass True, function will return a pairs of surnames as in Spain everybody got two surnames. Otherwise It'll return just one surnameThird oneexpects a array string with the path to the CSV. Default is my website host.See theDownload the CSVs locally (Recommended)point for more infoLast test was done in Python 3.9.7
Package: laproxy
LaProxyIntroductionAn easy to use proxy for A/D CTFsYou can read the documentation atrikyiso01.github.io/LaProxyCode SamplesTCPfromlaproxyimportTCPProxy,TCPHandlerclassHandler(TCPHandler):defprocess(self,packet:bytes,inbound:bool,/)->bytes|None:ifb"ciao"inpacketandnotinbound:returnNonereturnpacketif__name__=="__main__":TCPProxy("0.0.0.0",1234,"127.0.0.1",5005,Handler).run()HTTPfromlaproxyimportTCPProxy,HTTPHandler,HTTPRequest,HTTPResponseclassHandler(HTTPHandler):defrequest(self,request:HTTPRequest,/)->HTTPRequest|None:returnrequestdefresponse(self,response:HTTPResponse,/)->HTTPResponse|None:ifb"flag"inresponse.body:returnNonereturnresponseif__name__=="__main__":TCPProxy("0.0.0.0",1234,"127.0.0.1",5005,Handler).run()You can find more examples in the samples folderInstallationInstall locally with:python3-mpipinstalllaproxyOr use it in a docker compose:version:"3.9"services:proxy:image:ghcr.io/rikyiso01/laproxy:latestports:-"1234:1234"volumes:-./proxy.py:/app/proxy.py
Package: lapsepy
Installpip install lapsepyGetting StartedFeatures:Modify your bio, display name, date of birth, emojis, and username. Many past what's allowed on the app!Upload any image to your darkroom and make it develop at any time!Get your friends feed, and download images.Getting your refresh token:One of the most challenging parts of this project is authentication, it uses Apple's App Attest in place of a captcha. As far as I'm aware there's no work around without jailbreaking your phone / using a modified version of the app. I'm planning on waiting for Lapse's Android version to try to reverse engineer it there.How to get your refresh token (Windows-Iphone):Method 1 (Semi automated):Follow the instructions on myLapsePyRefreshTokenSnifferproject.Method 2 (Completely manual):Log out of the appInstallMitMProxyMake sure to install all certificates required.CertificatesStart MitMWeb ignoring the hostregister.appattest.apple.comusingmitmweb --ignore-hosts register.appattest.apple.comGet your computer's ip address. Open your terminal and writeipconfigOn your phone go to Settings -> Network ->  -> info -> proxy -> manual and enter the proxy information.Open the Lapse app and sign in.Watch your proxies API calls for the second call tohttps://auth.production.journal-api.lapse.app/verifyand inspect it.Go to the Response tab, and setviewtojsonCopy the refresh token from the response.How to use examples:Clone the repositorygit clone https://github.com/quintindunn/lapsepy.gitOPTION 1: Set your refresh token in your environment variable asREFRESH_TOKENOPTION 2: In the examples replace theos.getenv("REFRESH_TOKEN")with"YOUR_REFRESH_TOKEN"Run the file.
Package: laptimize
laptimize - Linear Approximated Programming for Optimizationlaptimize is a python package to solve separable non convex optimization problems which is based on branch and bound algorithm developed by  James E.Falk (1972) of The
George Washington University. It has been proven that it can generate global solution for large class of nonlinear programming problems in a finite number of steps.
In many practical cases Nonlinear optimization problems are hard to solve and only limited methods are implemented. Many such problems, can be approximated arbitrarily closely by separable problems if all functions are piecewise linear. The laptimize python package will find a global optimum to these latter problems.Installationpip install laptimizeDependencieslaptimize requires Python >= 3.6NumPy (>= 1.19.2)Pandas (>= 1.1.3)PuLP (>= 2.3)ExamplesRefer Examples directory for more examplesproblem type whichlaptimizecan appliesSeparable linear/non-linear convex/non-convex minimization objective functionMinimize:F0(x1, x2) = 12x1 + 7x2 - x2^2Subject to separable linear/non-linear convex/non-convex constraint with '<=' inequalityconvex polygonF1(x1, X2) = 2x1 + x2 - 0 <= 0non convex constraintF2(x1, X2) = -2x1^4 -x2 +2 <= 0

        0 <= x1 <= 2, 0 <= x1 <= 3Example Codefrom laptimize.solver import Solver


example_1 = {'objective': {'x1': lambda x: 12 * x, 'x2': lambda x: 7 * x - x ** 2},
             'constraint_1': {'x1': lambda x: -2 * (x ** 4), 'x2': lambda x: -x, 'value': -2},
             'constraint_2': {'x1': lambda x: 2 * x, 'x2': lambda x: x, 'value': 3},
             'capacity': {'x1': [0, 2], 'x2': [0, 3]}}


solution = Solver(example_1, partition_len=0.5).solve()


print(solution)Comments, bug reports, patches and suggestions are welcome.Comments and suggestions:https://github.com/uichathurika/laptimize/discussionsBug reports:https://github.com/uichathurika/laptimize/issuesPatches:https://github.com/uichathurika/laptimize/pulls
Package: laptools
laptools is currently unstable.Fast Assignment Problem SolversInstallationTo install laptools, run this command in your terminal:$pipinstall-UlaptoolsThis is the preferred method to install laptools,
as it will always install the most recent release.If you don't havepipinstalled,
theseinstallation instructionscan guide
you through the process.DevelopmentSeeCONTRIBUTING.mdfor information related to developing the code.
Package: laptop-pm
laptop-pm=========A python power mangagment scripts for a laptop which running linux.Installation------------visit `installing <https://bitbucket.org/igraltist/laptop-pm/src/tip/INSTALL.rst>`_ webpageUsage-----Use an acpi event to put the laptop in different powerstage.usage::usage: laptop-pm [-h] [-v] [-f]  {battery,ac-adapter,show}positional arguments::{battery,ac-adapter,show}  action to perform-h      show this help message and exit-v      make the script noisily-f      do action even its in the same stageSet device to manage:- `example config for devices <https://bitbucket.org/igraltist/laptop-pm/src/tip/docs/examples/etc/laptop-pm/laptop-pm.json>`_To use it with an acpi event see:- `battery <https://bitbucket.org/igraltist/laptop-pm/src/tip/docs/examples/etc/acpi/events/battery>`_- `ac-adapter <https://bitbucket.org/igraltist/laptop-pm/src/tip/docs/examples/etc/acpi/events/ac-adapter>`_You can just copy it to directory /etc/acpi/events and modify it.To get your acpi event just call::acpi_listenYou need create a file for local rc because on boot there is no acpi event.- `startup battery check <https://bitbucket.org/igraltist/laptop-pm/src/tip/docs/examples/etc/local.d/set-power-battery.start>`_Gentoo::Create /etc/local.d/set-power-battery.start and make it executable and write follow content:# check if the laptop start with batteryif [ "$(laptop-pm show)" = "battery" ]; thenlaptop-pm batteryfi
Package: lapx
Linear Assignment Problem Solverlapxbasically is Tomas Kazmar'sgatagat/lapwith support for all Windows/Linux/macOS and Python 3.7/3.8/3.9/3.10/3.11/3.12.Based on:[ed04ab7752]License: BSD-2-Clause, seeLICENSE@gatagatInstallation: Windows ✅ | Linux ✅ | macOS ✅Install fromPyPI:pip install lapxOr install from GitHub repo directly (Require C++ compiler):pip install git+https://github.com/rathaROG/lapx.gitOr clone and build on your local machine (Require C++ compiler):git clone https://github.com/rathaROG/lapx.git
cd lapx
python -m pip install --upgrade pip
pip install "setuptools>=67.2.0"
pip install wheel build
python -m build --wheel
cd distUsage 🧪lapxis just the name for package distribution.The same aslap, useimport lapto import; for example:import lap
import numpy as np
print(lap.lapjv(np.random.rand(4, 5), extend_cost=True))Click here to show more...lap: Linear Assignment Problem solverlapis alinear assignment
problemsolver using
Jonker-Volgenant algorithm for dense (LAPJV [1]) or sparse (LAPMOD [2])
matrices.Both algorithms are implemented from scratch based solely on the papers [1,2]
and the public domain Pascal implementation provided by A. Volgenant [3].In my tests the LAPMOD implementation seems to be faster than the LAPJV
implementation for matrices with a side of more than ~5000 and with less than
50% finite coefficients.[1] R. Jonker and A. Volgenant, "A Shortest Augmenting Path Algorithm for Dense
and Sparse Linear Assignment Problems", Computing 38, 325-340 (1987)[2] A. Volgenant, "Linear and Semi-Assignment Problems: A Core Oriented
Approach", Computer Ops Res. 23, 917-932 (1996)[3]http://www.assignmentproblems.com/LAPJV.htmUsagecost, x, y = lap.lapjv(C)The functionlapjv(C)returns the assignment cost (cost) and two arrays,x, y. If cost matrixChas shape N x M, thenxis a size-N array specifying to which column is row is assigned, andyis a size-M array specifying to which row each column is assigned. For example, an output ofx = [1, 0]indicates that row 0 is assigned to column 1 and row 1 is assigned to column 0. Similarly, an output ofx = [2, 1, 0]indicates that row 0 is assigned to column 2, row 1 is assigned to column 1, and row 2 is assigned to column 0.Note that this functiondoes notreturn the assignment matrix (as done by scipy'slinear_sum_assignmentand lapsolver'ssolve dense). The assignment matrix can be constructed fromxas follows:A = np.zeros((N, M))
for i in range(N):
    A[i, x[i]] = 1Equivalently, we could construct the assignment matrix fromy:A = np.zeros((N, M))
for j in range(M):
    A[y[j], j] = 1Finally, note that the outputs are redundant: we can constructxfromy, and vise versa:x = [np.where(y == i)[0][0] for i in range(N)]
y = [np.where(x == j)[0][0] for j in range(M)]
Package: lapymod
LapymodThis is a simple wrapper for the Labymod API.Installationpip install lapymodUsageimport lapymod

#This creates a user that lets you query and update their data.
user = lapymod.User('34e57efa578346c7a9fc890296aaba1f') #(The uuid of LabyStudio -- an admin for Labymod)

#Returns all emotes for the user into a list.
emotes = user.getEmotes()

#Returns all cosmetics for the user into a list.
cosmetics = user.getCosmetics()

#Returns all stickers for the user into a list.
stickers = user.getStickers()

#Returns all roles for the user into a list.
roles = user.getRoles()

#Returns all of the above functions in one nice easy to use dictionary.
all_data = user.getAllData()

#You can change the user while you go.
user.setUser('32645503-0d51-4fc6-8e4a-28b03714db5e') #(The uuid of LabyMarco -- another admin for Labymod)

#When you either want new data for the user or want to change the data with a new supplied user, update it!
user.update()

#Returns True/False if the module is able to connect to the Labymod API.
api_status = lapymod.getStatus()

#Returns the current daily shop emotes in a list.
shop_emotes = lapymod.getShopEmotes()
Package: lara-django
LARA django is a collection of applications for storing, evaluating and presenting experimental data and its meta data, based on python-django.
LARA has the following features:holistic approach, which covers:
- project planning
- process generation and organisation
- container management (barcodes, layout)
- data evaluation and visualisation
- subtances management and ordering
- user managementsmall, efficient and fast codemodular, fully flexible plugin architecture, support for virtually any open programming languageimage:: docs/figures/lara-django-menu.pngOverview of the packageslara-django [core]   - core of the lara-django project (required)lara-people [core]   - management of all users/institutions/addresseslara-projects [core] - management of all projects and experimentslara-data [core]     - all data goes herelara-metainfo [core] - all kind of meta information goes herelara-substances [optional] - chemicals, substances, DNA, proteinslara-devices    [optional] - lab devicesInstallation of lara-djangoThe simplest way to install lara-django and the rest of the core packages, simply executecd [lara-django repository directory]
python3 install-lara-django.pyThis will guide you through the complete setuplara-django django can be installed : plain and in a virtual python environment (venv).The recommended way to install lara-django is to install it in a virtualenv environment.AcknowledgementsThe LARA-django developers thankthe python teamthe R teamthe whole django team (https://www.djangoproject.com/) for their great tool !mptt developersthe blockly team (https://developers.google.com/blockly/andhttps://github.com/google/blockly) for their visual programming languageVladimir (Владимир) from pyinto (https://github.com/pyinto) for his inspirations on how to setup the ordering system and what you can do with css and JavaSkriptReferences
Package: laravel-queue
Laravel Queue for PythonCurrently:only supports readingonly supports queues in postgresCreate a Queue Objectfrom laravel_queue import Queue

connection_string = "postgresql://user:password@host:5432/db"
queue = Queue(connection_string)Queue Args:connection: str or sqlalchemy.engine.base.Engine
queue: str, default 'python', the queue name in the database
jobs_table: str, default 'jobs.jobs', the table name for the jobs
failed_jobs_table: str, default 'jobs.failed_jobs', the table name for the failed jobsRead Jobs from Queuejobs = queue.jobs  ## List of jobs in queue objectRunning Jobswhile queue.jobs:
    jobs = queue.jobs
    for job in jobs:
        job.run(function) ## run any functionfunction can be any function to run for this job
Using the while loop allows any new jobs placed on the queue while processing to be carried out next.Passing ParamsTo pass params in laravel to the python function, specify aparam_mapin the run function.# laravel:
$this->groupId = 12;

# python
def function(group_id):
    pass

job.run(function, param_map={
    'groupId': 'group_id'
})Additionally, you can specify acache_lock_uidThis can be either a str or a list of strings.
This is to be used for managing the job cache if you are using theShouldBeUniqueorShouldBeUniqueUntilProcessingproperties in Laravel
Thecache_lock_uidshould resemble what you have setuniqueId()to in laravel.
If a list is sent, you can specify parameters to be filled with$:# Laravel:
$this->param1 = 1
$this->param2 = 2

public function uniqueId(): string
    {
        return $this->param1 . '-' . $this->param2;
    }


# Python
job.run(function_name,
    param_map = {
        'param1': 'param1',
        'param2': param2'
    }
    cache_lock_uid = ['$param1', '-', '$param2'])

## $param1 and $param2 will be swapped in with the cooresponding value.To use cache locks, you must specify the database as your cache for queue. You can do this as so:public function uniqueVia(): Repository
    {
        return Cache::driver('database');
    }If using theShouldBeUniqueUntilProcessingproperty, you must specify that in you job runner as well.
By default (unique_until_processing=False), the cache lock will be relased after processing, emulating theShouldBeUniquelaravel property.
Setting thisunique_until_processing=Truewill relase this lock at the start of the job, likeShouldBeUniqueUntilProcessing
Package: laravel-versions-sdk
Laravel Versions PythonAn SDK to easily work with theLaravel Versions APIInstallVia Pippipinstalllaravel-versions-sdkUsagefromlaravel_versionsimportLaravelVersionslaravel_versions=LaravelVersions()versions=laravel_versions.versions()version=laravel_versions.version(9)Available MethodsDescriptionlaravel_versions.versions()Retrieve aLaravelVersionsCollectiondict containing information for all versions.laravel_versions.version("version")Retrieve aLaravelVersionsSingulardict containing information for a specific version.Change logPlease seeCHANGELOGfor more information on what has changed recently.Testinghatchshell

hatchruntestSecurityIf you discover any security related issues, please emailsecurity@voke.devinstead of using the issue tracker.CreditsOwen VokeAll ContributorsLicenseThe MIT License (MIT). Please seeLicense Filefor more information.TreewareYou're free to use this package, but if it makes it to your production environment you are required to buy the world a tree.It’s now common knowledge that one of the best tools to tackle the climate crisis and keep our temperatures from rising above 1.5C is to plant trees. If you support this package and contribute to the Treeware forest you’ll be creating employment for local families and restoring wildlife habitats.You can buy treeshere.Read more about Treeware attreeware.earth.
Package: larc
LARC Core ToolsetCollection of utilities for use across LARC projects.Installationpip3 install larcHow to UseThelarclibrary provides a number of utilities:larc.common: Collection of general-purpose functions and types,
patterned after thetoolzfunctional programming librarylarc.yaml: A few simple wrapper functions aroundruamel.yamlthat provides a standard interface for reading/writing YAML fileslarc.markdown: Collection ofPython MarkdownextensionsMetaYamlExtension(meta_yaml): A slight tweak to theMeta-Data extensionfor providing YAML metadata at the beginning of a markdown
fileSimpleTableExtension(simpletable): A<table>-parsing
extension for markdown that allows you to provide CSS classes
for table elements within the markdownYamlDataExtension(yaml_data): A more general YAML-parsing
extension that allows you to provide chunks of YAML data
throughout the markdown file (not just at the beginning)larc.rest: A ReST client-building tool that attempts to be more
functionallarc.logging: Some logging utility functions that relies oncoloredlogsfor log coloringlarc.parallel: Some functional parallelization utility functions
designed for use within thetoolz-ish functional idiomlarc.signature: Functions to construct a host signature, for use
when "fingerprinting" clients is necessarylarc.shell: Shell command functionsThe library also provides the following command-line tools:diffips: Given two files with IPs (A and B), get difference A - Bintips: Given two files with IPs (A and B), get intersection A & Bdifflines: Given two files with lines of text (A and B), get
difference A - Bintlines: Given two files with lines of text (A and B), get
intersection A & Bsortips: Given text content (from clipboard, file, or stdin),
extract IPs sort themgetips: Given text content (from clipboard, file, or stdin),
extract IPs and print them