Saravanan666/pypi-org huggingface dataset:
Package: numbakit-ode
numbakit-ode: leveraging numba to speed up ODE integrationnumbakit-ode (nbkode) is a Python package to solveordinary differential equations (ODE)that usesNumbato compile code and therefore speed up calculations.The API is very similar to scipy’s integrate module therefore
allowing for easy migration.It runs in Python 3.7+ depending onNumPy,SciPyandNumba.
It is licensed under BSD.It is extremely easy and natural to use:>>>importnbkode>>>deffunc(t,y):...return-0.1*y>>>t0=0.>>>y0=1.>>>solver=nbkode.ForwardEuler(func,t0,y0)>>>ts,ys=solver.run([0.,5.,10.])You can get a list of all solvers:>>>importnbkode>>>nbkode.get_solvers()#doctest: +SKIPor filter by characteristics or group name (or names).>>>nbkode.get_solvers(implicit=False,fixed_step=True)#doctest: +SKIP>>>nbkode.get_solvers('euler','adam-bashforth')#doctest: +SKIPQuick InstallationTo install numbakit-ode, simply (soon):$pipinstallnumbakit-odeor utilizing conda, with the conda-forge channel (soon):$condainstall-cconda-forgenumbakit-odeand then simply enjoy it!Design principlesFast: We loveNumba. It allows you to write clean Python code
that translates to optimized machine code at runtime. We aim to
be able to leverage this power to solve a system of ordinary
differential equations.Simple but useful API: Solvers are classes easy to instantiate,
with sensible defaults and convenient methods.Correctness: We check against established libraries likeSciPythat our implementation match those of established libraries using
automated testing.Data driven development: We take decisions based on data, and for this
purpose we measure the performance of each part of the package, and the effect
of each change we make.numbakit-ode is maintained by a community. SeeAUTHORSfor a complete list.To review an ordered list of notable changes for each version of a project,
seeCHANGES
Package: numba-mpi
numba-mpiNumba @njittable MPI wrapperscovering:size/rank,send/recv,allreduce,bcast,scatter/gather&allgather,barrierandwtimebasic asynchronous communication withisend/irecv(only for contiguous arrays); for request handling includingwait/waitall/waitanyandtest/testall/testanynot yet implemented: support for non-default communicators, ...API based on NumPy and supporting numeric and character datatypesauto-generated docstring-based API docs on the web:https://numba-mpi.github.io/numba-mpipure-Python implementation with packages available atPyPI,Conda Forgeand forArch LinuxCI-tested on: Linux (MPICH,OpenMPI&Intel MPI), macOS (MPICH&OpenMPI) and Windows (MS MPI)Hello world example:importnumba,numba_mpi,numpy@numba.njit()defhello():print(numba_mpi.rank())print(numba_mpi.size())src=numpy.array([1.,2.,3.,4.,5.])dst_tst=numpy.empty_like(src)ifnumba_mpi.rank()==0:numba_mpi.send(src,dest=1,tag=11)elifnumba_mpi.rank()==1:numba_mpi.recv(dst_tst,source=0,tag=11)hello()For information on MPI, see:https://www.mpi-forum.org/docs/https://mpi4py.readthedocs.io/en/stable/
Package: numba-progress
Numba-progressA progress bar implementation for numba functions using tqdm.
The module provides the classProgressBarthat works as a wrapper around thetqdm.tqdmprogress bar.It works by spawning a separate thread that updates thetqdmprogress bar
based on an atomic counter which can be accessed within a numba nopython function.The progress bar works with parallel as well as sequential numba functions.InstallationUsing pippip install numba-progressFrom sourcegit clone https://github.com/mortacious/numba-progress.git
cd numba-progress
python setup.py installUsagefromnumbaimportnjitfromnumba_progressimportProgressBarnum_iterations=100@njit(nogil=True)defnumba_function(num_iterations,progress_proxy):foriinrange(num_iterations):#<DO CUSTOM WORK HERE>progress_proxy.update(1)withProgressBar(total=num_iterations)asprogress:numba_function(num_iterations,progress)TheProgressBaralso works within parallel functions out of the box.fromnumbaimportnjit,prangefromnumba_progressimportProgressBarnum_iterations=100@njit(nogil=True,parallel=True)defnumba_function(num_iterations,progress_proxy):foriinprange(num_iterations):#<DO CUSTOM WORK HERE>progress_proxy.update(1)withProgressBar(total=num_iterations)asprogress:numba_function(num_iterations,progress)See also theexamplesfolder for more usage examples.
Package: numba-quaternion
Date:March 20, 2021ContentsIntroductionOther Python quaternion projectsdocstestspackageconda-forgeIntroductionThis package contains some numba-jit-compiled functions that perform Quaternion operations and a convenient classQuaternionthat provide convenient methods wrapping around those functions.Quaternionbehaves like a Numpy array containing quaternion, e.g. respect Numpy broadcast operations, but without really imitating anumpy.ndarrayand implemented adtype.This design allows you to write any jit-compiled functions involving those provided jit-compiled functions, and then write your own class methods that calls those functions as a convenient interface (by class inheritance.)If you do not care to use Quaternion in other jit-compiled functions you write, check out packages below instead.Other Python quaternion projectsOther Python projects that implements quaternions and I knew and used are:zonca/quaternionarray: written in pure Python using Numpy. Note that unusually they put the real part in the last column.lastcol_quat_to_canonicalandcanonical_quat_to_lastcolconvert between those and the canonical ordering (where real part comes first.)hpc4cmb/toast: toast.qarray is a reimplementation of the above quaternionarray package in C++ with the same interface, and following the same convention.moble/quaternion: implement Quaternion as a Numpy dtype in C.moble/quaternionic: implement Quaternion as a Numpy dtype using Numba. This package is inspired by my expectation of quaternionic—I expected I could use them in a Numba-jit-compiled function but it doesn’t.
Package: numbars
numbars
(the very useless but also cool python library)This python library allows you to add, subtract, multiply, divide, and randomize numbers.
- This is more so just a test to see how Python libraries work, and how to upload them.
- If you want to use it feel free, but it’s kinda useless. XD[For documentation, visit here.](https://github.com/zelofi/numbars/wiki)Change Log1.0.3 (8/28/2023)Added documentation page to READMEChanged “random_numbers()” to “randomize_numbers()”1.0.2 (8/26/2023)Fixed up the CHANGELOG.txt file1.0.1 (8/26/2023)Fixed up the README.txt file1.0.0 (8/26/2023)Release of numbars
Package: numba-scipy
Numba + SciPy = numba-scipynumba-scipyextends Numba to make it aware of SciPy. Numba is an open
source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc.
It uses the LLVM compiler project to generate machine code from Python syntax.For more information about Numba, see the Numba homepage:http://numba.pydata.orgFor more information about numba-scipy see:https://numba-scipy.readthedocs.io/en/latest/index.htmlDependenciesnumba
Package: numba-smartjit
numba-smartjitIntrosmartjit@jitdecorator adds extra customization of when code execution should fall back to the interpreter. It works as follow:For jitted functions with cache (overloads), use the jitted function if available, and interpreted code otherwiseAdd adispatchinglogic, an optional function to pass to the jit decorator, which will decide wether to use jit or not.Installnumba-smartjit is available on PyPI and can be installed with the command below:pipinstallnumba-smartjitHow to use ithowto.md
Package: numba-special
Numba SpecialNumba special allows SciPy's special functions to be used in
Numba.How to use it>>>importnumba>>>importscipy.specialassc>>>importnumba_special# The import generates Numba overloads for special>>>@numba.njit...defgamma_plus_1(x):...returnsc.gamma(x)+1.0...>>>gamma_plus_1(5.0)25.0For detailed information on which functions can be used from Numba,
check thedocumentation.InstallingNumba special depends on SciPy and Numba. Until the fix forthis issueis
included in a Numba release,numba_specialmust be installed
against the master branch of Numba.
Package: numbat
NumbatNumbat is an API to create and manipulate Sourcetrail databases.Sourcetrailis a code source
explorer which allows users to navigate through the different components (functions, classes, types, etc.) easily.Numbat main goal is to offer a full-python SDK given the fact that the current one, SourcetrailDB cannot be used anymore efficiently. It is not user-friendly at first sight, need to be compiled to provide Python bindings and, moreover, it is no longer maintained. Finally, we have added some little new features as finding an element in the database.With Numbat, you will be able to visualize your data quickly with the nice graphical Sourcetrail interface. For example,Pyrrhauses Numbat to map firmware structure.InstallationNumbat is available onpypi.pipinstallnumbatFrom sourcesYou can also install it from thegitrepository. Either using the following oneliner:pipinstall'numbat @ git+https://github.com/quarkslab/numbat'or doing it in few steps:# Download the repogitclonehttps://gitlab.qb/firmware-re/cartography/numbatcdnumbat# Install numbat locallypipinstall.Build DocumentationIf you want to build the documentation by first installing Numbat with the required[doc]dependencies and then serve the documentation on a local server.# if you already have a local clone of the projectcdNUMBAT_DIR
pipinstall.[doc]# otherwisepipinstall'numbat[doc]'# serve doc locallymkdocsserveBasic UsageA complete usage with examples is available in thedocumentationbut here is a quick usage to begin with Numbat.To use Numbat, you must first create aSourcetrailDBobject and either create a new database or open an existing one:frompathlibimportPathfromnumbatimportSourcetrailDBdb_path=Path('my_database')try:# Create a new databasesrctrl=SourcetrailDB.create(db_path)exceptFileExistsError:# Database already exists, open it and remove the entire content with clearsrctrl=SourcetrailDB.open(db_path)srctrl.clear()You could also use this one-liner to open the database quicker:frompathlibimportPathfromnumbatimportSourcetrailDBsrctrl=SourcetrailDB.open(Path('my_database'),clear=True)Once the database is open, you can add one or more source files in the database using the following snippet:file_id=srctrl.record_file(Path('my_file.c'))srctrl.record_file_language(file_id,'C')To populate the database, it is possible to record different symbols such as class or function like this:symbol_id=srctrl.record_class(name="MyClass",is_indexed=True)# if you have a source code, you can add the location of your symbol definitionsrctrl.record_symbol_location(symbol_id,file_id,2,7,2,12)# and your symbol scopesrctrl.record_symbol_scope_location(symbol_id,file_id,2,1,7,1)Once the database is filled with information, it must be saved and close like this:srctrl.commit()srctrl.close()AuthorsSami Babigeon (@sbabigeon), QuarkslabEloïse Brocas (@ebrocas), QuarkslabThe logo is a creation of Benoît Forgette and Sami Babigeon.
Package: numba-timer
Numba GPU TimerA helper package to easily time Numba CUDA GPU events.CompatibilityAs this package uses Numba, refer to theNumba compatibility guide.InstallationUsing Pip:pip3 install numba_timer.Exampleimport math
import numpy as np
from numba import cuda
from numba_timer import cuda_timer

@cuda.jit
def increment_a_2D_array(an_array):
    x, y = cuda.grid(2)
    if x < an_array.shape[0] and y < an_array.shape[1]:
       an_array[x, y] += 1

an_array = np.zeros((2, 100))
threadsperblock = (16, 16)
blockspergrid_x = math.ceil(an_array.shape[0] / threadsperblock[0])
blockspergrid_y = math.ceil(an_array.shape[1] / threadsperblock[1])
blockspergrid = (blockspergrid_x, blockspergrid_y)

timer = cuda_timer.Timer()

timer.start()
increment_a_2D_array[blockspergrid, threadsperblock](an_array)
timer.stop()

print(f'Elapsed time for run 1: {timer.elapsed()} ms')

timer.start()
increment_a_2D_array[blockspergrid, threadsperblock](an_array)
timer.stop()

print(f'Elapsed time for run 2: {timer.elapsed()} ms')Numba specific code is borrowed from theNumba documentation.
Package: numbercalculator
Hi , I am Chayan and I have created this simple libary that can calculate numbers . Hope this libary will help youHow to use it :
for adding - print(add(number1,number2))
for subtract - print(subtract(number1,number2))
for multiply - print(multiply(number1, number2))
for divide - print(number1, number2)Change Log0.0.1 (21/01/2021)First Release
Package: numberconverter
## [BDT Decimal to Word Converter](https://pypi.org/project/engtobngnum/)## Prerequisites* python 3x* pip3## Installation**BEFORE YOU INSTALL:** please ensure the [prerequisites](#prerequisites)```bashpip install numberconverter```## Usage### import NumberConverter from numberconverter```bashfrom numberconverter.NumberConverter import Converter```### make object of engtobngnum```bashconverter = Converter()```### call the converter() function with your value```bashconverter.convert_bng_to_eng('০২/০৬/২০১৮')orconverter.convert_eng_to_bng(8.89)```## Contact with [Author](https://www.fb.com/mahfuzcmt)
Package: number-count
BinanceSignalBotBinance Account Opening :https://accounts.binance.com/en/register?ref=35023868Why I making this :I know if Axiinfinity goes up then slp will rise up to and it will fall after axi this for the dynamix of relevent cripto.When Cardano perform well Agix will goes up also bechouse Agix is build on top of Cardano smart contract.When cripto start banded Private coin like monero will goes up bechouse people will go for darknet for buying things.I know when elom mask twit about cripto it will goes up.fact like that are strategy and we want to trun this into strategy.Hage Fund Club :
If you are in as a investor you will got all the profitable strategy without knowing what they are.
You will get the chance to learn what they are.We are pulling the profitable stocks but before that a human have to conform this project itself is nice project.We have to to find the full moon and new moon have any effect on price movement or not.We Can implement This in copy trade market like ptime x bt for collecting anonymous capital.
Package: numberdata
numberdata DocumentationInstallationnumberdatalets you perform basic operations on numberspipinstallnumberdataUsageimportnumberdataCalculateSquareRootof a numberoutput=numberdata.calculateSquareRoot(64)print(output)# 8CalculateSquareof a numberoutput=numberdata.calculateSquare(7)print(output)# 49CalculateCubeof a numberoutput=numberdata.calculateCube(3)print(output)# 27CalculatePowerof a numberoutput=numberdata.calculatePower(2,4))print(output)# 16Powercan also be calculated ofnon-integernumbers.# Calculate Power of a numberprint(numberdata.calculatePower(125,1/3))# 5
Package: numbereddoor-game
# Numbered Door GameNumbered Door Game is a reimplementation of the door puzzles from Zero Escape: 999. This version allows the player to choose what numbers are used on the door while leaving the others behind. The player must progress through all 4 stages to win.STILL A WORK IN PROGRESS## Usage```pip install numbereddoor-game```## Controls#### Mouse- Use the mouse to highlight elements, numbers and doors.- Use Left MOUSE CLICK to select numbers and add to selected door, or switch between active door.## How to Play- The goal of the game is to add the available numbers together in order to get the digital root of the door.```A digital root is the single digit value obtained by recursively summing digits, on each iterationusing the result from the previous iteration to compute a digit sum. The process continues until asingle-digit number is reached.ExampleDoor value        = 9Numbers available = _ _ 3 _ _ 6 _ _ 9calculate)3 + 6 + 9 = 18Add all the digits together to get 1818 = 1 + 8 = 918 is not a digit so we have to add the digits that make up this number in orderto reduce the result to a its DIGITAL ROOT. The digits 1 and 8 make up 18 so weadd those digits together resulting in 9. 9 is a digit so there is no need toreduce the result further.- OR -calculate)3 + 9 = 12We are given 3 digits: 1, 6, 9, so lets start with a different approach thanabove. Adding two digits, 3 and 9, resulting in 12.12 = 1 + 2 = 312 is not a digit so we need to reduce this number to its digital root. We have1 and 2 which when added together result in 3.3 + 6 = 9We still have the digit 6 that needs to be added to the answer, so we add 6 tothe result from the digital root of 3 + 9 which was 3. Adding 3 and 6 results in9 with 9 being a digit so we have found our digital root and solution to the problem.Its also interesting to note that 9 never changes the digital root of a problem.```- You must select a door for a ball to go to before it moves.- The player can only use each provided number once, and the player has to use 0 or 3-5 numbers for a door's solution. The solution will be counted as invalid otherwise.- After a stage is completed, all numbers used to get through the door(s) from the previous stage will then be available for the next stage rendering the unused numbers unavailable for the rest of the game.- Only one door has to be solved to proceed to the next stage, but this will hinder the player's progression and may stop them from winning at all.- In the HARD difficulty you must use all the available digits on the door.## Acknowledge- Spike Chunsoft
Package: numberify
Add line number to file, create a numbered dictionary object of a list and import numbered dictionary from a file list.InstallationThe script isavailable on PyPI. To install with pip:pip install numberifyUsageTo use the script in command line:numberify <filename>To use as a python package:>> from numberify.numberify import Numberify
>> nfy = Numberify()
>> nfy.numberify_data(['foo', 'bar'])
{1: 'foo', 2: 'bar'}
>> nfy.numberify_data(['foo', 'bar'], start=55)
{55: 'foo', 56: 'bar'}Let a file afile.txt contain:milk
potatoes
biscuits
sugarImport the file data numberifies as:...
>> nfy.numberify_data('afile.txt')
{1: 'milk', 2: 'potatoes', 3: 'biscuits', 4: 'sugar'}
>> nfy.numberify_data('afile.txt', start=15)
{15: 'milk', 16: 'potatoes', 17: 'biscuits', 18: 'sugar'}To numberify the whole file (apply changes to the file):...
>> nfy.numberify_file('filename')TestsRun test:python setup.py nosetestsDocsFor detailed docs of the package use python’s help utility.LicenseThis project is released under aGPL License.
Package: numbering2pdf
numbering2pdf adds numbering to pdf fileExample:fromnumbering2pdfimportadd_numbering_to_pdfadd_numbering_to_pdf("old_file.pdf","new_file.pdf")Parameters:pdf_file - string or bytesadd_numbering_to_pdf("old_file.pdf","new_file.pdf")file=open("new.pdf","br")read_file=file.read()file.close()add_numbering_to_pdf(read_file,"new_file.pdf")new_file=add_numbering_to_pdf("old_file.pdf")new_pdf_file_path - path to new pdf fileadd_numbering_to_pdf("old_file.pdf","test/new_file.pdf")position - position on screen (left, center, right)add_numbering_to_pdf("old_file.pdf","new_file.pdf",position="left")add_numbering_to_pdf("old_file.pdf","new_file.pdf",position="center")add_numbering_to_pdf("old_file.pdf","new_file.pdf",position="right")start_page - from which page number will the numbering be addedadd_numbering_to_pdf("old_file.pdf","new_file.pdf",start_page=3)end_page - on which page number will the numbering be completedadd_numbering_to_pdf("old_file.pdf","new_file.pdf",end_page=10)start_index - which number will the page numbering start fromadd_numbering_to_pdf("old_file.pdf","new_file.pdf",start_index=4)size - number sizeadd_numbering_to_pdf("old_file.pdf","new_file.pdf",size=25)font - font typeCourier, Courier-Bold, Courier-Oblique, Courier-BoldOblique,
Helvetica, Helvetica-Bold, Helvetica-Oblique, Helvetica-BoldOblique,
Times-Roman, Times-Bold, Times-Italic, Times-BoldItalic,
Symbol, ZapfDingbatsadd_numbering_to_pdf("old_file.pdf","new_file.pdf",font="Courier")
Package: numberize
Short InfoReplace numerals with corresponding numbers in the given text.
Available onPyPI.
Consider checking outWikifor more info.Supported languagesUkrainianRussianEnglishRequirementspython 3.6+NLTK DataHow to install it?pip install numberizeHow to install requirements?After installing this packagepython -m nltk.downloader punktHow to use it?import numberize
numberizer = numberize.Numberizer(lang='ru')    #or 'uk', 'en'
new_text = numberizer.replace_numerals(old_text)
Package: numbername
Installationpip install numbernameUsageConvert integers to number namefrom numbername import to_number_name
print to_number_name(1123)
#ouputs: one thousand one hundred and twenty threeConvert integers to comma placed numberfrom numbername import to_comma_placed
print to_number_name(1123)
#outputs: 1,123LimitationsWorks only with non-negative numbersWorks only with integers
Package: number-names
Number Namesnumber_namesconverts numbers to their English names.The main useful function this package provides is calledname. Usage
is as follows:>>>importnumber_names>>>number_names.name(0)'zero'>>>number_names.name(1)'one'>>>number_names.name(2)'two'>>>number_names.name(123_456_789)'one hundred and twenty three million four hundred and fifty six thousand seven hundred and eighty nine'>>>number_names.name(3.1415)'three point one four one five'>>>number_names.name(-10**15)'minus one quadrillion'>>>number_names.name(10**100000)'ten tretriginmilliatrecenduotrigintillion'>>>number_names.name(3+2j)'three plus two i'>>>fromfractionsimportFraction>>>number_names.name(Fraction(1,2))'one half'>>>number_names.name(Fraction(3,221))'three two hundred and twenty firsts'
Package: numberpartitioning
Partition problem solvers in PythonThis repository includes an implementation of theKarmarkar--Karp algorithm(also known as the largest differencing method) for themultiway number partitioning optimization problem, as well as some greedy algorithms.The problemConcretely, the problem we solve is the following: SupposeSis some collection of integers, andkis some positive integer, find a partition ofSintokparts so that the sums of the integers in each part are as close as possible.The objective function describing "closeness" is usually taken to be the difference between the largest and smallest sum among all parts. The optimization version is NP-hard, and the bundled algorithm only aims to provide a good solution in short time. This also means that they can be useful for other objective functions such as, say, the variance of all sums.InstallationThe package is available fromPyPI:pipinstallnumberpartitioningIt can also be obtained fromconda-forge:mambainstall-cconda-forgenumberpartitioningExamplesSuppose we want to split the collection[4, 6, 7, 5, 8]into three parts. We can achieve that as follows:fromnumberpartitioningimportkarmarkar_karpnumbers=[4,6,7,5,8]result=karmarkar_karp(numbers,num_parts=3)Here,result.partitionbecomes[[8], [4, 7], [5, 6]], andresult.sizesare the sums of each part,[8, 11, 11]. This happens to be optimal.As notedon Wikipedia, an example where this approach does not give the optimal result is the following:fromnumberpartitioningimportkarmarkar_karpnumbers=[5,5,5,4,4,3,3,1]result=karmarkar_karp(numbers,num_parts=3)Here,result.sizesis[9, 10, 11]but it is possible to achieve a solution in which the sums of each part is 10.
Package: number-precision
NumberPrecisionPerform addition, subtraction, multiplication and division operations precisely using pythonWhy0.1+0.2=0.300000000000000041.0-0.9=0.09999999999999998Installpipinstallnumber_precisionMethodsNP.plus(num1,num2,num3,...)//addition,num+num2+num3,twonumbersisrequiredatleast.NP.minus(num1,num2,num3,...)//subtraction,num1-num2-num3NP.times(num1,num2,num3,...)//multiplication,num1*num2*num3NP.divide(num1,num2,num3,...)//division,num1/num2/num3NP.round(num,ratio)//roundanumberbasedonratioUsagefromnumber_precisionimportNPNP.plus(0.1,0.2)//=0.3,not0.30000000000000004NP.plus(2.3,2.4)//=4.7,not4.699999999999999NP.minus(1.0,0.9)//=0.1,not0.09999999999999998NP.times(3,0.3)//=0.9,not0.8999999999999999NP.times(0.362,100)//=36.2,not36.199999999999996NP.divide(1.21,1.1)//=1.1,not1.0999999999999999NP.round(0.105,2)//=0.11,not0.1NP.plus(0.1,0.005,round=2)//=0.11,not0.1LicenseMIT