Saravanan666/pypi-org huggingface dataset:
Package: nullutil
#+author: conao3#+date: <2020-03-20 Fri>[[https://github.com/conao3/nullutil][https://raw.githubusercontent.com/conao3/files/master/blob/headers/png/nullutil.png]][[https://github.com/conao3/nullutil/blob/master/LICENSE][https://img.shields.io/github/license/conao3/nullutil.svg?style=flat-square]][[https://github.com/conao3/nullutil/releases][https://img.shields.io/github/tag/conao3/nullutil.svg?style=flat-square]][[https://github.com/conao3/nullutil/actions][https://img.shields.io/badge/patreon-become%20a%20patron-orange.svg?logo=patreon&style=flat-square]][[https://twitter.com/conao_3][https://img.shields.io/badge/twitter-@conao__3-blue.svg?logo=twitter&style=flat-square]][[https://conao3-support.slack.com/join/shared_invite/enQtNjUzMDMxODcyMjE1LWUwMjhiNTU3Yjk3ODIwNzAxMTgwOTkxNmJiN2M4OTZkMWY0NjI4ZTg4MTVlNzcwNDY2ZjVjYmRiZmJjZDU4MDE][https://img.shields.io/badge/chat-on_slack-blue.svg?logo=slack&style=flat-square]]* Table of Contents- [[#description][Description]]- [[#install][Install]]- [[#usage][Usage]]- [[#information][Information]]- [[#community][Community]]- [[#contribution][Contribution]]- [[#license][License]]- [[#author][Author]]* DescriptionPython nullutil.* Install* Usage* Information** CommunityAll feedback and suggestions are welcome!You can use github issues, but you can also use [[https://conao3-support.slack.com/join/shared_invite/enQtNjUzMDMxODcyMjE1LWUwMjhiNTU3Yjk3ODIwNzAxMTgwOTkxNmJiN2M4OTZkMWY0NjI4ZTg4MTVlNzcwNDY2ZjVjYmRiZmJjZDU4MDE][Slack]]if you want a more casual conversation.** ContributionWe welcome PR!** License#+begin_exampleMIT LicenseCopyright (c) Naoya Yamashita - https://conao3.comhttps://github.com/conao3/nullutil/blob/master/LICENSE#+end_example** Author- Naoya Yamashita ([[https://github.com/conao3][conao3]])
Package: num2azerbaijani
num2azerbaijaniLightweight python package to convert numbers into azerbaijani stringInstallationpipinstallnum2azerbaijaniUsagefromnum2azerbaijaniimportconvertasnum2aze# import moduleprint(num2aze(1235))# min iki yüz otuz beşprint(num2aze('8135919'))# səkkiz milyon yüz otuz beş min doqquz yüz on doqquz
Package: num2chinese
num2chineseA nlp tool to transform numbers to Chinesenum2chineseuses regular expression to parse alphanumeric literals and transform them into readable Chinese charaters.Why it mattersChinese's pronuncication has lots of exceptions.For Chinese numbers, a character is uttered dependent of context.Lots of rules are required to handle messy Chinese number pronunciation. Dont' reinvent the wheel!Examples$120 : 美金一百二十200塊 : 兩百塊12121212個蘋果 : 一千兩百一十二萬一千兩百一十二個蘋果2002002支 : 兩百萬兩千零二支9487 : 九四八七080080123 : 零八零零八零一二Usagetext = '12121212個蘋果''
normalizer = Normalizer()
text_normalized = normalizer.normalize(text)
print(text_normalized)
# result is '一千兩百萬十二萬一千兩百一十二個蘋果'Installationpip install num2chineseRequirementspython>=3.6,<4.0LicenseMIT license
Package: num2cyrillic
num2cyrillicWith num2cyrillic.NumberToWords() Python3 class you can convert an integer number
to simple Bulgarian cyrillic words. This can be useful when you need to spell a currency
value e.g. on an invoice.This is a Python3 porting of the excellent PEAR::Numbers_Words class byKouber Saparevfor PHP.All credit goes to him, this is simply an implementation of the same logic in Python.Installationpip install num2cyrillicUsagefromnum2cyrillicimportNumberToWordsnum2bg=NumberToWords()num2bg.cyrillic(3)num2bg.cyrillic(67)# три# шестдесет и седем(C) Copyright 2018 ClaimCompass, Inc.
Package: num2es
Examples# Python 2from random import randintfrom num2es import TextNumberwhile True:n = TextNumber(randint(0, 999999999999))print (n.number)print (n.nice_repr())print unicode(n)raw_input()# Python 3from random import randintfrom num2es import TextNumberwhile True:n = TextNumber(randint(0, 999999999999))print (n.number)print (n.nice_repr())print (n)input()Output:464315924464.315.924cuatrocientos sesenta y cuatro millones trescientos quince mil novecientosveinticuatro411738837411.738.837cuatrocientos once millones setecientos treinta y ocho mil ochocientos treintay siete244850467244.850.467doscientos cuarenta y cuatro millones ochocientos cincuenta mil cuatrocientossesenta y siete
Package: num2fa
Num2Fanum2fais a versatile solution that enables the conversion of numbers (integers, floats, decimals, fractions, or strings) into their corresponding number or word form in Persian.This repo is built upon the foundation ofnum2faword, and I would like to express my gratitude to theoriginal developerfor their contributions.Installationpipinstallnum2faThat's it!UsageYou can usenumbers,words, andordinal_wordsto convert numbers to their respective Persian forms, whether that be in numeric, word, or ordinal form.>>>fromnum2faimportnumbers,words,ordinal_words>>>numbers(1984)'۱۹۸۴'>>>numbers('1984')'۱۹۸۴'>>>numbers('1.1e-4')>>>words(1984)'یک هزار و نهصد و هشتاد و چهار'>>>ordinal_words(1232)'یک هزار و دویست و سی و دوم'>>>ordinal_words(123)'یکصد و بیست و سوم'>>>words(1.1e-9)'یک و یک دهم در ده به توان منفی نه'numbersandwordsalso accepts other common standard types:>>>fromdecimalimportDecimal>>>fromfractionsimportFraction>>>numbers(Decimal('1.1'))'۱٫۱'>>>numbers(Fraction(-2,5))'-۲/۵'>>>words(Decimal('1.1'))'یک و یک دهم'>>>words(Fraction(-2,5))'منفی دو پنجم'>>>ordinal_words(123)'یکصد و بیست و سوم'CustomizationThe default decimal separator fornumbersis٫and forwordsisو. it can be changed to any other strings withdecimal_separator:>>>numbers(19.75,decimal_separator='/')'۱۹/۷۵'>>>words(19.75,decimal_separator=' ممیز ')'نوزده ممیز هفتاد و پنج صدم'If you wanted to use different number characters for example٦instead of۶, you can just:fromnum2fa.constantsimportPERSIAN_DIGITSPERSIAN_DIGITS.update({'6':'٦'})numbers(19.66,decimal_separator='/')'۱۹/٦٦'Some people prefer, for example, "صد و هفتاد" over its other form "یکصد و هفتاد". This package uses the second form by default which is also used on official Iranian banknotes. But it can be changed:>>>fromnum2fa.constantsimportHUNDREDS>>>words(170)'یکصد و هفتاد'>>>HUNDREDS[1]='صد'>>>words(170)'صد و هفتاد'other customizations inwords:>>>words(7,positive='مثبت ')'مثبت هفت'>>>words(-2,negative='منهای ')'منهای دو'>>>words('۱/۲')'یک دوم'>>>words('1/2',fraction_separator=' تقسیم بر ',ordinal_denominator=False)'یک تقسیم بر دو'>>>words(1.1e-9)'یک و یک دهم در ده به توان منفی نه'>>>words(1.1e-9,scientific_separator=' ضربدر ده به قوهٔ ')'یک و یک دهم ضربدر ده به قوهٔ منفی نه'positive,negative,decimal_separator,fraction_separatorcan be used innumberstoo.All above arguments can be used together. If you prefer to change the default argument values once and for all, use thechange_defaults_numbersorchange_defaults_wordsfunction:>>>fromnum2faimportchange_numbers_defaults,change_words_defaults>>>change_numbers_defaults(fraction_separator=' بر ',decimal_separator='.')>>>numbers('1.89/23')>>>'۱.۸۹ بر ۲۳'>>>change_words_defaults(fraction_separator=' بخش بر ',ordinal_denominator=False)>>>words('۱/۴')'یک بخش بر چهار'ContributingWe welcome contributions! To learn how you can contribute, please check theContributingdocument.LicenseThis work is licensed under the terms of theGNU Affero General Public License (AGPL).
Package: num2fawords
num2fawordsnum2fawordsis a highly customizable library which provides functions to convert a number (int, float, Decimal, Fraction, or str) into Persian word form.InstallationPython 3.3+ is requiredpip install'setuptools>=36.2.1'pip install num2fawordsUsage>>>fromnum2fawordsimportwords,ordinal_words>>>words(1984)'یک هزار و نهصد و هشتاد و چهار'>>>ordinal_words(1232)'یک هزار و دویست و سی و دوم'>>>ordinal_words(123)'یکصد و بیست و سوم'>>>words(1.1e-9)'یک و یک دهم در ده به توان منفی نه'Obviously,wordsis used to convert to word form andordinal_wordsis for ordinal word from.wordsalso accepts other common standard types:>>>words(19.75)'نوزده و هفتاد و پنج صدم'>>>fromdecimalimportDecimal>>>words(Decimal('1.1'))'یک و یک دهم'>>>fromfractionsimportFraction>>>words(Fraction(-2,5))'منفی دو پنجم'The default decimal separator is “و” but it can be changed to “ممیز” (or any other string) as follows:>>>words(19.75,decimal_separator=' ممیز ')'نوزده ممیز هفتاد و پنج صدم'Some people prefer, for example, “صد و هفتاد” over its other form “یکصد و هفتاد”. This library uses the second form by default which is also used on official Iranian banknotes. But it can be changed:>>>fromnum2fawordsimportHUNDREDS>>>words(170)'یکصد و هفتاد'>>>HUNDREDS[1]='صد'>>>words(170)'صد و هفتاد'Some examples for other arguments ofwords:>>>words(7,positive='مثبت ')'مثبت هفت'>>>words(-2,negative='منهای ')'منهای دو'>>>words('۱/۲')'یک دوم'>>>words('1/2',fraction_separator=' تقسیم بر ',ordinal_denominator=False)'یک تقسیم بر دو'>>>words(1.1e-9)'یک و یک دهم در ده به توان منفی نه'>>>words(1.1e-9,scientific_separator=' ضربدر ده به قوهٔ ')'یک و یک دهم ضربدر ده به قوهٔ منفی نه'Of-course the above arguments can be used together.If you prefer to change the default argument values once and for all, use thechange_defaultsfunction:>>>fromnum2fawordsimportchange_defaults,words>>>change_defaults(fraction_separator=' بخش بر ',ordinal_denominator=False)>>>words('۱/۴')'یک بخش بر چهار'That’s all. Enjoy!
Package: num2rus
num2rus v 0.0.8Небольшой python пакет для преобразования чисела в текст с учетмо множественного числа.
A small package to convert float numbers to russian string.What's newbug fix for #4 issuebug fix for #2 issuebug fix for #1 issueunittests addedUsageInstall packagepip install num2rusImport itimportnum2rusnum2rus.converter(1.10)# один рубль десять копеек
Package: num2textilka
Num2Text Convertor░▒█▄░▒█░█░▒█░█▀▄▀█░░░█▀█░░░▀▀█▀▀░█▀▀░█░█░▀█▀░░░▀█▀░█░░█░▄░█▀▀▄
░▒█▒█▒█░█░▒█░█░▀░█░░░▒▄▀░░░░▒█░░░█▀▀░▄▀▄░░█░░░░▒█░░█░░█▀▄░█▄▄█
░▒█░░▀█░░▀▀▀░▀░░▒▀░░░█▄▄░░░░▒█░░░▀▀▀░▀░▀░░▀░░░░▄█▄░▀▀░▀░▀░▀░░▀A python program that converts Numbers to their Text Format.FeaturesEnglish and Persian words.
Supports Numbers from 0 to 10^105UsageBelow code will help you understand how to use this package:```
from number2textilka import num2text

num2text(123, "persian")
```

OR

use the following format if you like to get input from user.

```
from number2textilka import num2text

num2text()
```
Package: num2word
Num2WordThis module convert any number/digit to word
For e.g. if you pass a number '98498' then,
it will return 'Ninety Eight Thousand Four Hundred Ninety Eight'InstallationRun the following command to install:pip install num2wordUsage# Import word from num2word
 from num2word import word

 # Generate 'Nine Thousand eighty four'
 word('9284') or word(9284)

 #other examples
 <!-- you can use any bigger number -->
 a = 33221884729877832
 print(word(a)) /*It will print the output*/
 b = '99320489432099000982409203'
 b = word(b)
 <print(b)>/* will print out the the expected output */
Package: num2words-bydna
Num2WordsMyDNAThis is a simple numbers to word package. You can useMy GitHub Pageto ask your questions.num2word_mydna is a Python package for converting numbers to word sequences using custom short abbreviations.InstallationTo install num2words_mydna, simply use pip:UsageTo use Num2Word MyDNA, first import thenum2words_mydnamodule:Then, you can use thenum2words_mydnafunction to convert a number to word.
Package: num2wordshindi
num2wordshindiConvert number to hindi stringHow to Use:
convert number to stringengine = Num2wordshindi(num) # enter the number you want to change
converted_string = engine.to_words() # to convert in words
convert number to currency
converted_currency = engine.to_currency() # to converrt in indian currency (ruppes, paisa)Eg:engine = Num2wordshindi(3265.21)
words = engine.to_words()
curency = engine.to_currency()
print(words)
print(currency)
Package: numan
numan : Numerosity analysisNuman is a Python library for a project aiming to identify the neural correlates of numerosity
abilities in zebrafish. The broad project aims to test the hypothesis that the ability to represent numerosity has an evolutionarily conserved neural basis and to identify the cellular and molecular processes involved. In particular, using a 2P Light-Sheet microscope, we recorded the whole-brain GCaMP activity in zebrafish larvae in response to a change in the number of visual stimuli and we aim to find a set of neurons/activity patterns that is characteristic of a specific number presented to the fish.Numan contains only the analysis tools and relies onvodexfor data management.Schematic of the analysis pipeline used for numerosity-stimuli experiments. The image shows an experiment with two conditions: visual stimuli "2" and "5".InstallationUse the package managerpipto install numan.pipinstallnumanUsagePlease see notebooks/examples ongithub.ContributingPull requests are welcome, but for now it's only me working on this project, so it might take me some time. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: numan-plus
Example PackageThis package provides some additional functions to numan (https://github.com/LemonJust/numan) for analysis of calcium imaging experiments with numerical stimuli.
Specifically, new functions for cells segmentation with CaImAn and for computing number neurons with ANOVA followed by tuning curves creation are provided.Function new_report is adapted fromhttps://github.com/LemonJust/numanFunctions inside compute_anova and compute_tunings are adapted fromhttps://github.com/khalednasr/nn-numerosity-zero
Package: numaster
numasterExtract numbers from text, and normalize it.
For example,1,234.0is1 234,0in French, also1.234,0in German. Furthermore, if I say sometime number is written by Kanji in Japanese.Licensenumaster is availible under theGPLv3 license, see the LICENSE file for more information.Changes0.2.1Load version number from setup.py as__version__.0.2.0Normalize English number words.0.1.0You can path ignore words forextract.
Package: numat-chips
NuMat ChipsPython drivers for circuit board components. Intended for use with Raspberry Pis.Why?Many of the components supported here come with drivers. However, their code quality is mixed and many important chipset features can be missing or misconfigured. It's your chip and you should be able to use all of it.These drivers were built ground up from the appropriate datasheets, making sure that all useful features are exposed or set to sensible defaults. The code is concise and linted, and this library works as a dependency.Supported ChipsADS1115ADS8344MAX31855MAX31856MAX31865MCP3008MCP3202MCP3208Installationpip install numat-chipsUsageFor testing, you can call directly.importchipsadc=chips.ADS1115(channel=0)print(adc.read_voltage())For production, consider subclassing to provide space to document the circuit
and improve code reusability.classPiraniGauge(MCP3202):"""Reads from an Edwards APG100 vacuum pressure transmitter.The gauge is a 0-10V signal, split into a 0-3.3V range by three 10-kiloohmresistors."""defget(self):"""Returns the pressure reading, in torr."""return10**(3*self.read_voltage()-6.125)
Package: numato-cli
A command-line interface for Numato GPIO Expanders.Installpip install numato-clior even betterpipx install numato-cliWith pipx you can also ensure that thenumatoexecutable’s binary directory is in the PATH variable:pipx ensurepathUseDiscover all numato devices from a list of Linux/Windows devices:numato discoverDevelopBest practice is to usepoetryinstalled withpipx:pip install pipx
pipx install poetry
poetry shellIn the poetry shell you can also run an IDE like VSCode:code .Install the dependencies and run the Numato CLI:poetry install
numato --helpBuild packages:poetry build
Package: numba
A Just-In-Time Compiler for Numerical Functions in PythonNumba 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.Numba can compile a large subset of numerically-focused Python, including many
NumPy functions.  Additionally, Numba has support for automatic
parallelization of loops, generation of GPU-accelerated code, and creation of
ufuncs and C callbacks.For more information about Numba, see the Numba homepage:https://numba.pydata.organd the online documentation:https://numba.readthedocs.io/en/stable/index.htmlInstallationPlease follow the instructions:https://numba.readthedocs.io/en/stable/user/installing.htmlDemoPlease have a look and the demo notebooks via the mybinder service:https://mybinder.org/v2/gh/numba/numba-examples/master?filepath=notebooksContactNumba has a discourse forum for discussions:https://numba.discourse.group
Package: numba4jax
numba4jaxA small experimental python package allowing you to use numba-jitted functions from within jax
with no overhead.This package uses the CFFI of Numba to expose the C Function pointer of your compiled
function to XLA. It works both for CPU and GPU functions.This package exports a single decorator@njit4jax, which takes an argument, a function
or Tuple describing the output shape of the function itself.
See the brief example below.importjaximportjax.numpyasjnpfromnumba4jaximportShapedArray,njit4jaxdefcompute_type(*x):returnx[0]@njit4jax(compute_type)deftest(args):y,x,x2=argsy[:]=x[:]+1z=jnp.ones((1,2),dtype=float)jax.make_jaxpr(test)(z,z)print("output: ",test(z,z))print("output: ",jax.jit(test)(z,z))z=jnp.ones((2,3),dtype=float)print("output: ",jax.jit(test)(z,z))z=jnp.ones((1,3,1),dtype=float)print("output: ",jax.jit(test)(z,z))Backend supportThis package supports both the CPU and GPU backends of jax.
The GPU backend is only supported on linux, and is highly experimental.
It requires CUDA to be installed in a standard path.
CUDA is found throughnumba.cuda, so you should first check thatnumba.cudaworks.
Package: numba-dpex
Data-parallel Extension for Numba* (numba-dpex) is a standalone extension for
theNumbaPython JIT compiler. Numba-dpex provides
a generic kernel programming API and an offload feature that extends Numba's
auto-parallelizer to generate data-parallel kernels forparfornodes.Numba-dpex's kernel API has a design and API similar to Numba'scuda.jitmodule, but is based on theSYCLlanguage. The
code-generation for the kernel API currently supportsSPIR-V-basedOpenCLandoneAPI Level Zerodevices that are supported by Intel® DPC++ SYCL compiler runtime. Supported
devices include Intel&reg CPUs, integrated GPUs and discrete GPUs.The offload functionality in numba-dpex is based on Numba'sparforloop-parallelizer. Our compiler extends Numba'sparforfeature to generate
kernels and offload them to devices supported by DPC++ SYCL compiler runtime.
The offload functionality is supported via a new NumPy drop-in replacement
library:dpnpand NumPy-based expressions
andnumba.prangeloops are not offloaded.Refer thedocumentationand examples
to learn more.Getting StartedNumba-dpex is part of the Intel® Distribution of Python (IDP) and Intel®
oneAPI AIKit, and can be installed along with oneAPI. Additionally, we support
installing it from Anaconda cloud. Please refer the instructions
on ourdocumentation pagefor more details.Once the package is installed, a good starting point is to run the examples in
thenumba_dpex/examplesdirectory. The test suite may also be invoked as
follows:python-mpytest--pyargsnumba_dpex.testsCondaTo installnumba_dpexfrom the Intel(R) channel on Anaconda
cloud, use the following command:condainstallnumba-dpex-cintel-cconda-forgePipThenumba_dpexcan be installed usingpipobtaining wheel packages either from PyPi or from Intel(R) channel on Anaconda.
To installnumba_dpexwheel package from Intel(R) channel on Anaconda, run the following command:python-mpipinstall--index-urlhttps://pypi.anaconda.org/intel/simplenumba-dpexContributingPlease create an issue for feature requests and bug reports. You can also use
the GitHub Discussions feature for general questions.If you want to chat with the developers, join the#Data-Parallel-Python_communityroom on Gitter.im.Also refer ourCONTRIBUTINGpage.
Package: numba-inspector
numba-inspectorVisualize and Debug Numba compiled code in JupyterQuickstartInstallationInstall the package:pip install numba-inspector%%numba magic command%load_extnumba_inspector%%numba--bytecodefromnumbaimportnjit@njitdeffunc(x,y):ifx:x=x+1ify:y=y+1else:y=y-1else:x=x-1ify:y=y+1else:y=y-1returnx+yfunc(1,2)Install the cudatoolkit:conda install cudatoolkit%%numba--ptxfromnumbaimportcudaimportnumpyasnp@cuda.jit(lineinfo=True)defincrement_by_one(an_array):# Thread id in a 1D blocktx=cuda.threadIdx.x# Block id in a 1D gridty=cuda.blockIdx.x# Block width, i.e. number of threads per blockbw=cuda.blockDim.x# Compute flattened index inside the arraypos=tx+ty*bwifpos<an_array.size:# Check array boundariesan_array[pos]+=1a=np.arange(4096,dtype=np.float32)d_a=cuda.to_device(a)blocks=32threads=128increment_by_one[blocks,threads](d_a)cuda.synchronize()d_a.copy_to_host()
Package: numba-integrators
Numba IntegratorsNumba Integrators is collection numerical integrators based on the ones inSciPy. Aim is to make them faster and much more compatible withNumba.Table of ContentsQuick start guideThe first stepsInstallingImportingExampleExample of the advanced functionQuick start guideHere's how you can startThe first stepsInstallingInstall Numba Integrators with pippip install numba-integratorsImportingImport name is not the same as install name,numba-integrators.importnumba_integratorsExampleimportnumbaasnbimportnumba_integratorsasniimportnumpyasnp@nb.njit(nb.float64[:](nb.float64,nb.float64[:]))deff(t,y):'''Differential equation for sine wave'''returnnp.array((y[1],-y[0]))y0=np.array((0.,1.))solver=ni.RK45(f,0.0,y0,t_bound=1,atol=1e-8,rtol=1e-8)t=[]y=[]whileni.step(solver):t.append(solver.t)y.append(solver.y)print(t)print(y)Example of the advanced functionimportnumbaasnbimportnumba_integratorsasniimportnumpyasnp@nb.njitdeff(t,y,parameters):'''Differential equation for sine wave'''auxiliary=parameters[0]*y[1]dy=np.array((auxiliary,-y[0]))+parameters[1]returndy,auxiliaryt0=0.y0=np.array((0.,1.))parameters=(2.,np.array((-1.,1.)))# Numba type signaturesparameters_signature=nb.types.Tuple((nb.float64,nb.float64[:]))auxiliary_signature=nb.float64solver_type=ni.RK45Solver=ni.Advanced(parameters_signature,auxiliary_signature,solver_type)solver=Solver(f,t0,y0,parameters,t_bound=1,atol=1e-8,rtol=1e-8)t=[]y=[]auxiliary=[]whileni.step(solver):t.append(solver.t)y.append(solver.y)auxiliary.append(solver.auxiliary)print(t)print(y)print(auxiliary)Changelog0.2.2 2023-11-07Dev tools update0.2.1 2023-08-11Advanced mode solver to handle functions with parameters and auxiliary output0.1.2 2023-08-06Fixes0.1.1 2023-08-05Initial working version0.0.3 2023-05-14Inital working state
Package: numba-kdtree
Numba-kdtreeA simple KD-Tree for numba using a ctypes wrapper around the scipyckdtreeimplementation.
The KD-Tree is usable in both python and numba nopython functions.Once the query functions are compiled by numba, the implementation is just as fast as the original scipy version.Note: Currently only a basic subset of the originalckdtreeinterface is implemented.InstallationUsing pippip install numba-kdtreeFrom sourcegit clone https://github.com/mortacious/numba-kdtree.git
cd numba-kdtree
python setup.py installUsageimportnumpyasnpfromnumba_kdtreeimportKDTreedata=np.random.random(3_000_000).reshape(-1,3)kdtree=KDTree(data,leafsize=10)# query the nearest neighbors of the first 100 pointsdistances,indices=kdtree.query(data[:100],k=30)# query all points in a radius around the first 100 pointsindices=kdtree.query_radius(data[:100],r=0.5,return_sorted=True)TheKDTreecan also be used from within numba functionsimportnumpyasnpfromnumbaimportnjitfromnumba_kdtreeimportKDTreedefnumba_function_with_kdtree(kdtree,data):foriinrange(data.shape[0]):distances,indices=kdtree.query(data[0],k=30)#<Use the computed neighborsdata=np.random.random(3_000_000).reshape(-1,3)kdtree=KDTree(data,leafsize=10)numba_function_with_kdtree(kdtree,data[:10000])TODOsImplement all scipyckdtreefunctionsFix the parallel query functions