Saravanan666/pypi-org huggingface dataset:
Package: neoricalex
c (Windows)Esta pasta Ã© referente ao desenvolvimento no Ambiente Windows. Ainda estÃ¡ muito "crua" e tudo estÃ¡ tipo "atacado".Powershell:Criar Ambiente Virtual:
    python ambiente.py python

Activar Ambiente Virtual: 
    python/Scripts/Activate.ps1

Iniciar o neoricalex:
    python neoricalex.py

Pacote pip neoricalex:
    python -m pip install --upgrade setuptools wheel
    python setup.py sdist bdist_wheel

Instalar MÃ³dulo para criar EXE:
    pip install --upgrade --no-cache-dir pyinstaller

Criar EXE:
    pyinstaller --onefile -c neoricalex.py # Para esconder o terminal adicionar a flag -w

UI:

    Transformar .ui em Python:
        pyuic5 -x <nome.ui> -o <nome.py>Miscelaneous:Prompt de Comando:

    setups/python-3.8.2-amd64.exe
    python -m pip install --upgrade pip
    pip install --upgrade --no-cache-dir pyinstaller pysimplegui pyaudio gitpython
    cd c
    pyinstaller --onefile -w neoricalex.py
    cookiecutter https://github.com/neoricalex/cookiecutter-django.git

Start-Process powershell -Verb runAs

Powershell Administrador:

    Atualizar Windows 10:

        Install-Module PSWindowsUpdate
        Get-WindowsUpdate
        Install-WindowsUpdateAtivar Sub-Sistema Linux:.\dism.exe /online /enable-feature /featurename:Microsoft-Windows-Subsystem-Linux /all /norestart 
.\dism.exe /online /enable-feature /featurename:VirtualMachinePlatform /all /norestartCatÃ¡logo Windows:https://www.catalog.update.microsoft.com/
Package: neos
neosneuralnice end-to-end optimized statisticsAboutRun the binder demo :) ->Leverages the shoulders of giants (jaxandpyhf) to differentiate through a high-energy physics analysis workflow, including the construction of the frequentist profile likelihood.If you're more of a video person, seethis talkgiven byNathanon the broader topic of differentiable programming in high-energy physics, which also coversneos.Example usage -- train a neural network to optimize an expected p-valuesetupIn a python 3 environment, run the following:pip install --upgrade pip setuptools wheel
pip install neos
pip install git+http://github.com/scikit-hep/pyhf.git@make_difffable_model_ctorWith this, you should be able to run the demo notebookdemo.ipynbon your pc :)This workflow is as follows:From a set of normal distributions with different means, we'll generate four blobs of(x,y)points, corresponding to a signal process, a nominal background process, and two variations of the background from varying the background distribution's mean up and down.We'll then feed these points into the previously defined neural network for each blob, and construct a histogram of the output using kernel density estimation. The difference between the two background variations is used as a systematic uncertainty on the nominal background.We can then leverage the magic ofpyhfto construct anevent-counting statistical modelfrom the histogram yields.Finally, we calculate the p-value of a test between the nominal signal and background-only hypotheses. This uses the familiarprofile likelihood-based test statistic.This counts as one forward pass of the workflow -- we then optimize the neural network by gradient descent, backpropagating through the whole analysis!ThanksA big thanks to the teams behindjax,fax,jaxoptandpyhffor their software and support.
Package: neos-client
NEOS ClientThis is a Neos Client forNEOS Server: XML-RPC api. Whilethe official clientonly provides a queue or submit job call, this client supports the following methods:is_alive(): Equivalent to sending a ping to the server and checking the result.get_job_status(job_number, job_password): Equivalent to getJobStatus(jobNumber, password) in NEOS Server RPC.get_completion_code(job_number, job_password): Equivalent to getCompletionCode(jobNumber, password) in NEOS Server RPC.get_job_info(job_number, job_password): Equivalent to getJobInfo(jobNumber, password) in NEOS Server RPC.kill_job(job_number, job_password, message): Equivalent to killJob(jobNumber, password, killmsg="") in NEOS Server RPC.get_final_results(job_number, job_password, is_blocking): Equivalent to getFinalResults(jobNumber, password) if it's blocking. If it's not blocking, it Equivalent to getFinalResultsNonBlocking(jobNumber, password).email_job_results(job_number, job_password): Equivalent to emailJobResults(jobNumber, password) in NEOS Server RPC.get_intermediate_results(job_number, job_password, offset, is_blocking): Equivalent to getIntermediateResults(jobNumber, password, offset) if it's blocking. If it's not blocking, it Equivalent to getIntermediateResultsNonBlocking(jobNumber, password).download_output(job_number, job_password, working_directory): Downloads the results and extracts the files to the given working directory. It Equivalent to getOutputFile(job_number, job_password) in NEOS Server RPC and extracting it to the working directory.print_queue(): Prints the current queue on the server if the server is alive.submit_job(xml_path, is_blocking, working_directory): If the username and password is given, it is equivalent to authenticatedSubmitJob(xmlstring, user, password). If the username or password is not given, it is equivalent to submitJob(xml_string). If it's blocking, it waits until all the results are received. If it's non-blocking, it immediately returns after submitting the job.Installationpip install neos_clientorgit clone git@github.com:mabdullahsoyturk/neos_client.git
cd neos_client
pip install .Usagefromneos_clientimportNeosClientclient=NeosClient(email=<your_email_address>)client.submit_job(xml_path=<path_to_your_xml_file>)
Package: neos_common
NEOS Platform Common Code v0.10.36PrerequisitesThe following packages are used across python repositories. A global install of them all ishighlyrecommended.PoetryInvokeWSLIf running on Windows, you may need to installdistutilsto install the service.$sudoapt-getinstallpython3.8-distutilsInitial setup$invokeinstall-devCode QualityTestsinvoketests
invoketests-coverageLintinginvokecheck-style
invokeisortReleasesRelease management is handled usingbump2version. The below commands will tag
a new release. Jenkins will then publish the release to the artifact repository.$invokebump-patch
$invokebump-minor
$invokebump-major
>vX.Y.Z
Package: neoscore
neoscorenotation without barsExample sourceNeoscore is a Python library for creating scores without limits. While other notation software assumes scores follow a narrow set of rules, neoscore treats scores as shapes and text with as few assumptions as possible. In neoscore, staves and noteheads are just one way of writing. Its programmatic nature makes it especially useful for generative scoremaking, and it even supports experimental animation and live-coding!Quick StartNeoscore requires a minimumPython version of3.7. If you don't have it you'll need to first install it, thenset up a virtual environment with it. In that environment you can then install neoscore with pip  usingpip install neoscore, after which you should be able to run this example:fromneoscore.commonimport*neoscore.setup()Text(ORIGIN,None,"Hello, neoscore!")neoscore.show()If you have installation problems, please seeour troubleshooting guide.DocumentationVisitneoscore.orgfor thorough documentation and dozens of examples. You can find moreelaborate examples in this repository here.CreditsNeoscore was principally developed byAndrew Yoon, originally begun in 2016 atThe Recurse Centerthen revived and released in 2022 with substantial support fromCraig Vearand theDigiScoreresearch project. Additional support was provided byPrashanth Thattai Ravikumar. Many of neoscore’s design choices were informed by the excellent open source notation projectsAbjad,Lilypond, andMuseScore.The Digital Score project (DigiScore) is funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. ERC-2020-COG - 101002086).Get your name added here by getting involved!
Package: neoscrypt
python-neoscrypt-wrappersrc/          || neoscrypt source files (C)scripts/      || ie. cross-compiling scripts to create .so, .dylib or dlltests/        || python neoscrypt testfileThis Software needs compile (gcc, mingw ...)Install from gitCompiling and installing as PythonModule (python2/python3)::bash:~/: sudo python3 -m pip install git+https://github.com/sparkspay/python-neoscrypt.git
 
or
 
bash:~/: git clone git+https://github.com/sparkspay/python-neoscrypt.git
bash:~/: cd python-neoscrypt
bash:~/: python-neoscrypt/: sudo python3 setup.py installPypi Version install will be available ::bash:~/: pip install neoscryptNeoScryptNeoScrypt is a strong memory intensive key derivation function.Compile time definitions: --DNEOSCRYPT_SHA256 enables optional SHA-256support (Scrypt compatibility); --DNEOSCRYPT_BLAKE256 enables optionalBLAKE-256 support; --DNEOSCRYPT_OPT enables FastKDF performanceoptimisations; --DNEOSCRYPT_ASM enables 32-bit and 64-bit assemblyoptimisations; --DNEOSCRYPT_MINER_4WAY enables 4-way mining per thread (requires -DNEOSCRYPT_ASM).DocumentationRefer to the following white paper for an introduction to NeoScrypt:http://phoenixcoin.org/archive/neoscrypt_v1.pdfSource Linkshttps://github.com/goacoincore/neoscrypt-> goacoincorehttps://github.com/ghostlander/NeoScrypt-> ghostlander
Package: neosctl
Core CLI v0.11.16PrerequisitesThe following packages are used across python repositories. A global install of them all ishighlyrecommended.PoetryInvokeKubefwdWSL (optional)If running on Windows, you may need to installdistutilsto install the service.$sudoapt-getinstallpython3.10-distutilsPort forwarding (optional)To access the APIs locally, you will need to connect to the pod inside
the cluster usingkubefwd.$sudokubefwdsvc-ncore-c~/.kube/configSetupInstall CLIInstall the CLI usingpip:pipinstallneosctlTo install the CLI from source, clone the repository and run the following$invokeinstall-devWhen running locally, if you do not manage your own virtual environments, you
can use poetry to put you in a shell with access to the installed code.$poetryshellSetup profileTo setup a profile, run the following command:neosctlprofileinitand set all required parameters. More information about this command you can find in theDOCS.mdfile.Login to the systemTo login to the system, run the following command:neosctlauthloginYou will need username and password for that.Setup service user (optional)For some operations, you will need to provide a service useraccess_key_idandsecret_access_key, adding them into the file~/.neosctl/credential. To create service user and get it's access and secret key, use IAM API.Review settingsAll setting are stored by default in the folder~/.neosctl/.You can also review all settings by running the following commands:neosctlprofilelist
neosctlprofileviewReuse profileTo work with the same profile across multiple commands you can export the
profile name as anNEOSCTL_PROFILEenvironment variable.$neosctl-pmy-profile--help
...
$exportNEOSCTL_PROFILE=my-profile
$neosctl--helpUsageTo see all available commands, run the following command:neosctl--helpor go to theDOCS.mdfile.Code QualityTestsinvoketests
invoketests-coverageLintinginvokecheck-style
invokeisortGenerate docsTo generate docs in a markdown format, run the following command:invokegenerate-docs-mdThe outputDOCS.mdfile could be used to update the NEOS documentation site
(docs.neosmesh.com).ReleasesRelease management is handled usingbump2version. The below commands will tag
a new release. This will also update the helm chart version, this should not be
manually changed.$invokebump-patch
$invokebump-minor
$invokebump-major
>vX.Y.Z
Package: neosekai-api
neosekai-apiAn Unoffical Python API forneosekaitranslations.comInstallationfrom a terminal, run :pip install neosekai-apiin case of an error, goherein the Github Wiki.UsageGo to theGithub wikifor this project to read full usage tutorial.Below is a short and sweet versionfromneosekai_apiimportNovel,NovelChapternovel=Novel('https://www.neosekaitranslations.com/novel/oresuki/')chapter=NovelChapter('https://www.neosekaitranslations.com/novel/oresuki/volume-1/chapter-3-part-3/')NovelandNovelChapterare the two main objects in this APIthrough these two objects, neosekai-api offers a list of functions that are specified in theGithub wiki.neosekai-dlneosekai-dl is a project that uses neosekai-api to download novels fromneosekaitranslations.comas PDF. The project is also in it's starting stages, so please bear with me. It's not a far fetch to say I made this API for the sole purpose of making neosekai-dlFor Future ReleasesIf you are using this project and wish to see more features in it, do open an issue ongithub.As I mentioned, I built this API for making neosekai-dl. This project, as it is, is all that is required for me to make neosekai-dl. However, if you wish to see more features in this API, I would be glad to implement your needs.On creating support for authentificationneosekai-api, at the moment, does NOT support the registraton of new users or the ability to log in to neosekaitranslations.com.This is mostly because, In neosekaitranslations.com, the only thing a registered user can do is leave comments. Since I feel like commenting is better done on the website itself, I didn't implement this functionality in the program.
Package: neosensory-python
Neosensory Python SDKA Python package for interacting with Neosensory products. This is designed to work withBleak, a cross-platform Python Bluetooth Low Energy (BLE) client for Windows, MacOS, and Linux. Please see the Bleak project page for more specifics on supported platforms.IMPORTANT: At the moment this is tested and only working on Windows.RequirementsThis SDK relies onBleakto take care of the underlying Bluetooth low energy shenanigans. It is included as a dependency and should get automatically installed if using pip or setup.py.Installationyou can clone this repo and runpythonsetup.pydevelopfrom within the root directory.UsageYou may need to first pair your Neosensory Buzz in advance with your operating system (OS). To do so, find your OS’s Bluetooth settings/panel and follow its instructions for pairing a new device. To put Buzz into pairing mode, hold down the (+) and (-) buttons until the LEDs flash blue.See this repo’sexamplesdirectory to get up and running quickly.DocumentationYou can learn more about the available commands on thisproject documentation page. Neosensory platform-agnostic documentation can be obtained from the Neosensorydeveloper site.LicensePlease note that while this project has an Apache 2.0 license, usage of the Neosensory API to interface with Neosensory products is still subject to the Neosensory developer terms of service locatedhere
Package: neospatial
neospatial-py=============Python Wrapper for Neo4j Spatial Plugin### Requirements* Install [neo4j/spatial](https://github.com/neo4j/spatial) server plugin### Installation```sudo pip install neospatial```### Usage```python>>> from neospatial import *>>> spatial = SpatialConnection("localhost", 7474)>>> spatial.createLayer('geom')>>> index = spatial.createIndex('geom')>>> node1 = spatial.createNode(45.46775, 9.21056, {'name':'Gold Restaurant'})>>> spatial.addNodeToIndex(node1, index)>>> node2 = spatial.createNode(45.48107, 9.19997, {'name':'Ristorante Da Giannino'})>>> spatial.addNodeToIndex(node2, index)>>> result = spatial.findWithinDistance(index, 45.48608, 9.20018, 5)>>> for i in result:>>>     print i['data']['name']>>> Ristorante Da Giannino>>> Gold Restaurant```
Package: neotasker
neotaskerLightweight Python library for modern thread / multiprocessing pooling and task
processing via asyncio.Neotasker is lightweight variation ofataskerlibrary: tasks don't have
priorities, go directly to ThreadPoolExecutor and are standard Python future
objects. This library is useful for the high-load projects with lightweight
tasks as majority tasks are directly proxied to pool.Neotasker works on top ofThreadPoolExecutorandasyncioand provides
additional features:Easy thread pool and asyncio loops initializationInterval, queue and event-based workersBuilt-in integration withaioschedInstallpip3installneotaskerSources:https://github.com/alttch/neotaskerDocumentation:https://neotasker.readthedocs.io/Code examplesStart/stopfromneotaskerimporttask_supervisor# set pool size# min_size='max' means pre-spawn all pool threadstask_supervisor.set_thread_pool(min_size='max',max_size=20)task_supervisor.start()# ...# start workers, other threads etc.# ...# optionally block current threadtask_supervisor.block()# stop from any threadtask_supervisor.stop()Executing futureYou may work withneotasker.thread_poolobject directly or usetask_supervisor.spawnfunction, which's directly mapped tothread_pool.submit)fromneotaskerimportthread_poolthread_pool.start()defmytask(a,b,c):print(f'I am working in the background!{a}{b}{c}')return777task=task_supervisor.spawn(mytask,1,2,c=3)# get future resultresult=task.result()Creating async io loopfromneotaskerimportthread_poolthread_pool.start()task_supervisor.create_aloop('default',default=True)# The loop will until supervisor is stopped# Spawn coroutine from another thread:task_supervisor.get_aloop().spawn_coroutine_threadsafe(coro)Worker examplesfromneotaskerimportbackground_worker,task_supervisortask_supervisor.start()# we need to create at least one aloop to start workerstask_supervisor.create_aloop('default',default=True)# create one more async looptask_supervisor.create_aloop('loop2')@background_workerdefworker1(**kwargs):print('I am a simple background worker')@background_workerasyncdefworker_async(**kwargs):print('I am async background worker')@background_worker(interval=1,loop='loop2')defworker2(**kwargs):print('I run every second!')@background_worker(queue=True)defworker3(task,**kwargs):print('I run when there is a task in my queue')@background_worker(event=True)defworker4(**kwargs):print('I run when triggered')worker1.start()worker_async.start()worker2.start()worker3.start()worker4.start()worker3.put_threadsafe('todo1')worker4.trigger_threadsafe()fromneotaskerimportBackgroundIntervalWorkerclassMyWorker(BackgroundIntervalWorker):defrun(self,**kwargs):print('I am custom worker class')worker5=MyWorker(interval=0.1,name='worker5')worker5.start()
Package: neotermcolor
modern ANSII Color formatting for output in terminalWhat is neotermcolorneotermcolor library is a fork of old goodtermcolor, which is widely used. I like
it very much, but unfortunately last release was long time ago.Everything is fully backward compatible with original termcolor:importsysfromneotermcolorimportcolored,cprinttext=colored('Hello, World!','red',attrs=['reverse','blink'])print(text)cprint('Hello, World!','green','on_red')print_red_on_cyan=lambdax:cprint(x,'red','on_cyan')print_red_on_cyan('Hello, World!')print_red_on_cyan('Hello, Universe!')foriinrange(10):cprint(i,'magenta',end=' ')cprint("Attention!",'red',attrs=['bold'],file=sys.stderr)Installationpip3installneotermcolorNew featuresIt works in Windows terminalYep, right out-of-the-box (tested on Windows 10)It is readline-safeWhen you mix ANSI color codes with readline input, it may cause a problem.
neotermcolor has a workaround:new param forcprintandcolored:readline_safe=Trueyou may also turn on readline-safe colorizing by default, settingimportneotermcolorneotermcolor.readline_always_safe=TrueIt has 256-color paletteIf color code is specified as an integer (0..255), ANSI 256-color palette is
used. You may specify color codes both for foreground and background and mix
them with other attributes:fromneotermcolorimportcprintcprint('Underline light-green (119) on grey (237)',119,237,['underline'])It is TTY-awareneotermcolor will not colorize text if process stdout or stderr is not a TTY.This feature is on by default, but you may turn it off:importneotermcolorneotermcolor.tty_aware=FalseIt has palette overridingYou may define own color names or override existing ones: e.g. you may use
standard palette for 16-color terminals, but override it when your program
detect terminal with 256-color support or when it's forced by user:importneotermcolorneotermcolor.set_color('red',197)neotermcolor.cprint('Red color is now purple','red')It has stylesStyles are alternative to classical defining a "style" for certain type of
messages withfunctools.partialorlambda. A style may contain color,
on_color and attributes:importneotermcolorneotermcolor.set_style('error',color='red',attrs='bold')neotermcolor.cprint('ERROR MESSAGE',style='error')# orneotermcolor.cprint('ERROR MESSAGE 2','@error')Note: if you specify both style and e.g. attrs, the style attrs will be
overriden.Single attribute can now be specified as a string# as list or tuplecprint('test',attrs=['bold'])# as a stringcprint('test',attrs='bold')How to use it instead of old termcolor in the existing projectsimportneotermcolorastermcolorI'll do my best to keep it backward compatible with original termcolor.Enjoy!
Package: neoteroi-auth
Authentication and authorization framework for Python appsBasic framework to handle authentication and authorization in asynchronous
Python applications.Features:strategy to implement authentication (who or what is using a service?)strategy to implement authorization (is the acting identity authorized to do a certain action?)support for dependency injection for classes handling authentication and
authorization requirementsbuilt-in support for JSON Web Tokens (JWTs) authenticationThis library is freely inspired byauthorization in ASP.NET
Core;
although its implementation is extremely different.Installationpipinstallneoteroi-authTo install with support forJSON Web Tokens (JWTs)validation:pip install neoteroi-auth[jwt]ExamplesFor examples, refer to theexamples folder.If you have doubts about authentication vs authorization...Authenticationanswers the question:Who is the user who is initiating the
action?, or more in general:Who is the user, or what is the service, that is
initiating the action?.Authorizationanswers the question:Is the user, or service, authorized to
do something?.Usually, to implement authorization, is necessary to have the context of the
entity that is executing the action.Usage in BlackSheepneoteroi-authis used in the second version of theBlackSheepweb framework, to implementauthentication and authorization
strategiesfor request
handlers.To see howneoteroi-authis used inblacksheepweb framework, read:AuthenticationAuthorizationDocumentationUnder construction. 🚧