Saravanan666/pypi-org huggingface dataset:
Package: turboguard
turboguardPython C extension to validate and sanitize the user input using blacklist
and character map.InstallpipinstallturboguardQuickstart.Create an instance of theSanitizerclass as the below.TheSanitizer.__enter__method returns acallable(str) -> strwhich let
you to call it many times without worring about performance and memory leak.fromturboguardimportSanitizer,BlacklistedErrorblacklist=[('\U0001d100','\U0001d1ff'),# Blacklist Unicode range('\u0600','\u0610'),# Blacklist Unicode range'\u0635',# Blacklist single character]replace=[('\u0636','\u0637'),# Replace \u0636 by \u0637('b','B'),]withSanitizer(blacklist,replace)assanitize:# Loading(Slow) parttry:# Fast callsassertsanitize('foo bar')=='foo bar'assertsanitize(None)==NoneexceptBlacklistedError:print('Validation failed!')ContributionTheturboguard/core.cfile contains all logics for allocation and memory
cleanup as well as thecore_sanitizefunction which is the only function
to use the given database.turboguard/__init__.pyjust contains the Python wrapper arround the C
module and force cleanup and initiate using the Python's context manager
(thewithsyntax).What to do after fork:Setup development environmentIt's highly recommended to create a virtual environment at the first.Install project in editable mode:pip install -e .makeenvBuild the C extensionmakebuildTest your environmentmakecoverWhat to do after edit:Lint  code using:makelintPass tests:makecleanbuildcoverSubmit a pull request.
Package: turbo-http
turbo-http 🚀turbo-http is an asynchronous Python requests-like wrapper for PortSwigger's Turbo Intruder. Unleash the power of Turbo
Intruder's lightning-fast HTTP request engine directly from your Python scripts. Perfect for cybersecurity tasks, mass
API requests, and web scraping at scale!Table of ContentsInstallationRequirementsQuick StartPerformanceAcknowledgmentsLicenseInstallationDownload and install the latest released version fromPyPI:pipinstallturbo-httpRequirementsJava SE Development17+Quick StartHere's a simple example to get you started:fromturbo_httpimportTurboClientclient=TurboClient(url='https://example.net')response=awaitclient.get('/endpoint/example',params={'key':'foo','page':'bar'})response.url"https://example.net/endpoint/example?key=foo&page=bar"response.status_code"200"response.headers"{'Content-Type': 'application/json; charset=utf-8', 'Content-Length': 'XX', 'Connection': 'keep-alive'}"POST Requests:fromturbo_httpimportTurboClientclient=TurboClient(url='https://fakestoreapi.com')post_data={'title':'example product','price':14.4,'description':'example desc'}response=awaitclient.post('/products',json=post_data)PerformanceResults are completely dependent on the target server, engine configuration, and network conditions. The following are only some basic examples to give an idea of the general speed. Turbo Intruder's authormentions30,000 RPS being the highest they were able to achieve.# THREADED Engine (default) 100 requests (116 requests per second)withTurboClient(url='https://httpbin.org/')asclient:tasks=[]foriinrange(1,100):tasks.append(client.get(f'/get'))r=awaitasyncio.gather(*tasks)Elapsedtime:0.8579223155975342seconds.# THREADED Engine (default) 1,000 requests (371 requests per second)withTurboClient(url='https://httpbin.org/')asclient:tasks=[]foriinrange(1,1000):tasks.append(client.get(f'/get'))r=awaitasyncio.gather(*tasks)Elapsedtime:2.6971070766448975seconds.# HTTP2 Engine 1,000 requests (760 requests per second)withTurboClient(url='https://httpbin.org/',engine=Engine.HTTP2)asclient:tasks=[]foriinrange(1,1000):tasks.append(client.get(f'/get'))r=awaitasyncio.gather(*tasks)Elapsedtime:1.3151829242706299seconds.# HTTP2 Engine 10,000 requests (3,367 requests per second)withTurboClient(url='https://httpbin.org/',engine=Engine.HTTP2)asclient:tasks=[]foriinrange(1,10000):tasks.append(client.get(f'/get'))r=awaitasyncio.gather(*tasks)Elapsedtime:2.9748992919921875seconds.Acknowledgmentsturbo-http is made possible thanks to the incredible work done by the original authors
ofTurbo Intruder. This library is merely a humble wrapper, designed to
bring the capabilities of Turbo Intruder into the hands of Python developers without the need to navigate Burp Suite. We
extend our deepest gratitude and encourage all users to explore the fantastic tools offered
byPortSwigger.Licenseturbo-http is licensed under the Apache-2.0 License. However, it's important to note that this library is built upon the
hard work of others, so please use responsibly and always give credit where credit is due.
Package: turbojpeg
turbojpeg-pythonPython bindings for libjpeg-turbo using pybind11. It's using Version 3 of the TurboJPEG C API.It implements the three functionstransform,compressanddecompress. For more details see the function docstrings or the TurboJPEG C API docshttps://rawcdn.githack.com/libjpeg-turbo/libjpeg-turbo/main/doc/html/group___turbo_j_p_e_g.htmlInstallpip install turbojpegExampleRotate JPEG file losslessly by 90 degrees.importturbojpegwithopen("image.jpg","rb")asfr:img_data=fr.read()# lossless rotation. fails if lossless rotation is not possible# for example when the image dimensions are not multiples of 16img_data_rot=turbojpeg.transform(img_data,op=turbojpeg.OP.ROT90)withopen("image-rotated.jpg","xb")asfw:fw.write(img_data_rot)Save random image as JPEG with arithmetic coding.importnumpyasnpimportturbojpegarr=np.random.randint(0,256,(256,256,3),dtype=np.uint8)# random color imagequality=90result=turbojpeg.compress(arr,quality,turbojpeg.SAMP.Y420,arithmetic=True)withopen("arithmetic.jpg","xb")asfw:fw.write(result)BuildThe Pythonsetup.pyexpects thelibjpeg-turbobinaries inlibjpeg-turbo-build. You can either build it yourself or download precompiled binaries. For more details seehttps://github.com/libjpeg-turbo/libjpeg-turbo/blob/main/BUILDING.md.GitHub actions CI builds wheels for Windows 32 and 64-bit, manylinux 64-bit, musllinux 64-bit and MacOS 64-bit. 32-bit Linux builds fail due some weird NASM error. Not all wheels are tested, since some of the test dependencies are not available for all platforms.Run testspython -m unittest discover -s tests
Package: turbo-lazy
Turbo-LazyBasically, it comes down to the following statement:{% lazy 'apps.core.partial_views._machine_card' poll_status.machine_id %}
    {% include 'core/partials/_machine_card_loading.html' with name=poll_status.name machine_id=poll_status.machine_id %}
{% endlazy %}The content inside the lazy tag is rendered right away in a turbo-frame with a src tag that points to an auto generated URL where the view given lazy controller is called (with the parameters given in the controller). So this can be a slow loading view as its lazy loaded.
This is based on turbo-frames, i.e. the lazy loading is client initiated. With something like turbo-streams (and the django implementation), it would be possible to push the update from the server which would be even cooler (especially a single channel could be used for multiple elements).
So, the page will be rendered as follows:<turbo-frame id="1c87a216-6ad5-4320-861e-261caf3e5dd7" src="/lazy/?token=eyJpZCI6ICIxYzg3YTIxNi02YWQ1LTQzMjAtODYxZS0yNjFjYWYzZTVkZDciLCAidmlldyI6ICJhcHBzLmNvcmUucGFydGlhbF92aWV3cy5fbWFjaGluZV9jYXJkIiwgImFyZ3MiOiBbMV0sICJrd2FyZ3MiOiB7fX0=">   
    <div class="card bg-primary text-white mb-4">
        <div class="card-body">

            <b>Machine 1</b>

        </div>
        <div class="card-footer" style="height: 7rem;">

            <div class="d-flex justify-content-center">
                <div class="spinner-border" role="status">
                    <span class="sr-only">Loading...</span>
                </div>
            </div>

        </div>
        <div class="card-footer d-flex align-items-center justify-content-between">

            <a class="small text-white stretched-link"
               href="/machine/1/" target="_top">View Details</a>
            <div class="small text-white"><i class="fas fa-angle-right"></i></div>

        </div>
    </div>
</turbo-frame>And a call to/lazy/?token=...will return the response of calling the view functionapps.core.partial_views._machine_cardwith given positional argumentpoll_status.machine_idalso wrapped inside
a<turbo-frame>tag with the sameidso that it will automatically swapped by turboframe.ExampleA complete example can be found underexample/django_example.
Go to this directory and proceed.Optionally, create a custom virtualenvpython -m venv venv
source venv/bin/activate
pip install -r requirements.txtIf you want to use the current snapshot ofturbo-lazyinstall it viapip install ../../Then start everything viapython manage.py migrate
python manage.py runserverand go tohttp://localhost:8000/Release Notes0.2.2The install dependencydjango-tag-parser>=3.2was missing insetup.py0.2.1Template folder was accidentally named wrong in the published version0.2.0Package renamed to 'turbo_lazy' and also root folder renamed to 'turbo_lazy'.Template Tag{% include_view %}was added to integrate complete views into templates (in modulepartials)0.1.2Complete working Django Example0.1.1Several minor fixes0.1.0Initial Release
Package: turbo-markdown
turbo-markdownA cli tool to view local markdown filesFree software: MIT licenseDocumentation:https://turbo-markdown.readthedocs.io.Get StartedInstall and use turbo_markdown:pip install turbo-markdown
cd markdown_dir/
turbo-markdownHistory0.1.0 (2017-09-28)First release on PyPI.0.2.0 (2017-09-29)Fix docs.
Package: turbo_motor
turbo-motorFree software: Apache Software License 2.0Documentation:https://turbo-motor.readthedocs.io.Turbo motor plugin, supportturbouseMotor.History0.0.2 (2017-1-31)release 0.0.2, formally support motor in turbo 0.4.50.0.1 (2016-12-24)First release on PyPI.
Package: turbopuffer
turbopuffer Python ClientThe official Python client for accessing the turbopuffer API.UsageInstall the turbopuffer package and set your API key.$pipinstallturbopufferOr if you're able to run C binaries for JSON encoding, use:$pipinstallturbopuffer[fast]Start using the APIimportturbopufferastpuftpuf.api_key='your-token'# Alternatively: export=TURBOPUFFER_API_KEY=your-token# Open a namespacens=tpuf.Namespace('hello_world')# Read namespace metadataifns.exists():print(f'Namespace{ns.name}exists with{ns.dimensions()}dimensions and approximately{ns.approx_count()}vectors.')# Upsert your datasetns.upsert(ids=[1,2],vectors=[[0.1,0.2],[0.3,0.4]],attributes={'name':['foo','foos']})# Alternatively, upsert using a row iteratorns.upsert({'id':id,'vector':[id/10,id/10],'attributes':{'name':'food'}}foridinrange(3,10))# Query your datasetvectors=ns.query(vector=[0.15,0.22],distance_metric='cosine_distance',top_k=10,filters={'name':[['Glob','foo*'],['NotEq','food']]},include_attributes=['name'],include_vectors=True)print(vectors)# [#   VectorRow(id=2, vector=[0.30000001192092896, 0.4000000059604645], attributes={'name': 'foos'}, dist=0.001016080379486084),#   VectorRow(id=1, vector=[0.10000000149011612, 0.20000000298023224], attributes={'name': 'foo'}, dist=0.009067952632904053)# ]# List all namespacesnamespaces=tpuf.namespaces()print('Total namespaces:',len(namespaces))fornamespaceinnamespaces:print('Namespace',namespace.name,'contains approximately',namespace.approx_count(),'vectors with',namespace.dimensions(),'dimensions.')# Delete vectors using the separate delete methodns.delete([1,2])Endpoint DocumentationFor more details on request parameters and query options, check the docs athttps://turbopuffer.com/docs
Package: turborest
TurboRESTTurboREST is a REST API framework for Python 3.7+.InstallationpipinstallturborestUsagefromturborestimportClientdefmain():client=Client(format="json")client.get("https://api.bytesentinel.io/test")AuthenticationfromturborestimportClientdefmain():auth=("Bearer","xyz")client=Client(format="json",auth=auth)client.get("https://api.bytesentinel.io/test")ProxyfromturborestimportClientdefmain():proxy="http://localhost:8080"client=Client(format="json",proxy=proxy)client.get("https://api.bytesentinel.io/test")Advanced OptionsfromturborestimportClientdefmain():proxy="http://localhost:8080"endpoint="https://api.bytesentinel.io/test"auth=("Bearer","xyz")client=Client(format="json",proxy=proxy,auth=auth)client.set_user_agent("TestAgent/1.0.0")client.set_success(print)client.set_header("X-Test","Test")res=client.get(endpoint)print(res.status_code)ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseMIT LicenseMIT License

TurboREST - A REST API framework for Python 3.7+.

...
Package: turbos3-py
turbos3-pyturbos3-pyis a python package for turbocharged S3 downloads and uploads.Installturbos3-pyis available via pippipinstallturbos3-pySetupturbos3-pyuses the same auth methods as boto3 and the AWS CLI i.e. credentials file and env variables. Seeherefor more detailsUsagefromturbos3_pyimportdownload,uploadimportasyncioasyncdefmain():awaitdownload(bucket="my-bucket",output="./data")awaitupload(bucket="my-other-bucket",input="./some_local_dir")if__name__=="__main__":asyncio.run(main())
Package: turboself
turboself-apiAPI for the TurboSelf web application.Is able to fetch user reservations, remaining credits and account data.InstallationUse at least python3.11Install using pip:pip install git+https://github.com/Egsagon/turboself-api.gitOr clone this repository and use the module locally.UsageThere is an example usage in themain.pyfile which show how to print resservations per week:importturboselfclient=turboself.Client('username','password')forweekinclient.get_reservations():print(f'WEEK{week.date.month}/{week.date.year}')fordayinweek.days:do_eat='92myes'ifday.eatelse'91mno'ifnotday.can_eat:do_eat='30mno'print(f'\t*{day.date.date()}:\033[{do_eat}\033[0m')
Package: turboshtein
turboshteinBackgroundI didn't study computer science sobitwise operationshave always been a bit of a gray area for me. I finally gave in and decided to spend some time reading up on the subject and ended up implementing the Myers bit-parallel algorithm[1]for computingLevenshtein edit distance. I was fairly happy with the results so I wrapped the original C code in Python and uploaded it to PyPI. Coincidentally, it's also pretty fast.UsageInstall via PyPI:pipinstallturboshteinOr not:python setup.py installComputing levenshtein edit distance between two strings:>>>fromturboshteinimportlevenshtein>>>levenshtein("saturday","sunday")3Note that there are several limitations, mainly:Max string length must be less than 64.ASCII characters only.PerformanceValues represent the number of string pairs processed per second.libraryversionm=n=8m=n=16m=n=24m=n=32m=n=40m=n=48m=n=56turboshtein.levenshtein0.0.47,625,6126,134,3675,160,7574,455,6923,897,9663,477,8853,147,237Levenshtein.distance0.20.56,448,0355,335,2394,556,6804,034,2263,550,6953,211,6642,910,581rapidfuzz.distance.Levenshtein.distance2.11.16,446,9545,268,4544,560,2134,027,1103,540,5743,215,2062,912,386jellyfish.levenshtein_distance0.9.03,823,8431,494,087718,785399,693262,541183,554135,081textdistance.levenshtein4.5.0444,305413,950411,441401,539395,777389,462385,233Test setup:XPS 15 9570Intel i7-8750HFedora Linux 36 (Workstation Edition)References[1]: Gene Myers. 1999. A fast bit-vector algorithm for approximate string matching based on dynamic programming. J. ACM 46, 3 (May 1999), 395-415.https://doi.org/10.1145/316542.316550
Package: turbosmsua-python3
Реализация SOAP сервиса для отправки смс через сервис turbosms.ua Python3+Для установки пакета выполните:$pipinstallturbosmsua-python3Все. Теперь в системе доступен пакет turbosmsua.importturbosmsua# Создадим инстанс# Если неверный логин/пароль или что-то еще будет выброшен ValueError с описанием ошибки от сервисаt=turbosmsua.Turbosms(login,password)# Получение баланса. Float или ValueErrort.balance()# Отправка смс.# Первый параметр - отправитель# Второй параметр - список получателей# Третий параметр - текст# Четвертый параметр - wappush - url. Оно как-то странно работает и смс с ним на айфон не доходят## В ответ dict. С ключем status - это статус отправок по русски как отдал сервис,# все остальные ключи - это номер телефона и статус ответа по ним.send_statuses=t.send_text("sended",[960000000,80960000000,380960000000,"960000000","+380960000000"],"Это текст сообщения")# Получение статуса отправки по id сообщения. В ответ не обработанный ответ от сервисаprintt.message_status("123123-3123123-1231ad-sd")
Package: turbosmtp
TurboSMTP client for service http://serversmtp.comTurboSMTP (http://serversmtp.com) is online service for sending emails. TurboSMTP client is very simple Python library for using online API of this service. The difference between this client and client provided by serversmtp.com (http://www.serversmtp.com/en/smtp-api) is in a way how e-mail messages are handled. This library supports python built-in email module, which makes composing complex e-mail messages much easier.To use this module you have to have account athttp://serversmtp.com. Basic account is free of charge.Version1.0.1Installationsh:$ pip install turbosmtpUsagepython:import turbosmtp
from email import message

message = message.Message()
message["From"] = "from@address.com"
message["To"] = "to@addresss.com"
message["Subject"] = "Hello World!"

smtp = turbosmtp.TurboSMTP("your_username", "your_password")
smtp.send(message)Want to contribute?Anyone is welcome to contribute to this project :).
Just contact me on Github.LicenseGNU GENERAL PUBLIC LICENSE
Package: turbo-text-transformer-prompts
Turbo Text Transformer PromptsNote this is automatically installed when you dopip install turbo-text-transformer. This repo is just for storing the templates.Designed for use withturbo-text-transformer.You pipe some text in, the template is applied, then you pipe it intottt(frompip install turbo-text-transformer) which will process it with eg. OpenAI.cat pyproject.toml tttp/__main__.py | tttp -t readme | ttt > README.mdTurbo Text Transformer Prompts is a command-line tool that allows users to generate text files from pre-configured templates using user input prompts. The tool uses Jinja2 templating engine to render text files from templates.How to Runpipinstallturbo-text-transformer-promptsYou will also need to clone the repository containing the templates you want to use. For example:mkdir-p~/.config/ttt/
gitclonehttps://github.com/fergusfettes/turbo-text-transformer-prompts~/.config/tttTemplate StructureA template is a text file written in Jinja2 syntax. The file should have the.j2extension and be placed inside thetemplatesdirectory. They will be installed in the~/.config/ttt/templatesdirectory.This is a smiple example of a template:Context: Provide only code as output.Prompt:{{prompt}}Code:It will just output a code snippet based on the query.You can also pass a list of flags to the prompt to fine tune the control, such as this:This is an example of minimally altering some given code to achieve a specific task.I received the following code:`{{language}}{{prompt}}`My task was to make minimal alterations to this code to: "{{task}}".The altered code is below.`{{language}}For this one, you can pass the 'task' and 'language' arguments to make it more specific.ContributingPULL REQUESTS WITH MORE TEMPLATES VERY WELCOME!If you find a bug or would like to contribute to Turbo Text Transformer Prompts, please create a new GitHub issue or pull request.LicenseTurbo Text Transformer Prompts is licensed under the MIT License. See theLICENSEfile for details.
Package: turbtools
turbtoolsPython Package for Statistical Analysis of Turbulence DataInstallationPyPIturbtoolsis available as a python package fromhttps://pypi.org/project/turbtools/.
Download and install it as a system or environment package with pip. It can be then used in a CLI mode or as a python module$pipinstallturbtoolsSourceAlternatively, the latestturbtoolspackage source tarball can be downloaded fromhere(18.9 kB).UsageUsage: turbtools [-h] [-o] [-f datafile] FUNCTION_NAMEOptions-h, --help            Show this help message and exit-o, --outpath         Path to save output stats and plots-f, --datafile        Path of datafile in .mat or .ncFUNCTION_NAME         Name of the the functionExamplesInitialize the datafile to analyze$wgethttps://github.com/andre-fuchs-uni-oldenburg/OPEN_FPE_IFT/raw/master/Renner_8000_Hz.mat$turbtools-f./Renner_8000_Hz.matDescribe stats and plot the raw signal$turbtoolsplot_signalPlot stats stationarity$turbtoolsplot_stationarityPlot probability density function$turbtoolsplot_pdfPlot turbulent energy spectrum$turbtoolsplot_spectrumIssues:Problems? Please raise an issue athttps://github.com/aakash30jan/turbtools/issues.LicenseThis work is licensed under a GNU General Public License Version 3 .
Package: turbx
turbxExtensible toolkit for analyzing turbulent flow datasets.Install withpip:pip install --upgrade --user turbxDocumentation available at:https://iagappel.github.io/turbxturbxruns inpython3and uses parallelHDF5(wrapped byh5py) for high-performance collective MPI-IO withmpi4py. This requires:Apython3installation (3.8+ recommended)An MPI implementation such asOpenMPIA parallelHDF5installation (must be compiled with--enable-parallel)mpi4py(optionally compiled from source)h5pycompiled with parallel configurationAn environment configuration guide can be found here:https://iagappel.github.io/turbx/envVisualization ofHDF5datasets is possible usingParaviewwith the use ofxdmfdata descriptor files, which are written automatically by calling.make_xdmf()onturbxdata class (such asrgd) class instances.
Package: turcy
turCyAn Open Information Extraction System  mainly designed for German.InstallationpipinstallturcyCan be applied to other languages as well, however some extrawork is necessary
as no patterns for english are shipped. Therefore, you would have to build your own patterns first.
For building patterns, a `pattern_builder module is available.How it works1. Building a Pattern2. ExtractionLoad the German Language Model from spaCy.Add turCy to the nlp-Pipeline.Pass the document to the pipeline.Iterate over the sentences in the document and access the triples in each sentence.defexample():nlp=spacy.load("de_core_news_lg",exclude=["ner"])nlp.max_length=2096700turcy.add_to_pipe(nlp)# apply/use current patterns in listpipeline_params={"attach_triple2sentence":{"pattern_list":"small"}}doc=nlp("Nürnberg ist eine Stadt in Deutschland.",component_cfg=pipeline_params)forsentindoc.sents:print(sent)fortripleinsent._.triples:(subj,pred,obj)=triple["triple"]print(f"subject:'{subj}', predicate:'{pred}' and object: '{obj}'")3. ResultsReferences
Package: turengcli
Command-line tool fortureng.comwith rich output.pip install turengcliUsagetureng <word># example usage with -d(detailed results) argument
tureng goner -dArguments# -d, --detailed  detailed results
$ tureng goner -d

# -c, --correct   auto-correct misspelled input with first suggestion
$ tureng corrct -d
> Auto-correcting: corrct -> correct

# -p, --plain   returns plain text output
$ tureng renascence -p
> General: uyanÄ±ÅŸ, yeniden doÄŸma
> History: rÃ¶nesans

# -f, --fuzzy   returns fuzzy search results
$ tureng renas -f
> renascence, renascency, renascent, renascibleRequirementsPython >3.5Dependenciesrequestsbeautifulsoup4richlxmlLicenseCC0
Package: tureng-cli
tureng-cli- Tureng CLITureng CLI is a very basic command line tool for tureng.comInstallpip install tureng-cliUsageUsage:tureng.pytr<word>tureng.pyen<word>-h--helphelpExampletr-en$ tureng tr bilgisayar
https://tureng.com/en/turkish-english/bilgisayar
Word: bilgisayar Language: tr
----------------------------------------
computer
hardware system
machine
data computer
computing device
computing machine
thinking-machine
machine
computer
computeren-tr$ tureng en computer
https://tureng.com/en/turkish-english/computer
Word: computer Language: en
----------------------------------------
bilgisayar
kompüter
elektronik beyin
bilgisayar
kompüter
bilgisayar
kompütör
bilgi sayar
bilgisayarı kapatmak
bilgisayarı çökmekRequirementsrequests==2.25.1
docopt==0.6.2
selectolax==0.2.11