Saravanan666/pypi-org huggingface dataset:
Package: tpqtlib
Collection of Python modules to make your life easier when working with PySide/PyQt.Also, when working with specific DCCs, tpQtLib will auto import proper modules and will use
DCC specific implementations for PySide/PyQt widgets.InstallationManual1. Clone/Download tpQtLib anywhere in your PC (If you download the repo, you will need to extract
the contents of the .zip file).CopytpQtLibfolder located insidesourcefolder in a path added tosys.pathAutomaticAutomatic installation for tpQtLib is not finished yet.DCC ImplementationsAt this moment following DCCs are supported:3ds Max:https://github.com/tpoveda/tpMaxLibMaya:https://github.com/tpoveda/tpMayaLibHoudini:https://github.com/tpoveda/tpHoudiniLibNuke:https://github.com/tpoveda/tpNukeLibBlender:Work in ProgressDuring tpQtLib initialization, if DCC specific implementation package is found in sys.path, tpQtLib
will automatically detect it and will import it.UsageInitialization CodetpQtLib must be initialized before being used.importtpQtLibtpQtLib.init()ReloadingFor development purposes, you can enable reloading system, so
you can reload tpQtLib sources without the necessity of restarting
your Python session. Useful when working with DCCs.importtpQtLibreload(tpQtLib)tpQtLib.init(True)Enabling debug logBy default, tpQtLib logger only logs warning messages. To enable all log messages
you can set TPQTLIB_DEV environment variables to ‘True’importosos.environ['TPQTLIB_DEV']='True'importtpQtLibtpQtLib.init()Deploying new version (only for devs)Update versionMake suresetup.cfgfile version field is updatedInstalling librariesMake sure that you have installed the following packages:wheelpip install wheeltwinepip install twinesetuptoolspip install setuptoolsMake sure to update setuptools to latest available version:pip install setuptools --upgradeGenerate wheelpython setup.py sdist bdist_wheelValidate wheel package generationtwine check dist/*Upload package to PyPitwine upload dist/*
Package: tprenamer
Renamer Tool to easily rename DCC objects in a visual wayDependenciestpPyUtils:https://github.com/tpoveda/tpPyUtilstpDccLib:https://github.com/tpoveda/tpDccLibtpQtLib:https://github.com/tpoveda/tpQtLibDepending on the DCC you are going to use you will need to download and install one of the following repositories:3ds Max:https://github.com/tpoveda/tpMaxLibMaya:https://github.com/tpoveda/tpMayaLibHoudini:Work in ProgressNuke:Work in ProgressBlender:Work in ProgressInstallationManualClone/Download tpRenamer dependencies Python packages and follow instructions to install them.Clone/Download tpRenamer anywhere in your PC (If you download the repo, you will need to extract the contents of the .zip file).CopytpRenamerfolder located insidesourcefolder in a path added tosys.pathAutomaticAutomatic installation for tpRenamer is not finished yet.UsageInitialization CodeimporttpPyUtilstpPyUtils.init()importtpDccLibtpDccLib.init()importtpQtLibtpQtLib.init()importtpNameIttpNameIt.init()importtpRenamertpRenamer.run()
Package: tprint
tprintPrint python collections as tablesExamplefromtprintimporttprintdata={"a":(100,2,3,4),"b":(1,200,3,4),"c":(1,2,300,4),"d":(1,2,3,400),}# as rowstprint(data)"""0 | 1   | 2   | 3   | 4--+-----+-----+-----+----a | 100 | 2   | 3   | 4b | 1   | 200 | 3   | 4c | 1   | 2   | 300 | 4d | 1   | 2   | 3   | 400"""# as columnstprint(data,as_columns=True)"""a   | b   | c   | d----+-----+-----+----100 | 1   | 1   | 12   | 200 | 2   | 23   | 3   | 300 | 34   | 4   | 4   | 400"""DocstprintThis is effectivly just the following snippet:deftprint(*args,**kwargs):print(tformat(*args,**kwargs))tformatdeftformat(data:Union[Dict[str,Sequence],Sequence],*,as_columns:bool=False,headers:Optional[Sequence[str]]=None,alignments:Optional[str]=None,formattings:Optional[str]=None,)->str:"""Format a dictionary or list as a table.:param data: table data:param as_columns: interpret given data as collection of columnsinstead of as rows:param headers: sequence of column headers(Defaults to enumerating the columns if headerscan't be extracted from data):param alignments: string with alignments per column(Defaults to left alignment for all columnsor those skipped with spaces):param formattings: string with formattings per column(Defaults to string casting for all columnsor those skipped with spaces)"""...
Package: tpro
tproTranscript Processing!tprotakes JSON-formatted transcripts produced by
various speech-to-text services and converts them to various standardized
formats.Installation and UsageNon-pip Requirement:  Stanford NER JARdownload and unzipthisput these files in in /usr/local/bin/:stanford-ner.jarclassifiers/english.all.3class.distsim.crf.ser.gzyou might have toupdate Javaon LinuxPip$ pip install tproUsage$ tpro --help

Usage: tpro [OPTIONS] TRANSCRIPT_DATA_PATH OUTPUT_PATH
        [amazon|gentle|speechmatics|google] [universal|vo]

Options:
  -p, --print-output    pretty print the transcript, breaks pipeability
  --language-code TEXT  specify language, defaults to en-US.
  --help                Show this message and exit.Example$ cat transcript.json 

  { "job": {
      "lang": "en",
      "user_id": 2152310,
      "name": "recording.mp4",
      "duration": 7,
      "created_at": "Mon Nov 12 14:57:06 2018",
      "id": 9871364
    },
    "speakers": [
      {
        "duration": "6.87",
        "confidence": null,
        "name": "M2",
        "time": "5.98"
      }
    ],
    "words": [
      {
        "duration": "0.13",
        "confidence": "0.670",
        "name": "Hello",
        "time": "5.98"
      },
      {
        "duration": "0.45",
        "confidence": "1.000",
        "name": "there",
        "time": "6.14"
      }
  ]

} 

$ tpro transcript.json converted_transcript.json speechmatics universal_transcript

[
    {
        "start": 5.98,
        "end": 6.11,
        "confidence": 0.67,
        "word": "Hello",
        "always_capitalized": false,
        "punc_after": false,
        "punc_before": false
    },
    {
        "start": 6.14,
        "end": 6.59,
        "confidence": 1.0,
        "word": "there",
        "always_capitalized": false,
        "punc_after": false,
        "punc_before": false
    }
]

☝☝☝ There\'s your transcript, which was saved to converted_transcript.json.STT ServicesSpeechmaticsAmazon TranscribeGentleGoogle Cloud Speech-to-TextPlannedWatsonMozilla's new open-source STT thingOutput FormatsUniversal Transcript(JSON)viraloverlay(JSON)PlannedWord (.doc,.docx)text filesSRT (subtitles)Draft.js JSON
Package: tprofile
Usage:======use `tprofile.ProfileMeta' as the metaclass of handler subclass or base handler class.Example:..code::pythonimport tornado.webfrom tprofile import ProfileMetadef condition(self):return True if self.get_argument('profile', None) == "1" else FalseProfileMeta.set_condition(condition)class BaseHandler(tornado.web.RequestHandler):__metaclass__ = ProfileMetaclass MainHandler(BaseHandler):def get(self):pass
Package: tprogress
tprogressSimple time capturing for python, that logs the time passed since last call with logger level.DEBUG.How-toTime LoggingCreate tprogress class instance, e.g.timep = tprogress.progress(). Starts the clock.Calltprogress.elapsed_time (self,stage='')that returns a formatted string with time and in debug mode the time.
The argumentstageis a string that can be used to state at what stage the program is when called.ExampleDEBUG:tprogress:Elapsed Time:   1m 0.683s (  0: 1:0.7) - Backward Calculation of RankTime MonitoringCreate tprogress class instance, e.g.timep = tprogress.progress(max_num=100,step_size=1,freq=5). Starts the clock.Calltprogress.monitor()within a largefor-loop that tells about how far program is and predicts
when the loop is finished. The setting is done in theinitcall:max_num: Number of loops, e.g. 100step_size: Size of step, e.g. 1freq: How often an info print should be done, e.g. 5, every 5th loop iteration
Package: tprojection
This library allows you to visually inspect the relation between a dependent variable (the
target) and a predictor in a meaningful way. This library is particularly convenient when the
target and/or the predictor are categorical, ie when it is difficult to compute a traditionnal correlation coefficient.
And by the way, Tprojection stands for target projection.Installationpip install tprojectionBasic usagefrom tprojection import Tprojection

tproj = Tprojection(df, "target", "predictor")
tproj.plot()Advanced usageYou can find several examples depicting more advanced functionalities inexamples/examples.ipynbDocumentationPlease find a light documentation [here](https://tprojection.readthedocs.io/en/latest/?)CreditsThis package was created withCookiecutterand thecookiecutter-pypackageproject template.
Package: tproj-zombie110year
tprojtproj 用于管理用户创建的项目模板，一个项目所需的起始文件、目录被包含在一个 zip 格式的压缩包中。install通过源码安装gitclonehttps://github.com/zombie110year/tproj-py.gitcdtproj-py&&pythonsetup.pyinstall通过 pip 安装pipinstalltproj-zombie110year通过 pipx 安装更推荐使用此方法， pipx 是一个用于安装基于 Python 的应用程序的包管理器，相比 pip
它会创建独立的虚拟环境，不会污染当前 Python 环境。pipxinstalltproj-zombie110yearusagetproj 提供名为 tproj 的可执行文件，可以：根据当前目录内容创建归档并自动重命名、
移动至数据目录中，应用模板归档创建项目，浏览已有的归档。创建模板归档创建归档使用tproj create子命令，默认将当前目录下的所有文件以及子目录中的文件递归地添加到归档中。
并且通过 stdin 询问归档名称，最终保存到$TPROJ_HOME/template目录下。除此之外还提供以下参数：-n/--name直接在命令行参数中指定归档名（将会覆盖 tproj.cfg 中的设置）-f/--force覆盖同名归档（默认取消创建）这要求在项目的根目录下存在一个名为tproj.cfg的文件，在这个文件中定义此模板的相关参数。
可配置的各键值对参考tproj.cfg.示例tprojcreate-nhelloworld应用模板归档应用指定的归档，使用tproj apply子命令，扫描$TPROJ_HOME/template目录下的模板归档，将其解压至当前工作目录。具有特性不输入 .zip 后缀，程序会自动添加，如果输入了 .zip 后缀，那么 tproj 将会寻找名称类似于 *.zip.zip 的归档不需要输入完整的模板名，只要输入足够唯一确定一个模板的名字即可，但如果输入对应了多个模板，程序将终止除此之外还提供以下参数：-O/--out-dir解压至指定目录，而非当前工作目录（不存在会创建）示例mkdirhelloworldcdhelloworld
tprojapplyhelloworld浏览已有模板浏览已有模板，使用tproj ls子命令，将列出$TRPOJ_HOME/template下的归档文件（去除 .zip 后缀）示例tprojls参考资料文件系统tproj 的所有数据文件都保存在特定的目录中，将这个目录所在的路径称为TPROJ_HOME，
可以修改环境变量TPROJ_HOME来修改此目录的路径，如果未设置此环境变量，那么将使用以下默认值：对于 Windows 系统，默认使用%APPDATA%\tproj对于 Linux 系统，遵守 XDG 规范，默认使用$XDG_DATA_HOME/tproj有些 Linux 没有提供$XDG_*变量，那么就直接使用~/.local/share/tproj归档文件存储在$TPROJ_HOME/template目录下配置文件存储为文件$TPROJ_HOME/tproj.conf（TOOD）tproj 包一个 tproj 包实质上是一个用过 Deflate 算法压缩的归档文件，并且以.zip为后缀名。
除了用tproj create -n ...来创建之外，也可以使用你喜欢的任何压缩工具来打包。
项目所需的文件原样存储在归档的 "根目录" 中，并在应用时原样解压到当前工作目录或tproj apply -d ...指定的目录中。tproj.yml键类型默认值含义namestr""此模板的名字authorstr""模板的作者，name<email>includeList[str]["**/*"]默认包含所有文件、子目录excludeList[str][".git/**/*"]被排除的文件descriptionstr（可选）一段对该模板的描述示例name:"helloworld"author:"zombie110year<zombie110year@outlook.com>"include:-"README.md"-".gitignore"-"src/**"-"tests/**"
Package: tpsisar18
This is a simple package to use topsis(technique for Order of preference by Similarity to Ideal Solution)
approach to select best among many things based on different attributes, such as selecting the best
Machine Learning algorithms based on correlation,R-square,root mean square error and accuracy.
You can use this simple package to get ranks on basis of topsis approach, ypu simply need to pass
NumPy array after pre-processing your data along with weights between 0 and 1 and all weights should
sum to 1 to get correct results and you need to pass impacts as well in form of '+' or '-'
where '+' indicates that you need to maximise value of that particular column and '-' indicates that
you need to minimise value of that particular column.
After passing all these parameters you will get your rank row.
Package: tpsp
TPSP: Transporte Público de São Paulotpsp(acrônimo para "Transporte Público de São Paulo") é uma pequena aplicação
escrita em Python cujo objetivo é exibir o estado atual das linhas daCPTMeMetro.AVISO: Este projeto não possui relações com o Estado de São Paulo, a CPTM, o
Metro ou qualquer outro órgão governamental.SumárioRequerimentosDependênciasFuncionamentoUsoRequerimentosPython (3.6.1 ou superior).Dependênciasrequests-htmlcoloramatabulateFuncionamentoNão há, até a data de publicação desta aplicação, uma API pública para os
serviços de trem e metrô do Estado de São Paulo. Os dados utilizados por esta
aplicação são obtidos medianteweb scrapingdas páginas online dos serviços.O fluxo é relativamente simples:O parser recebe os argumentos e flags da linha de comando;Uma requisiçãoGETé enviada à página do serviço a ser consultado;O conteúdoHTMLé analisado, quebrado a partir da estrutura e filtrado;os trechos de interesse do conteúdo (asdivs/.classque guardam os
nomes das linhas e seus respectivo status) são retidos;Os dados das linhas são formatados e impressos na tela.Cada resultado é filtrado e manipulado a partir das especificidades da construção
da página web do serviço. Apesar de contraproducente, uma vez que mudanças na
contrução das páginas podem (e vão) quebrar a funcionalidade, este foi o melhor
approach encontrado.Instalação$pip3installtpspUsopositional arguments:
  {cptm,metro}   the public transportation service

optional arguments:
  -h, --help     show this help message and exit
  -v, --version  show the program version and exit
  -j, --json     show the output in JSON format
  --copyright    show the copyright information and exit

examples:
    $ tpsp cptm
    # => shows the current state of all CPTM lines

    $ tpsp metro --json
    # => shows the current state of all Metro lines and formats
         the output in JSONLicençaNa medida do possível sob a lei,Caian Rais Ertlrenunciou atodos os
direitos autorais e direitos relacionados ou adjacentes a este trabalho. No
espírito daliberdade de informação, encorajo você a forkar, modificar,
alterar, compartilhar ou fazer o que quiser com este projeto!^ C ^ V
Package: tptapi
This package interacts withThe Powder Toy’s API.Usage:importtptapiclient=tptapi.Client()LoginMost actions need a session token you can obtain from Login:client.login(user, password)returns a boolean.Check LoginTo verify that your session is still valid you can run checkLogin:client.check_login()which will return a boolean.Vote [LOGIN NEEDED]To cast a vote, you need to doclient.vote(id, type)where type is a
negative or positive number that defines if it’s a upvote or downvote.
Returns a boolean.Comment [LOGIN NEEDED]To add a comment, you need to doclient.comment(id, text). Returns a
boolean.Add Tag [LOGIN NEEDED]To add a tag, you need to doclient.add_tag(id, name). Returns a
boolean.Delete Tag [LOGIN NEEDED]To remove a tag, you need to doclient.delete_tag(id, name). Returns a
boolean.Delete Save [LOGIN NEEDED]To remove a save, you need to doclient.delete_save(id). Returns a
boolean.Unpublish Save [LOGIN NEEDED]To unpublish a save, you need to doclient.unpublish_tag(id). Returns
a boolean.Publish Save [LOGIN NEEDED]To publish a save, you need to doclient.publish_save(id). Returns a
boolean.Update Profile [LOGIN NEEDED]To update your profile, you need to doclient.set_profile(data).
Returns a boolean.Browse [LOGIN ENHACES OUTPUT]To browse, you need to doclient.browse(query, count, offset).
Returns results.List TagsTo list tags, you need to doclient.list_tags(start, count). Returns
array of tags.Favourite a Save [LOGIN NEEDED]To add a save to favutrites, you need to doclient.add_fav(id). Returns
a boolean.UnFavourite a Save [LOGIN NEEDED]To remove a save from favutrites, you need to doclient.remove_fav(id).
Returns a boolean.Save [LOGIN NEEDED]Saves a CPS file. Data has to be OPS1-encoded save.data=open("save.cps","br").read()client.save(name,description,data)Returns the save id.Update Save [LOGIN NEEDED]Updates a save with new description and content. Data has to be
OPS1-encoded save.data=open("save.cps","br").read()client.updateSave(id,description,data)Returns a boolean.Startup Data [LOGIN ENHACES OUTPUT]Returns Startup.json data.client.startup()Save’s CommentsReturns a save’s comments.client.comments(id, count, offset)
Package: tp-timesheet
TP-TimesheetAutomated submission of TP timesheets.Version 0.x.x is web form onlyVersion 1.x.x is web + ClockifyVersion 2.x.x is Clockify onlyInstallationInstallThis tool is published on pypi so from any terminal with python3 installed run:pipinstalltp-timesheetNote:some outdated OS's still require pip3 instead of pip. If you get an errorUnknown command: pipthen try the following:pip3installtp-timesheetUpgradepipinstall--upgradetp-timesheetUninstallpipuninstalltp-timesheetUsageRun the tool with the help option to find out additional usage informationtp-timesheet--helpCommon cli options# submit live hours (default) for todaytp-timesheet--starttoday# submit live hours (default) for next 5 daystp-timesheet--starttoday--count5# submit for Mon 3/10/22 to Thursday 6/10/22 with a task of Out Of Office (OOO)tp-timesheet--start'3/10/22'--count5-tOOO# Schedule the form to submit automatically on weekdaystp-timesheet--automateweekdays# append '--verbose' to any command to get more log messages about what is going on# append '--dry-run' to any command to avoid clicking submit. Good for testingDevelopmentInstall the dev environment and run tool locally:pipinstall-e.[dev]tp-timesheet--helpnote: if zsh is used, install the dev environment with:pipinstall-e".[dev]"ContributingTo run checks prior to committingblack--check--difftp_timesheet# See what formatting changes need to be madeblacktp_timesheet# Run formatterpylinttp_timesheet# Run linterpytest# Run testing
Package: tptool
OverviewESP32-S2/S3 can support up to 14 capacitive touch sensor, which can be used to design touch buttons, sliders, etc. For a long time touchpad (touchsensor) debugging and driver configuration has been a difficult problem. But now we're trying to make it easier and interesting, now several touchpad debugging tools and methods for ESP chip is under developing.We've made some progress so far, and would like to sharing a Beta version of our touchpad debugging tool (tptool) with you. This version support:Modification of all Touch parameters in real time, including thresholds, filter parameters, and more.Plot all Touch channel's data (raw, smooth, baseline, diff) in real time,Control and log hardware events in tableEnter sleep mode with one button, the wakeup channel parameters can be configuredWireless debugging, using ESP-NOW protocol (A second ESP32XX module is required as a receiver)Save the original data on the local machine for further analysisMore...If you're interested, please try it. (There could be some issues in current Beta version, if you encounter any problems, feel free to add your questions tohttps://esp32.com/How to run the GUIInstall from pip usingpip install tptoolRun GUI usingpython -m tptoolNoteCurrently supported SoC:ESP32-S2ESP32-S3Currently only supports using theDebug Firmwarefor touchsensor debugging, please using any ESP download tool then following the below configuration for download.Debug FirmwareUser guide:ESP_TouchSensor_Debugging_Tool(Beta).pdf
Package: tptp
A TPTP python packageInstallationinstall python >= 3.5clone this repositoryUsageRun locally:List all solverspython3 -m tptp local list-solversRun Leo III.$ python3 -m tptp local run --solver-name leo3 --problem problems/SYN001+1.p --timeout 60Run cvc4$ python3 -m tptp local run --solver-name cvc4 --problem problems/SYN001+1.p --timeout 60Run System-on-TPTP:List all solvers$ python3 -m tptp system-on-tptp list-solversRun Leo III.$ python3 -m tptp system-on-tptp request --solver-name "Leo-III---1.4" --solver-command "run_Leo-III %s %d" --problem "problems/SYN001+1.p"Test the competition mode$ python3 -m tptp competition competition-test/definition.pyRun with more output. Good for error tracking.$ python3 -m tptp competition competition-test/definition.py --verboseMaking a solver TPTP readySZS Status, SZS OntologyA solver can be used by this libary if it supports the SZS Ontology as its result on thestdout.The solution status should be reported exactly once, in a line starting% SZS status"(the leading '%' makes the line into a TPTP language comment).For instance, a SAT-solver started for problemSYN001+1should output the line% SZS status Satisfiable for SYN001+1as part of its output if it proves the problem issatisfiable.Consequently:if your solver proves the problem isunsatisfiable, the line% SZS status Unsatisfiable for SYN001+1.if your solvergaves upthe prove, the line% SZS status GaveUp for SYN001+1.a full list of possible values for the SZS Status can be found at theSZS Ontologydefinition.Any prove or prove-model should additionally be printed on the stdout in the following form.% SZS output start CNFRefutation for SYN001+1
  ...
% SZS output end CNFRefutation for SYN001+1Test your solverTo test your solve add it to our test competition, which can be found atcompetition-test/definition.pyHere you add your solver to theSOLVERSconstantSOLVERS=(...{'type':'local','name':'my-solver','pretty-name':'MySolver''command':'my-solver-binary-or-shell-script%s-t%d',},...)This will ensure that your solveris calledmy-solverand is pretty printedMySolverwhen invoked to solve the problemabsolute/path/SYN001+1.pwithin a timeout of60seconds the
program/shellscriptmy-solver-binary-or-shell-script "absolute/path/SYN001+1.p" -t 60is calledyour problem should output an SZS Status from the SZS Ontology (see above)Running our test competition should now list your solver.$ python3 -m tptp competition competition-test/definition.py
...
% Satisfiable for Sat1.cnf expecting Satisfiable with MySolver -t 60s which is correct
...
Package: tptp-lark-parser
tptp-lark-parserAttention!The project is inactive.tptp-lark-parseris a parser for theTPTP librarylanguage using theLark parser. For now, only CNF
sublanguage is supported.How to InstallThe best way to install this package is to usepip:pipinstalltptp-lark-parserThe package is also available onconda-forge:condainstall-cconda-forgetptp-lark-parserOne can also run it in a Docker container:dockerbuild-ttptp-lark-parserhttps://github.com/inpefess/tptp-lark-parser.gitdockerrun-it--rm-p8888:8888tptp-lark-parserjupyter-lab--ip=0.0.0.0--port=8888How to Usefromtptp_lark_parserimportTPTPParsertptp_parser=TPTPParser()parsed_text=tptp_parser.parse("cnf(test, axiom, ~ p(Y, X) | q(X, Y)).")clause_literals=parsed_text[0].literalsSeethe
notebookor run it inBinderfor more information.More DocumentationMore documentation can be foundhere.Similar ProjectsThere are many TPTP parsers in different languages:C(by TPTP’s creator Geoff Sutcliffe)JavaC++JavaScriptRustCommon LispScalaHaskellOCamlThere also isanother parser in Pythoncontaining only the Lark syntax file.How to CiteIf you want to cite thetptp-lark-parserin your research paper,
please use the following doi:https://doi.org/10.5281/zenodo.7040540.How to ContributePlease readthe Code of Conductand then followthe contribution guide.
Package: tpu
TPU - Tab Processing UnitTPU is a set of awesome scripts that aim to facilitate musicians with programming background ;)Supported FeaturesTab Transposition with Semitone OffsetUpcoming FeaturesTab Transposition with Single Chord BaseFAQWho is this package intended for?Hmm, anyone interested really... :DTPU is intended to be used bymusiciansbut its core goal includes defining a more general processing unit for several tab related operations as well. You can think of it as abuilding blockfor greater stuff!Can I contribute?Definitely! Pull Requests are always more than welcome!What kind of help is needed?Honestly, there are no constraints... Found a bug? Came up with a super cool idea for a new feature? Wanna help out with documentation? Can write some more testing?YES, PLEASE!
Package: tpu-index
TPU IndexTPU Index is a package for fast similarity search over large collections of high dimension vectors on TPUs.
This package was built to support our project that we developed forhttps://tfworld.devpost.com/.Link to our project:https://devpost.com/software/naturallanguagerecommendationsInstallation!pip install tpu-indexBasic usagefrom tpu_index import TPUIndex

index = TPUIndex(num_tpu_cores=8)
index.create_index(vectors)  # vectors = numpy array, shape == [None, None]

...
D, I = index.search(xq, distance_metric='cosine', top_k=5)ToDo:Add more distance metricsOptional GPU support