Saravanan666/pypi-org huggingface dataset:
Package: tacle
TaCle: Tabular Constraint LearnerTaCLe is a constraint learner designed for usage in spreadsheets and tabular data.Installationpip install tacleUsing TaCLeRunning TaCLe from command lineTaCLe can be used from command line to analyze a CSV file:python -m tacle data/magic_ice_cream.csvThis returns a list of constraints in the file:ALLDIFFERENT(T2[:, 1])
ALLDIFFERENT(T1[:, 4])
ALLDIFFERENT(T1[:, 5])
ALLDIFFERENT(T1[:, 6])
T1[:, 1] -> T2[:, 1]
T1[:, 6] = SUM(T1[:, 3:5], row)Filter outputIf you are interested in specific types of constraints, you can filter the output:python -m tacle data/magic_ice_cream.csv -f "foreign-key"  # Report only foreign keys
python -m tacle data/magic_ice_cream.csv -f "<f>"  # Report only formulas -- use <c> for only constraintsTablesTo view the tables that TaCLe finds in a file you can run:python -m tacle data/magic_ice_cream.csv -tThis returns the tables and blocks found by TaCLe:Table T1, (1:9, 0:7)
Columns 0-2 (string), Columns 2-6 (int), Columns 6-7 (string)

Table T2, (11:15, 0:2)
Columns 0-1 (string), Columns 1-2 (int)PapersRead more about how TaCLe works in one of our two papersjournal version,demo paper.
Package: tacl-extra
tacl-extra provides scripts and libraries that make use of theTACLsoftware.Scripts provided are:int-all: Generates extended and reduced intersect results files
for every pair of texts in a supplied corpus.jitc: Generates an HTML report showing the amount of overlap
between a set of works, ignoring those parts that overlap with
works in a second set of works.lifetime: Generates results data and a report showing the
lifetime of n-grams that come into or fall out of use in a group of
corpora.paternity: Generates a series of results files giving the
n-grams in common between one corpus and each work in a second
corpus, that are not present in a third corpus.The actual work of the scripts is done in library code that can be
imported and used by other code.The code is developed athttps://github.com/ajenhl/tacl-extra/and the
documentation is available athttp://tacl-extra.readthedocs.io/en/latest/.
Package: ta-clickup
================
ta-bitwarden-cliThoughtful Automation BitWarden CLI installation packageInstallationFor correct work please use python virtualenv approach!
Also use--no-cache-diroption.
Otherwise it would cache package wheel and wont downloadbwCLI binary anymorepython3 -m virtualenv venv
source venv/bin/activate
pip install --no-cache-dir ta-bitwarden-cliCode above will additionally installbwCLI binary to a first available folder in the $PATHExample Usageimportosfromta_clickupimportClickUpcredentials={'login':os.getenv("CLICKUP_LOGIN"),'password':os.getenv("CLICKUP_PASSWORD"),'API Key':os.getenv("CLICKUP_API_KEY")}clickup=ClickUp(credentials)DevelopmentPrepare local dev envpython3 -m virtualenv venv
source venv/bin/activate
pip install -r requirements.txt
Package: ta-cmi
TA-CMIA Python wrapper to read out  sensors from Technische Alternative using the C.M.I.How to use packageJson APIimportasynciofromta_cmiimportCMI,Languages,ApiError,RateLimitError,InvalidCredentialsError,InvalidDeviceError,ChannelTypeasyncdefmain():try:cmi=CMI("http://192.168.1.101","admin","admin")devices=awaitcmi.get_devices()device=devices[0]# Set type automaticallyawaitdevice.fetch_type()# Set type manuallydevice.set_device_type("UVR16x2")awaitdevice.update()print(str(device))inputChannels=device.get_channels(ChannelType.INPUT)outputChannels=device.get_channels(ChannelType.OUTPUT)analogLogging=device.get_channels(ChannelType.ANALOG_LOGGING)foriininputChannels:ch=inputChannels.get(i)print(str(ch))foroinoutputChannels:ch=outputChannels.get(o)print(f"{str(ch)}-{ch.get_unit(Languages.DE)}")foralinanalogLogging:ch=analogLogging.get(al)print(f"{str(ch)}-{ch.get_unit(Languages.DE)}")except(ApiError,RateLimitError,InvalidCredentialsError,InvalidDeviceError)aserror:print(f"Error:{error}")loop=asyncio.get_event_loop()loop.run_until_complete(main())loop.close()Supported data typesDevice typeInputsOutputsDL-inputsSystem-values: GeneralSystem-values: DateSystem-values: TimeSystem-values: SunSystem-values: Electrical powerAnalog network inputsDigital network inputsM-BusModbusKNXAnalog loggingDigital loggingUVR1611✔✔❌❌❌❌❌❌✔✔❌❌❌❌❌UVR16x2✔✔✔✔✔✔✔❌❌❌❌❌❌✔✔RSM610✔✔✔❌❌❌❌❌❌❌✔❌❌❌❌CAN-I/O45✔✔✔❌❌❌❌❌❌❌❌❌❌❌❌CAN-EZ2✔✔❌❌❌❌❌✔❌❌❌❌❌❌❌CAN-MTx2✔✔❌❌❌❌❌❌❌❌❌❌❌❌❌CAN-BC2❌❌❌❌❌❌❌❌❌❌✔✔✔❌❌UVR65✔✔❌❌❌❌❌❌❌❌❌❌❌❌❌CAN-EZ3❌❌✔✔✔✔✔✔❌❌❌✔❌✔✔UVR610✔✔✔❌❌❌❌❌❌❌✔❌❌✔✔UVR67✔✔❌❌❌❌❌❌❌❌❌❌❌❌❌NoteThe supported data types may differ from the official API. If a device type supports other data types than listed here, please create an issue.CoE ServerData can be retrieved usingthisCoE to HTTP serverimportasynciofromta_cmiimport(ApiError,ChannelMode,CoE,InvalidCredentialsError,InvalidDeviceError,Languages,RateLimitError,)asyncdefmain():try:coe=CoE("http://192.168.2.201:9000")can_id=42awaitcoe.update(can_id)analog_channels=coe.get_channels(can_id,ChannelMode.ANALOG)digital_channels=coe.get_channels(can_id,ChannelMode.DIGITAL)foriinanalog_channels:ch=analog_channels.get(i)print(str(ch))foroindigital_channels:ch=digital_channels.get(o)print(f"{str(ch)}-{ch.get_unit(Languages.DE)}")except(ApiError,RateLimitError,InvalidCredentialsError,InvalidDeviceError)aserror:print(f"Error:{error}")loop=asyncio.get_event_loop()loop.run_until_complete(main())loop.close()
Package: taco-box
Tiling and Corruption (TACo)TACois a simple and effective data augmentation technique for the task of Optical Character Recognition (OCR) or Handwritten Text Recognition (HTR) (check reference).And,taco-boxis an implementation ofTACoalgorithm. This is currently under the Apache 2.0, Please feel free to use for your project. Enjoy!InstallingFirst, you need to havepython 3installed in your system.Next, you can Installtaco-boxwithpipor your favorite PyPi package manager.pipinstalltaco-boxUsageCheckout this jupyter notebook on usage -NotebookHere is an example:fromtacoboximportTaco# creating Taco object. (Note: parameters are at their default value.)mytaco=Taco(cp_vertical=0.25,cp_horizontal=0.25,max_tw_vertical=100,min_tw_vertical=20,max_tw_horizontal=50,min_tw_horizontal=10)# apply random vertical corruptionaugmented_img=mytaco.apply_vertical_taco(input_img,corruption_type='random')mytaco.visualize(augmented_img)-------Understanding Arguments--------
    :cp_vertical:        corruption probability of vertical tiles
    :cp_horizontal:      corruption probability for horizontal tiles
    :max_tw_vertical:    maximum possible tile width for vertical tiles in pixels
    :min_tw_vertical:    minimum tile width for vertical tiles in pixels
    :max_tw_horizontal:  maximum possible tile width for horizontal tiles in pixels
    :min_tw_horizontal:  minimum tile width for horizontal tiles in pixelsExpected resultsBelow picture shows the variations ofTACoaugmentation algorithm from current implementation:-ContributingThis project is in very early stages of development. If there is an issue or feature request, feel free to open an issue. Additionally, a PR is always welcome.ReferenceTACo algorithm is part of a research project on Handwritten Text Recognition. Link to the original paper will be posted soon!!
Package: tacocapstone-prebuilt
1. To install pure Python binding on *nix, run the command below:$ sudo make installTo install Python3 binding package, run the command below:(Note: this requires python3 installed in your machine)$ sudo make install32. For better Python performance, install cython-based binding with:$ sudo make install_cythonNote that this requires cython installed in your machine first.To install cython, see section 3 below.3. To install cython, you have to ensure that the header filesand the static library for Python are installed beforehand.E.g. on Ubuntu, do:$ sudo apt-get install python-devDepending on if you already have pip or easy_installinstalled, install cython with either:$ sudo pip install cythonor:$ sudo easy_install cythonNOTE: Depending on your distribution you might also be able toinstall the required cython version using your repository.E.g. on Ubuntu, do:$ sudo apt-get install cythonHowever, our cython-based binding requires cython version 0.19 or newer,but sometimes distributions only provide older version. Make sure toverify the current installed version before going into section 2 above.E.g, on Ubuntu, you can verify the current cython version with:$ apt-cache policy cythonWhich should at least print version 0.19This directory contains some test code to show how to use Capstone API.- test.pyThis code shows the most simple form of API where we only want to get basicinformation out of disassembled instruction, such as address, mnemonic andoperand string.- test_lite.pySimilarly to test.py, but this code shows how to use disasm_lite(), a lightermethod to disassemble binary. Unlike disasm() API (used by test.py), which returnsCsInsn objects, this API just returns tuples of (address, size, mnemonic, op_str).The main reason for using this API is better performance: disasm_lite() is at least20% faster than disasm(). Memory usage is also less. So if you just need basicinformation out of disassembler, use disasm_lite() instead of disasm().- test_detail.py:This code shows how to access to architecture-neutral information in disassembledinstructions, such as implicit registers read/written, or groups of instructionsthat this instruction belong to.- test_<arch>.pyThese code show how to access architecture-specific information for eacharchitecture.2. To install Python binding on Windows:Recommended method:Use the Python module installer for 32/64 bit Windows from:http://www.capstone-engine.org/download.htmlManual method:If the module installer fails to locate your Python install, or if you haveadditional Python installs (e.g. Anaconda / virtualenv), run the followingcommand in command prompt:C:\> C:\location_to_python\python.exe setup.py installNext, copy capstone.dll from the 'Core engine for Windows' package availableon the same Capstone download page and paste it in the path:C:\location_to_python\Lib\site-packages\capstone\
Package: ta-core
ta-coreThoughtful Automation Core package. This package contains the function required for Thoughtful Automation bots. Documentation may be closed and certain functions may not work outside the Thoughtful Automation environment.Example UsageTo useStatusyou need to install the libraryrpaframeworkand get WorkItems fromrobocorpenvironment.from ta_core.status import Status

Status.change_status_to_warning()
Package: tacosdedatos-utils
Bienvenidx a la documentación detacosdedatos-utilsUna colección de herramientas para facilitar el análisis y visualización de datos por@tacosdedatos.Que traela funciónarbolpara mostrar los contenidos de la carpeta que le pases.importtacosdedatos_utilsastddtdd.arbol("./notebooks")>>>>+notebooks+Cpp.ipynb+Data.ipynb+Fasta.ipynb+Lorenz.ipynb+R.ipynb+audio+audio.wav+bqplot.ipynb+images+marie.png+xeus-cling.png+xtensor.png+xwidgets.png+lorenz.py+pandas.ipynbla funcióncrear_proyectoimporttacosdedatos_utilsastddtdd.crear_proyecto(nombre="proyecto-de-analisis-de-datos")tdd.arbol("proyecto-de-analisis-de-datos/")>>>>+proyecto-de-analisis-de-datos+AUTORES.md+README.md+datos+brutos+externos+finales+interinos+procesados+docs+notebooks+reportes+figuras+src+apps+datos+externos+herramientas+modelos+visualizacionesque también funciona desde tu línea de comandos.📃 Citeishon@misc{tacosdedatos-utils,
  author = {tacosdedatos},
  title = {Una coleccion de herramientas para facilitar el analisis y visualizacion de datos por @tacosdedatos.},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/tacosdedatos/tacosdedatos-utils}}
}Este proyecto fue generado conpython-package-template.
Package: tacost
TACOST: Test ACOustic TrackingFor more on this package see the detailed documentationheretacostallows you to test and characterise the accuracy of your acoustic tracking system.
It allows you to create audio recordings to simulate sound emission from known points of your choice,
and uncover the maximum expected accuracy of your acoustic tracking system.What TACOST doesCreates audio files which simulate sound arrival at each microphone in an array from user-specified pointsAllows customisation of multiple parameters that may affect acoustic tracking accuracyWhat TACOST doesnotdoPerform the actual acoustic tracking. You need to use a separate system (eg. TOADSuite, Batalef, or the
tracking system of your choice)Simulate complex sound propagation (reflection, reverberation,etc). Sound is assumed to travel
in straight line paths. In version 0.0.1 there's no spherical spreading too!About the logoThe two T's in the logo are tristar arrays (shaped like T's) with four microphones each on them
(blue dots). The red dots are positions from where sound was emitted. In this case, I like to
imagine it to be a bat's calls as it flew past two arrays!Getting startedCreating an audio file to test your system is as simple as opening up your shell/command line and typing:python -m tacost -run_exampleThe command above will generate a four channel WAV file with 500 kHz sampling rate based on the default array geometry and source positions.
See the page on default array geometry and source position.Installationtacostis currently pre-PyPi and it'll be some time before you canpip install tacost!
However, youcanuse :code:tacostwith a local install! Headhereand download the repo. From the downloaded
repo open up a shell/prompt and typepip install ./.
Package: tacot
Installation$ pip install tacotQuick startDownloaddemo1.tar.gzarchive todemosfolder.$ mkdir demos; cd demos
$ curl -o demo1.tar.gz http://packages.python.org/tacot/en/_static/demo1.tar.gz
$ tar xfz demo1.tar.gz
$ cd demo1Web site generation :$ tacot
Please wait, tacot process 11 files :

jupiter.html
pluto.html
neptune.html
styles.css
index.html
saturn.html
uranus.html
earth.html
mars.html
venus.html
mercury.htmlThe site is generated in_buildfolder :$ ls _build/ -1
earth.html
index.html
jupiter.html
mars.html
mercury.html
neptune.html
pluto.html
saturn.html
styles.css
uranus.html
venus.htmlYou can see the web site with your browser :$ firefox _buildSeedocumentationfor more informations…News0.3.4 (2013-06-15)Fix bug0.3.3 (2013-06-15)Fix bug around current_page variable (new strip “.mako”)Append current_template variable0.3.2 (2013-06-14)Fix bug0.3.1 (2013-06-14)Now tacot copy and parse only newer source file. You can use -f option to force copy and parsing.0.3.0 (2013-06-11)Append –version argumentImprove unit tests (build system)Migrate to docoptAppend web assets support0.2.9 (2013-06-10)Fix bug around manifest path0.2.8 (2013-06-10)Fix bug around manifest pathAppend verbose function0.2.7 (2013-05-30)Improve source code, setup.py…Fix error around current working directory0.2.6 (2012-08-09)Remove some unused functions (Pablo Caro Revuelta)Fix some pep8 error (Pablo Caro Revuelta)Now Tacot can parse .html.mako files (Pablo Caro Revuelta)0.2.5 (2012-01-23)Fix error in navigation documentation section and in demo examples (thankshttps://twitter.com/#!/meldianen)Remove debug message in source code0.2.4 (2012-01-16)Now tacot copy hidden (.*) files, like .htaccessAppend MIT License0.2.3 (2011-10-22)Append global variable “g”0.2.2 (2011-10-18)Now auto reload feature didn’t use manifest, example tacot restart same if include files changes0.2.1 (2011-10-01)Fix mako dependency version issue0.2.0 (2011-09-17)Append auto reload feature0.1.1 (2011-09-11)Fix unicode bug0.1.0 (2011-06-04)First version
Package: tacotron2
Tacotron2 libraryThis Tacotron2 library was changed to be used with vait library.Original code, README.md and additional information:https://github.com/NVIDIA/tacotron2Instalation1) Install tacotron2 library(This will also install waveglow library)pipinstalltacotron2==22.12.282) Install CUDA 11.3 or 11.6pipinstall-rrequirements-cuda-11.3.txt# orpipinstall-rrequirements-cuda-11.6.txt3) Install apexgitclonehttps://github.com/NVIDIA/apex/home/${USER}/apexcd/home/${USER}/apex
pipinstall-v--disable-pip-version-check--no-cache-dir./cd-4) Download published model fileswgethttps://drive.google.com/file/d/1c5ZTuT7J08wLUoVZ2KkUs_VdZuJ86ZqA/view?usp=sharingUsage: Trainingtacotron2-train--output_directory=outdir--log_directory=logdir
tacotron2-train--output_directory=outdir--log_directory=logdir-ctacotron2_statedict.pt--warm_start
Package: tacotron2-model
tacotron2-modelA PyPI port of the NVIDIA Tacotron2 modelSource:https://github.com/NVIDIA/tacotron2(model.py)A pytorch install is required but is not added to requirements to avoid configuration issues.The only change from the NVIDIA original is a replacement of hparams with individual arguments.
This removes the dependency on tf.contrib.training.HParams (deprecated since tensorflow 1).Model usagefrom tacotron2_model import Tacotron2

model = Tacotron2().cuda()
print(model.eval())Loss usagefrom tacotron2_model import Tacotron2Loss

criterion = Tacotron2Loss()Collate usagefrom tacotron2_model import TextMelCollate

collate_fn = TextMelCollate()STFTfrom tacotron2_model import TacotronSTFT

stft = TacotronSTFT()
Package: tacpho.adapters
RPi-DeviceAdaptersRPi-DeviceAdapters is a tool for controlling microscope hardware with
theRaspberry Pi. It provides a common
software interface to different types of peripherals throughMicro-Manager, an open source software
package widely used in microscopy.RPi-DeviceAdapters contains the following tools:DeviceAdaptersReady-made Micro-Manager device adapters for various hardware peripheralsbuildA Docker image
for cross-compiling Micro-Manager for the Raspberry Pi's ARM processors on a x86 laptop or
workstationappA Docker image for
running the Micro-Manager Python wrapper on the Raspberry Pi and that can be easily downloaded
onto any Pi that has Docker installed on ittacpho.adaptersA Python package for easily working with the project's Docker images on the Raspberry PiIn addition, compiled device adapter libraries may be found in thereleasespage.QuickstartOn a Raspberry Pi that hasDockeralready
installed , create a file namedscript.pythat contains the
following lines:import MMCorePy
mmc = MMCorePy.CMMCore()
print(mmc.getVersionInfo())Run the following commands from a terminal window from the same Pi:$ pip3 install tacpho.adapters
$ mm.py pull
$ mm.py run script.pymm.py pullmay take several minutes to download the
application on the first use.Getting helpChatDocumentationExample scriptsComments, questions, and feedback may be posted in the Zulip chat room.Related pagesrpi-micromanagerThe project's Docker imagesMicro-ManagerOpen source microscopy software
Package: tact
TACT: Taxonomic Addition for Complete TreesTACT is a Python app for stochastic polytomy resolution. It uses birth-death-sampling estimators across an ultrametric phylogeny to generate branching times for unsampled taxa, using taxonomic information to compatibly place new taxa onto a backbone phylogeny.Getting started with TACTInstallingIntroduction and backgroundExample analysisTroubleshooting problemsCitationTACT is described more fully in its manuscript. If you use TACT, please cite:Chang, J., Rabosky, D. L., & Alfaro, M. E. (2019). Estimating diversification rates on incompletely-sampled phylogenies: theoretical concerns and practical solutions. Systematic Biology. doi:10.1093/sysbio/syz081TACT owes its existence to much foundational work in the area of stochastic polytomy resolution, namely PASTIS and CorSiM.Thomas, G. H., Hartmann, K., Jetz, W., Joy, J. B., Mimoto, A., & Mooers, A. O. (2013). PASTIS: an R package to facilitate phylogenetic assembly with soft taxonomic inferences. Methods in Ecology and Evolution, 4(11), 1011–1017. doi:10.1111/2041-210x.12117Cusimano, N., Stadler, T., & Renner, S. S. (2012). A New Method for Handling Missing Species in Diversification Analysis Applicable to Randomly or Nonrandomly Sampled Phylogenies. Systematic Biology, 61(5), 785–792. doi:10.1093/sysbio/sys031SponsorshipPlease consider sponsoring the ongoing maintenance of TACT viaGitHub Sponsors.Initial development was supported by a National Science Foundation Doctoral Dissertation Improvement Grant (DEB-1601830).
Package: tac-tasks-validation
tac_tasks_validationPerforms validation for tasks performed by TAC. Involves validating the data input like ONTsUsageImport the various validations fromtac_tasks_validationfromtac_tasks_validationimportOltToNewValidationvalidate=OltToNewValidation(old_ont='some_ont_sn',new_ont='another_ont_sn',logger=logger,# logging.Logger objectold_db_session=old_db_session,# sqlalchemy.orm.Session objectold_db_radius_session=old_db_radius_session,# sqlalchemy.orm.Session objectnew_db_session=new_db_session# sqlalchemy.orm.Session object)validate.validate_old_to_new_swap()
Package: tactic
Tactics2DAboutTactics2D is a Python library that provides diverse traffic environments, facilitating the development of driving decision-making models.Quick StartInstall from PyPIYou can simply install Tactics2D from PyPI with the following commandpipinstalltactics2dInstall from GithubYou can also install Tactics2D from from its source on GitHub.[TODO]If no errors occurs, you should have installed Tactics2D successfully.Samples[TODO: The command lines to run sample codes.]CitationIf you find Tactics2D useful, please cite this in your publication.@article{li2023tactics2d,title={Tactics2D: A Multi-agent Reinforcement Learning Environment for Driving Decision-making},author={Li, Yueyuan and Zhang, Songan and Jiang, Mingyang and Chen, Xingyuan and Yang, Ming},journal={arXiv preprint arXiv:2311.11058},year={2023}}
Package: tactics2d
Tactics2DAboutTactics2D is a Python library that provides diverse traffic environments, facilitating the development of driving decision-making models.Quick StartInstall from PyPIYou can simply install Tactics2D from PyPI with the following commandpipinstalltactics2dInstall from GithubYou can also install Tactics2D from from its source on GitHub.[TODO]If no errors occurs, you should have installed Tactics2D successfully.Samples[TODO: The command lines to run sample codes.]CitationIf you find Tactics2D useful, please cite this in your publication.@article{li2023tactics2d,title={Tactics2D: A Multi-agent Reinforcement Learning Environment for Driving Decision-making},author={Li, Yueyuan and Zhang, Songan and Jiang, Mingyang and Chen, Xingyuan and Yang, Ming},journal={arXiv preprint arXiv:2311.11058},year={2023}}
Package: tactigon-gear
Tactigon GearThis package enables the wearable device Tactigon Skin to connect to your python project using Bluetooth Low Energy.ArchitectureTactigon Gear environment has the following architecture:Server is located on the cloud and it is manteined by Next Industries s.r.l.
Server has a web interface where you can handle your profile and your data (models and gestures)Provided Tactigon Gear SDK is the implementation of Tactigon Gear environment client sideTactigon Gear SDK is used for collecting new raw data, send the data to server,
ask server to train a model using the raw data, and download the model from server.
Finally use the model for testing real-time gesture recognition.PrerequisitesIn order to use the Tactigon Gear SDK the following prerequisites needs to be observed:Python version: following versions has been used and tested. It is STRONGLY recommended to use these ones depending on platform.Win10: 3.8.7Linux: 3.8.5Mac osx: 3.8.10Raspberry: 3.7.3It is recommended to create a dedicated python virtual environment and install the packages into the virtual environment:python -m venv venvpip install tactigon-gearDepending on your installation (Linux, Raspberry, Mac users) you may need to usepython3andpip3instead ofpythonandpiprespectivelyLicensingIn order to perform new training and download them you need to register on following web side:https://www.thetactigon.com/ai/web/Once registration is done you can go to Profile section and click onJson Filebutton to download file user_data.json
The use of this file is described later in this doc.