Saravanan666/pypi-org huggingface dataset:
Package: ttlv
Welcome to tiktok-long-videoA useful package/tool to make webm/mp4 videos greater than 60s in length uploadable to TikTok.InstallationpipinstallttlvWeb ApplicationYou can also use the web app deployed on heroku if you don't want to install it. Link to repo:longify-tiktokLink to web app:LongifyTikTokProofThis videohttps://www.tiktok.com/@harrynepal26/video/6917917976008183042is actually 2 Minutes 45 Seconds long.
You can download this video usinggovue-tiktok-downloaderto confirm that video is more than 60 seconds long.UsageTo upload longer videos than 60 seconds, first you must have a webm/mp4 video file or you can convert the video to webm/mp4 using online or offline tools and you have to save using below commands and upload via Tiktok web not mobile app.You can use as cli tool.
For help,ttlv-hInput filepath is must and output filepath is optional. For saving tiktok long video,ttlv-i"/home/user/Desktop/sample.webm"-o"/home/user/Desktop/sample-ttlv.webm"ORYou can import and use in your programs.
output_path is optional.fromttlvimportVideovideo=Video(input_path="./sample.mp4",output_path="./sample-ttlv.mp4")video.save()Author👤Pawan PaudelGithub:@pawanpaudel93🤝 ContributingContributions, issues and feature requests are welcome!Feel free to checkissues page.Show your supportGive a ⭐️ if this project helped you!Copyright © 2021Pawan Paudel.
Package: ttml
ttmlTensor train based machine learning estimator.Uses existing machine learning estimators to initialize a tensor train
decomposition on a particular feature space discretization. Then this tensor
train is further optimized with Riemannian conjugate gradient descent.This library also implements much functionality related to tensor trains, and
Riemannian optimization of tensor trains. Finally there is some functionality
for turning decision trees and forests into CP/tensor trains.InstallationThettmlpython package can be installed usingpipby runningpip install ttmlor by cloning this repository and running the following command in the root directory of this project:git clone git@github.com:RikVoorhaar/ttml.git
    pip install .If you want to reproduce the experiments discussed in our paper, then first clone this repository. Then run the scriptdatasets/download_datasets.pyto download all relevant datasets from theUCI Machine Learning Repository. Then all figures and results can be reproduced by the scripts in thenotebooksfolder. To install all the dependencies for the scripts and tests, you can use thecondaenvironment defined inenvironment.yml.DocumentationThe documentation for this project lives onttml.readthedocs.io.CreditsAll code for this library has been written byRik
Voorhaar, in a joint project withBart
Vandereycken. This has been performed
in the scope of a Swiss National Science Foundation grant.This software is free to use and edit. When using this software for academic purposes, please cite the following preprint:@article{
    title = {TTML: Tensor Trains for general supervised machine learning},
    journal = {arXiv:2202.XXXXX},
    author = {Vandereycken, Bart and Voorhaar, Rik},
    year = {2022}, 
}All figures in the preprint have been produced using version 1.0 of this software.
Package: ttml-to-json
ttml-to-jsonConvert TTML to JSONInstallationInstall this tool usingpip:pip install ttml-to-jsonUsageTo output JSON for a TTML file:ttml-to-json subtitles.ttmlThis will output to standard output. Use-o filenameto send it to a specified file.Use-sor--singleto output single"line"keys instead of a"lines"array.You can also use:python -m ttml_to_json ...OutputRegular output:[{"start":"00:00:00.000","end":"00:00:04.560","lines":["my career in side projects and open"]}]-sor--singleoutput:[{"start":"00:00:00.000","end":"00:00:04.560","line":"my career in side projects and open"}]DevelopmentTo contribute to this tool, first checkout the code. Then create a new virtual environment:cd ttml-to-json
python -m venv venv
source venv/bin/activateNow install the dependencies and test dependencies:pip install -e '.[test]'To run the tests:pytest
Package: ttmltosrt
Copyright (c) 2015 Robbie ClarkenPermission is hereby granted, free of charge, to any person obtaining a copyof this software and associated documentation files (the "Software"), to dealin the Software without restriction, including without limitation the rightsto use, copy, modify, merge, publish, distribute, sublicense, and/or sellcopies of the Software, and to permit persons to whom the Software isfurnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included inall copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS ORIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THEAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHERLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS INTHE SOFTWARE.Description: ttmltosrt---------Convert Timed Text Markup Language subtitles to SubRip format... image:: https://travis-ci.org/RobbieClarken/ttmltosrt.svg:target: https://travis-ci.org/RobbieClarken/ttmltosrtInstallation------------::pip3 install ttmltosrtUsage-----::ttmltosrt /path/to/input_file.ttml /path/to/output_file.srtPlatform: UNKNOWNClassifier: Development Status :: 4 - BetaClassifier: Topic :: MultimediaClassifier: Intended Audience :: DevelopersClassifier: Natural Language :: EnglishClassifier: Programming Language :: PythonClassifier: Programming Language :: Python :: 3Classifier: Programming Language :: Python :: 3.5Classifier: License :: OSI Approved :: MIT License
Package: ttncli
ttn-cliBasic CLI utilityTo make buildMake sure you have the latest versions of setuptools and wheel installed:python3-mpipinstall--upgradesetuptoolswheelNow run this command from the same directory where setup.py is located:python3setup.pysdistbdist_wheelTo Install The Library LocallyInside the folder containing setup.pypython3setup.pyinstallTo Run In Development ModeSetup a virtual environment to install the packagevirtualenv venv -p python3.6Then to install the package in development modepython setup.py developTo enter shellttn_cliTo Run CLI Commandsttn_cli --help # show all the available options

ttn_cli git --help # shows all the commands related to git

ttn_cli git logs -f <form_date> -a <author-name> show the logs of a particular author

... and many more

ttn_cli network --help # shows all the commands related to network

ttn_cli network show_public_ip # shows public IP of host

ttn_cli network show_private_ip # shows private IP of hostFor reference, follow thisarticle.
Package: ttok
ttokCount and truncate text based on tokensBackgroundLarge language models such as GPT-3.5 and GPT-4 work in terms of tokens.This tool can count tokens, using OpenAI'stiktokenlibrary.It can also truncate text to a specified number of tokens.Seellm, ttok and strip-tags—CLI tools for working with ChatGPT and other LLMsfor more on this project.InstallationInstall this tool usingpip:pip install ttokCounting tokensProvide text as arguments to this tool to count tokens:ttokHelloworld2You can also pipe text into the tool:echo-n"Hello world"|ttok2Here theecho -noption prevents echo from adding a newline - without that you would get a token count of 3.To pipe in text and then append extra tokens from arguments, use the-i -option:echo-n"Hello world"|ttokmoretext-i-6Different modelsBy default, the tokenizer model for GPT-3.5 and GPT-4 is used.To use the model for GPT-2 and GPT-3, add--model gpt2:ttokbooHellotherethisis-mgpt26Compared to GPT-3.5:ttokbooHellotherethisis5Further model options aredocumented here.Truncating textUse the-t 10or--truncate 10option to truncate text to a specified number of tokens:ttokThisistoomanytokens-t3This is tooViewing tokensThe--encodeoption can be used to view the integer token IDs for the incoming text:ttokHelloworld--encode9906 1917The--decodemethod reverses this process:ttok99061917--decodeHello worldAdd--tokensto either of these options to see a detailed breakdown of the tokens:ttokHelloworld--encode--tokens[b'Hello', b' world']ttok --helpUsage: ttok [OPTIONS] [PROMPT]...

  Count and truncate text based on tokens

  To count tokens for text passed as arguments:

      ttok one two three

  To count tokens from stdin:

      cat input.txt | ttok

  To truncate to 100 tokens:

      cat input.txt | ttok -t 100

  To truncate to 100 tokens using the gpt2 model:

      cat input.txt | ttok -t 100 -m gpt2

  To view token integers:

      cat input.txt | ttok --encode

  To convert tokens back to text:

      ttok 9906 1917 --decode

  To see the details of the tokens:

      ttok "hello world" --tokens

  Outputs:

      [b'hello', b' world']

Options:
  --version               Show the version and exit.
  -i, --input FILENAME
  -t, --truncate INTEGER  Truncate to this many tokens
  -m, --model TEXT        Which model to use
  --encode, --tokens      Output token integers
  --decode                Convert token integers to text
  --tokens                Output full tokens
  --help                  Show this message and exit.You can also run this command using:python-mttok--helpDevelopmentTo contribute to this tool, first checkout the code. Then create a new virtual environment:cdttok
python-mvenvvenvsourcevenv/bin/activateNow install the dependencies and test dependencies:pipinstall-e'.[test]'To run the tests:pytest
Package: ttool
TTOOL (Tugy Tools)This Repository is for utils that me, Tugy, thought that will be usefull.Author:Tugy| Github Repo:tugytoolsInstallation:$ pip install ttoolPackages:Jira:To use the Jira utils you need to create 2 Global variables:JIRA_API_TOKENandJIRA_MAIL.
Get yourJIRA_API_TOKENhere.YourJIRA_MAILshould store your jira mail ofc..I suggest that you store this values in$HOME/.bashrcor$HOME/.aliasesif you have one.$ ttool jira --help

Usage: ttool jira [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  checkout  Checkout/Print your chosen jira issue with...
  setup     Set up your jira configurations.The Setup CommandTo setup a local config file to store your jira project and host.$ ttool jira setup --help

Usage: ttool jira setup [OPTIONS]

  Set up your jira configurations.

Options:
  --help  Show this message and exit.The Checkout Command:It really annoyed me every time to copy the key and description from the jira and create indicative branch names.
So this command let you pick a ticket of yours from the jira in the CLI, and then checkout to a branch with this syntax:ISSUE-123/My-issue-description$ ttool jira checkout --help

Usage: ttool jira checkout [OPTIONS]

  Checkout/Print your chosen jira issue with 'ISSUE-ID/the-issue-summery'
  format. use --print to print the branch name without checkout

Options:
  --print / --no-print  [default: no-print]
  --help                Show this message and exit.
Package: t-toolbox
T-ToolboxMy toolbox for python.FreeformFreeform is a class that can turn any set of arguments into fields.Examples# Import the Freeform classfromt_toolboximportFreeform# Create a Freeform instance with class fieldsmy_data=Freeform(name="John",age=30,city="New York")# Access the class fieldsprint("Name:",my_data.name)print("Age:",my_data.age)print("City:",my_data.city)ConsumableBool that resets to false after use.ExamplesfromconsumableimportConsumableconsumable=Consumable(True)ifconsumable:print("Consumable is true!!")ifconsumable:# Will not activate, previous use sets consumable to False!print("Consumable is true again!! Should not happen.")
Package: ttop
ttopttop is CLI graphical system monitor.
this tools is designed for use withtmux.requirementspythonpython >= 3.3.1 or 2.7.3tmuxtmux >= 1.8python librarydocopt >= 0.6.1psutil >= 2.1.1installationinstall from pypieasy_install ttoporpip install ttopinstall from githubgit clone https://github.com/ton1517/ttop.git
cd ttop
python setup.py installusageUsage:
  ttop [--no-color] [--interval 
] [--no-tmux] [normal | minimal | stack] [horizontal | vertical]
  ttop -h | --help
  ttop -v | --version

Options:
  -h --help           show help.
  -v --version        show version.
  -C --no-color       use monocolor.
  -i --interval 
   refresh interval(second) [default: 1.0].
  -T --no-tmux        don't use tmux mode.CHANGES0.9.0 (2014-06-23)add color option.add bright color theme.show process nums.show memory as MB.graph color overwrite existing percent color.remove modules. (hurry.filesize and uptime)bugfix.refactoring.0.8.0 (2014-05-06)implements stack vertical view.flexible stack.delete empty line.refactoring.bug fixed : when CPU resource is 100%, raise error.0.7.2 (2014-02-06)refactoring.change libraries version.0.7.0 (2013-10-07)check tmux version. (tmux 1.8 or higher)show Uptime and LoadAverage with horizontal style.0.6.0 (2013-10-02)view mode options.tmux mode.minimal horizontal view.stack view.normal vertical view.minimal vertical view.0.1.0 (2013-09-30)initial version.
Package: ttopt
ttoptDescriptionGradient-free optimization method for multivariable functions based on the low rank tensor train (TT) format and maximal-volume principle.Please, see also our software producttenevawhich provides a very compact implementation of basic operations in the TT-format.InstallationYou can install thettoptpackage forpython >= 3.7with pip:pipinstallttopt==0.6.2ExamplesThe demo-scripts with detailed comments are collected in the folderdemo:base.py- we find the minimum for the 10-dimensional function with vectorized input;qtt.py- we do almost the same as in thebase.pyscript, but use the QTT-based approach (note that results are much more better then in thebase.pyexample);qtt_max.py- we do almost the same as in theqtt.py, but consider the maximization task;qtt_100d.py- we do almost the same as in theqtt.pyscript, but approximate the 100-dimensional function;vect.py- we find the minimum for the simple analytic function with "simple input" (the function is not vectorized);cache.py- we find the minimum for the simple analytic function to demonstrate the usage of the cache;tensor.py- in this example we find the minimum for the multidimensional array/tensor (i.e., discrete function);tensor_init- we do almost the same as in thetensor.pyscript, but we use special method of initialization (instead of a random tensor, we select a set of starting multi-indices for the search).AuthorsAndrei ChertkovIvan OseledetsRoman SchutskiKonstantin SozykinCitationIf you find this approach and/or code useful in your research, please consider citing:@article{sozykin2022ttopt,author={Sozykin, Konstantin and Chertkov, Andrei and Schutski, Roman and Phan, Anh-Huy and Cichocki, Andrzej and Oseledets, Ivan},year={2022},title={{TTOpt}: {A} maximum volume quantized tensor train-based optimization and its application to reinforcement learning},journal={Advances in Neural Information Processing Systems},volume={35},pages={26052--26065},url={https://proceedings.neurips.cc/paper_files/paper/2022/hash/a730abbcd6cf4a371ca9545db5922442-Abstract-Conference.html}}Please, note that the calculations presented in this paper correspond to version<0.5.0of thettoptpackage (and to very old version of thetenevapackage), to run the calculations, please use the appropriate version. In the new versions>=0.6.0, we have removed all the corresponding folders in the foldercomputations_old. In the future, we will try to update the interface of these experiments.✭__🚂  The stars that you give tottopt, motivate us to develop faster and add new interesting features to the code 😃
Package: ttpalette
TTPALETTEA library for coloring texts in the terminalThis library works withANSI escape code256 Color tableThe photo is fromhereInstallpip install ttpaletteExamplesUse a specific colorfromttpaletteimportttpalettettpalette.BackColor().list_costum_colors(14)ttpalette.Color().list_costum_colors(14)Use a solid colorfromttpaletteimportttpalettettpalette.Color.RED()ttpalette.BackColor.RED()Fixed colors availableBLACK
RED
GREEN
YELLOW
BLUE
MAGENTA
CYAN
WHITE

RESETBack to normalfromttpaletteimportttpalettettpalette.Color.RESETttpalette.BackColor.RESET
Package: ttpbuilder
TTP Builder GUI ApplicationIntroductionTTP Builder is a Python-based GUI application built using PyQt6 that facilitates the creation and testing of Text-based Template Parsing (TTP) templates. TTP Builder accelerates the process of creating and testing TTP templates for network automation projects.FeaturesText Editor: A primary text editor to paste sample text data.Named Selections: A ListWidget displays identified variables parsed from the sample text data.Template Generator: A 'Generate Template' button for automated TTP template creation.Help System: In-app help documentation linking directly to TTP's official documentation and an "About" section.Screen ShotsPre-requisitesPython 3.xPyQt6PyQt6-WebEngine (For Help menu)TTPInstall and RunFrom an activated venv environmentpip install ttpbuilderExecute:ttpbuildergitclonehttps://github.com/scottpeterman/ttpbuilder.gitcdttp_builderInstall the required packages.pipinstall-rrequirements.txtRun the application.pythonmain.pyHow to UsePaste Sample Data: Open the app and paste your sample text data into the text editor on the left-hand side.Named Selections: After pasting text data, highlight a section of the text that you want to be a variable in the TTP template. Right-click and choose "Create Named Selection".ListWidget: This will populate the ListWidget on the right with your identified variables. You can edit or remove these as necessary.Generate Template: Once you've highlighted all variables of interest, click on the 'Generate Template' button at the bottom to create the TTP template.Help Menu: Use the Help menu for additional resources and documentation on TTP.Technologies and Libraries UsedPyQt6: For the GUI.QWebEngineView: For embedded web browser support.TTP: For template generation logic.
Package: ttp-scripts
Manage pipeline versionsInstallation$ pip install -r requirements.txt

$ pip install setup.pyDevelopmentThis project includes a number of helpers in theMakefileto streamline common development tasks.Environment SetupThe following demonstrates setting up and working with a development environment:### create a virtualenv for development

$ make virtualenv

$ source env/bin/activate


### run ttp cli application

$ ttp --help


### run pytest / coverage

$ make testReleasing to PyPiBefore releasing to PyPi, you must configure your login credentials:~/.pypirc:[pypi]
username = YOUR_USERNAME
password = YOUR_PASSWORDThen use the included helper function via theMakefile:$ make dist

$ make dist-uploadDeploymentsDockerIncluded is a basicDockerfilefor building and distributingTwinThread Pipeline Release Manager,
and can be built with the includedmakehelper:$ make docker

$ docker run -it ttp --help
Package: ttp-sros-parser
TTP SrosParserA library to parse a Nokia SROS 7750 full hierarchical configuration text file into structured data. Show commands are also able to be parsed, if included in the file alongside the configuration. This library is still under development, but a lot of parsers are readily available (see below for supported features). Configurations used for testing have all been on release version higher than TiMOS 16. At the moment, there is no capability to specify release version to accommodate different parsing templates.What TTP SrosParser is notA library that connects and extracts information from a remote device. You, as the end-user must obtain a text file of the configuration. This file will be passed into the SrosParser class and you are able to convert a flat text configuration file into structure data using the built-in TTP parser templates. At this point, it's recommended to use a new instance of the srosparser with an individual show command as the text to parse to get the best results when parsing a show commands.Example"""SrosParser - Example."""fromttp_sros_parser.srosparserimportSrosParserEXAMPLE_CONFIG="some/dir/path/to/7750-config.txt"# Instantiate classrouter=SrosParser(EXAMPLE_CONFIG)# Call `get_system_cards` methodrouter.get_system_cards()Results:[{"configure":{"card":{"card-type":{"card-type":"iom-1","subscription-level":"cr"},"fail-on-error":true,"mda":{"admin-state":true,"egress-xpl":{"window":"10"},"fail-on-error":true,"ingress-xpl":{"window":"10"},"mda-slot":"1","mda-type":"me6-100gb-qsfp28"},"slot-number":"1"}}}]Available Methods (Parsers)Current methods available (Automatically updated at build):MethodsFull ConfigSrosParserallows you to parse the full configuration with a single method call,get_full_config()and receive the full JSON output of the device.Custom TemplatesSrosParser allows you to simply specify a custom template after you initialize a new class object.router=SrosParser("path/to/config.txt")cool_ttp_template=router.get_custom_template("path/to/template")print(cool_ttp_template)ContributingAny contribution to the project must include unit tests and pass all linting.Simply run:docker-compose builddocker-compose run test
Package: ttra
Time-proof Time-series Reduction AlgorithmTTRA is a lightweight algorithm reducing a time-series with a time omission.It has been described in the Master's Thesis.Example of real-time usageUsageimportpandasaspdimportmatplotlib.pyplotaspltfromttraimportTTRA# define minimal percentage change that should be detected by TTRAPCT_CHANGE:float=0.01# let's take the inflation in Poland as an examplesource:str="https://stat.gov.pl/download/gfx/portalinformacyjny/pl/defaultstronaopisowa/4741/1/1/miesieczne_wskazniki_cen_towarow_i_uslug_konsumpcyjnych_od_1982_roku_13-05-2022.csv"# download and process datainflation=pd.read_csv(source,encoding='ISO-8859-2',sep=';').sort_values(['Rok','Miesišc'])inflation=inflation[inflation['Sposób prezentacji']=='Analogiczny miesišc poprzedniego roku = 100']inflation=inflation['Wartoć'].dropna().map(lambdax:x.replace(',','.')).astype(float)inflation=inflation.iloc[-12*25:].reset_index(drop=True)# last 25 years only to not obscure the newest data# initiate TTRA and reduce data with a given PCT_CHANGEtr=TTRA(inflation)reduced=tr.reduce(PCT_CHANGE)# plot data, reduced data and an assumption of the current extremumfig,ax=plt.subplots()inflation.plot(ax=ax)reduced.plot(ax=ax)plt.scatter(tr.a.Index,tr.a.x,s=150,color='black')Output