Saravanan666/pypi-org huggingface dataset:
Package: tuzikmediatools
tuzikmediatoolsinstallationpip install tuzikmediatoolsorpip3 install tuzikmediatoolsorpython -m pip install tuzikmediatoolsDescriptiontuzikmediatools- is a small module that can:Generate strings from user-specified characters.Сheck if a number is primeCount the number of words in a textUsageimportimport tuzikmediatools as tmtGenerateimport tuzikmediatools as tmt

# tmt.generate(lenght, 
#              symbols='a-z / numbers / castom_symbols', 
#              case='low / up', 
#              numbers_range=(from, before), 
# ) 


tmt.generate(8) # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> qHG1%lb4


tmt.generate(8, symbols = 'a-z') # >>>>>>>>>>>>>>>>>>>>>>>>>> ajTDkapW

tmt.generate(6, symbols='a-z', case='up') # >>>>>>>>>>>>>>>>> URLDGQ
tmt.generate(12, symbols='a-z', case='low') # >>>>>>>>>>>>>>> kdyemfjsgisy


tmt.generate(6, symbols='numbers') # >>>>>>>>>>>>>>>>>>>>>>>> 194837

tmt.generate(6, symbols='numbers', numbers_range=(0, 6)) # >> 163402
tmt.generate(17, symbols='numbers', numbers_range=(0, 1)) # > 10011110011100010


tmt.generate(6, symbols='1230 *') # >>>>>>>>>>>>>>>>>>>>>>>>> 10 *32
tmt.generate(6, symbols='йцукенгшщзх') # >>>>>>>>>>>>>>>>>>>> кщйгцкIs_primeimport tuzikmediatools as tmt

tmt.is_prime(5) # >>>>>>>>  True
tmt.is_prime(18) # >>>>>>>  False
tmt.is_prime(-3, 5, 4) # > (False, True, False)Word_countimport tuzikmediatools as tmt

tmt.word_count('Hello, world!') # >>>>>>>>>>>>>>>>>>>>>>>>>  2
tmt.word_count('Hello,   word!', 'Hello, how are you?') # > (2, 4)
Package: tv2
tv2 - Terminal Velocity 2tv2 is a fast note-taking app for the UNIX terminal, that focuses on letting you create or find a note as quickly and easily as possible, then uses your$EDITORto open and edit the note. It is heavily inspired by the OS X appNotational Velocity. For screenshots and features, see theTerminal Velocity website.Installationpip - Python package managerTo install tv2, run:pip3 install tv2Then to launch it just run:tv2To use a different notes directory, run:tv2 path/to/your/notes/dirTo see all the command-line options, run:tv2 -hTo quit the app, pressctrl-corctrl-x.To upgrade tv2 to the latest version, run:pip install --upgrade tv2To uninstall it, run:pip uninstall tv2From SourceEnsure python modulesurwid,setuptoolsandchardetare installed. Python-dev also.apt install python-setuptools python-chardet python-urwid python-devClone the repository from:https://github.com/makyo/tv2.gitMove into tv2 directory you just cloned and run the following:sudo python setup.py installContributingTo contribute code to Terminal Velocity, seeCONTRIBUTING.
Package: tvallogging
Tonic Validate LoggingTonic Validate helps you develop your retrieval augmented generation (RAG) system by providing RAG metrics and a platform for tracking and evaluating experiments and changes to your RAG system. This isTonic Validate Logging, the logging component of Tonic Validate - how you send your RAG system output to the Tonic Validate Application. When you log RAG system outputs using Tonic Validate Logging, the outputs are scored usingTonic Validate Metrics, and then the outputs and scores are sent to theTonic Validate Application, where the outputs and responses are visualized allowing you to easily track the performance of you RAG system.Tonic Vaidation DocumentationGetting StartedSign up for afree Tonic Validate account.Install Tonic Validate Logging via pippip install tvalloggingGet a Tonic Validate API key, and set it in your environment as theTONIC_VALIDATE_API_KEYenvironment variable.Set up a project and a benchmark dataset of questions and reference answers, and the log your RAG system responses to the questions in the benchmark. Use the following code snippet to get started. Tonic Validate uses LLM assisted evaluation to score your RAG responses, so in addition to assuming you have theTONIC_VALIDATE_API_KEYenvironment variable set to you Tonic Validate API Key, it also assumes you have theOPENAI_API_KEYenvironment variable set so Open AI models can be used to evaluate your RAG responses.importos# to set the environment variables via pythonos.environ["OPENAI_API_KEY"]="put-your-openai-api-key-here"os.environ["TONIC_VALIDATE_API_KEY"]="put-your-tonic-validate-api-key-here"fromtvallogging.apiimportTonicValidateApifromtvallogging.chat_objectsimportBenchmarkproject_name:str# name of your new projectbenchmark_name:str# name of your new benchmark# list of dictionaries of the form# {#     "question": "question for the benchmark",#      "answer": "reference answer to the question"# }question_with_answer_list:List[Dict[str,str]]api=TonicValidateApi()benchmark=Benchmark.from_json_list(question_with_answer_list)benchmark_id=api.new_benchmark(benchmark,benchmark_name)project=api.new_project(benchmark_id,project_name)llm_evaluator="gpt-4"run=project.new_run(llm_evaluator)forquestion_with_answerinrun.benchmark.question_with_answer_list:question=question_with_answer.questionllm_answer:str# answer obtained from the RAG systemretrieved_context_list:List[str]# list of context retrieved by the RAG system# log the llm_answer and retrieved_context_list to Tonic Validate# in this step, the RAG metrics are calculated locallyrun.log(question_with_answer,llm_answer,retrieved_context_list)Review how your RAG system is performing in the Tonic Validate UI.Documentation and Tonic Validate MetricsTonic Vaidation Documentationhas extensive information on how Tonic Validate works and the RAG metrics used in Tonic Validate.Check outTonic Validate Metricsfor more information about the RAG metrics used in Tonic Validate and if you're interested in computing the RAG metrics outside of Tonic Validate.
Package: tvarit-api
tvarit_apiWhat is this library for?Tvarit API library for Python. Support both 2 and 3 Python versions.RequirementsYou need either 2nd or 3rd version of Python and only therequestslibrary installed.Quick startInstall the pip package:pip install -U tvarit_apiAnd then connect to your Tvarit API endpoint:fromtvarit_apiimportTvarittvarit_api=Tvarit(auth='abcde....',host='cloud.tvarit.com')# Search dashboards based on tagtvarit_api.search.search_dashboards(tag='applications')# Find a user by emailuser=tvarit_api.users.find_user('test@test.com')# Add user to team 2tvarit_api.teams.add_team_member(2,user["id"])# Create or update a dashboardtvarit_api.dashboard.update_dashboard(dashboard={'dashboard':{...},'folderId':0,'overwrite':True})# Delete a dashboard by UIDtvarit_api.dashboard.delete_dashboard(dashboard_uid='abcdefgh')Status of REST API realizationWork on API implementation still in progress.APIStatusAdmin+Alerting+Annotations+Authentication+-Dashboard+Dashboard Versions-Dashboard Permissions+Data Source+Folder+Folder Permissions+Folder/Dashboard Search+-Organisation+Other+Preferences+Snapshot+Teams+User+Issue trackerPlease report any bugs and enhancement ideas using thetvarit_apiissue tracker:https://github.com/tvarit-foggy/tvarit_api/issuesFeel free to also ask questions on the tracker.Licensetvarit_apiis licensed under the terms of the MIT License (see the fileLICENSE).
Package: tvb-contrib
THE VIRTUAL BRAIN CONTRIB LIBRARYThe Virtual Brain Project (TVB Project) has the purpose of offering some
modern tools to the Neurosciences community, for computing, simulating
and analyzing functional and structural data of human brains.For more details, check:http://www.thevirtualbrain.org/The currenttvb-contribis a package with TVB contributed additions to our simulator,
useful especially for scripting.Use with:pip install -U tvb-contribFurther ResourcesFor issue tracking we are using Jira:http://req.thevirtualbrain.orgFor API documentation and live demos, have a look here:http://docs.thevirtualbrain.orgA public mailing list for users of The Virtual Brain can be joined
and followed using:tvb-users@googlegroups.comRaw demo IPython Notebooks can be found under:https://github.com/the-virtual-brain/tvb-root/tree/master/tvb_documentation/demosAcknowledgmentsThis project has received funding from the European Union’s Horizon 2020 Framework Programme for Research and
Innovation under the Specific Grant Agreement Nos. 785907 (Human Brain Project SGA2), 945539 (Human Brain Project SGA3)
and VirtualBrainCloud 826421.
Package: tvb-data
Using this repository (tvb-data) from Github is now considered DEPRECATED.
We advise you to use our Zenodo repository instead:
https://zenodo.org/record/3474071#.XZmcU-cza_UTVB DataVarious demonstration datasets for use with The Virtual Brain are provided here.Here you will find:
* compatible file/folder structures that can be uploaded thorugh the web interface of TVB; and
* the default datasets used when working inconsoleorlibrarymode.This package should be treated as a regular Python module.Possibly what you want to do after cloning it locally from GitHub (https://github.com/the-virtual-brain/tvb-data) is:cd tvb-data/
python setup.py developto have it linked into your current Python environment.tvb-dataalso is shared on Pypi, but it has fewer files, due to size restrictions there.
To work with this package from pypi, run:pip install tvb-dataFurther ResourcesFor issue tracking we are using Jira:http://req.thevirtualbrain.orgFor API documentation and live demos, have a look here:http://docs.thevirtualbrain.orgA public mailing list for users of The Virtual Brain can be joined and followed using:tvb-users@googlegroups.comRaw demo IPython Notebooks can be found under:https://github.com/the-virtual-brain/tvb-documentation/tree/master/demosData from here will be used bytvb-libraryandtvb-frameworkpackages on Pypi
Package: tvb-ext-bucket
tvb-ext-bucketExtension for collabs bucket interactionThis extension is composed of a Python package namedtvb_ext_bucketfor the server extension and a NPM package namedtvb-ext-bucketfor the frontend extension.RequirementsJupyterLab >= 3.0InstallTo install the extension, execute:pipinstalltvb-ext-bucketUninstallTo remove the extension, execute:pipuninstalltvb-ext-bucketTroubleshootIf you are seeing the frontend extension, but it is not working, check
that the server extension is enabled:jupyterserverextensionlistIf the server extension is installed and enabled, but you are not seeing
the frontend extension, check the frontend extension is installed:jupyterlabextensionlistContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the tvb-ext-bucket directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Server extension must be manually installed in develop modejupyterserverextensionenabletvb_ext_bucket# Rebuild extension Typescript source after making changesjlpmbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstall# Server extension must be manually disabled in develop modejupyterserverextensiondisabletvb_ext_bucket
pipuninstalltvb-ext-bucketIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedtvb-ext-bucketwithin that folder.Testing the extensionServer testsThis extension is usingPytestfor Python code testing.Install test dependencies (needed only once):pipinstall-e".[test]"To execute them, run:pytest-vv-rap--covtvb_ext_bucketFrontend testsThis extension is usingJestfor JavaScript code testing.To execute them, execute:jlpm
jlpmtestPackaging the extensionSeeRELEASEAcknowledgmentsThis project has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).
Package: tvb-ext-xircuits
tvb-ext-xircuitsThis is a jupyterlab extension built as a prototype for building EBRAINS
(including TVB simulator, Siibra API) workflows in a visual and interactive manner. It
extends the already existentXircuitsjupyterlab extension
by adding new components and new features on top.For installing in a Jupyter Lab environment:pip install tvb-ext-xircuitsFor dev mode setup there are 2 alternatives:Usingjlpm:jlpmis a JupyterLab-provided, locked version ofyarnand has a similar usage:conda activate [my-env]
pip install --upgrade pip
pip install -e .
jupyter labextension develop . --overwrite  # Link your development version of the extension with JupyterLab
jupyter server extension enable tvbextxircuits  # Enable the server extension
tvbextxircuitsUsingyarn:You need to have a dedicatedPython env,yarn,rustandcargo(fromhttps://rustup.rs/) prepared:conda activate [my-env]
pip install --upgrade pip
pip install -e .
yarn install
yarn install:extension
tvbextxircuitsTo rebuild the extension after making changes to it:# Rebuild Typescript source after making changes
  jlpm build
  # Rebuild extension after making any changes
  jupyter lab buildTo rebuild automatically:# Watch the source directory in another terminal tab
  jlpm run watch
  # Run Xircuits in watch mode in one terminal tab
  jupyter lab --watchNotesTo be able to see details info related to TVB components you must first run the commandpython generate_description_files.pyNotebooks generated can be found atTVB_generated_notebooks/<xircuits_id>AcknowledgmentsCopyright (c) 2022-2023 to Xircuits Team See:https://github.com/XpressAI/xircuitsCopyright (c) 2022-2023 to TVB-Xircuits team (SDL Neuroscience Juelich, INS Marseille, Codemart) for changes in this fork.This extension is build on top of the Xircuitshttps://xircuits.ioJupyter extension, and it adds custom features, tailored for EBRAINS env.This project has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).
Package: tvb-liteline
tvb-litelineThis file will become your README and also the index of your
documentation.Installpipinstalltvb_litelineHow to useFill me in please! Don’t forget code examples:1+12help(recon_all)Help on function recon_all in module tvb_liteline.core:

recon_all(image_fname, subject_name, parallel=True, openmp=4, subjects_dir=None, license_fname=None)
    Generate call to the main FreeSurfer script `recon-all` on a given image.' '.join(recon_all('foo','bar')[0])'recon-all -s bar -i foo -parallel -openmp 4'🎉 that was easy!
Package: tvbutler
tvbutler reads RSS feeds fromhttp://tvtorrents.comand downloads torrents of new episodes for you in your preferred quality (but is smart enough to fallback to non-HD versions, if available).Usagetvbutleris designed to work as acrontaborlaunchdentry: called without any parameters it will download all given feeds, try to identify shows and then figure out which episodes are available in which quality.It then downloads the torrent files into the given target directory (which typically will be ‘watched’ by a torrent client).tvbutler keeps a log of its activities in~/.tvbutler/logand a (sqlite) database of known shows and episodes in~/.tvbutler/database.dbConfigurationThe configuration lives in~/.tvbutler/config. If it doesn’t exist, tvbutler will create a sample configurationwhen it’s run for the first time. It has some comments and should thus be pretty self-explanatory:[main]
torrent_download_dir=~/Downloads/
# one of sd, 720p, 1080p
preferred_quality=720p

# one per line, indented
feeds =
    http://www.tvtorrents.com/mytaggedRSS?digest=xxxxx
    http://www.tvtorrents.com/mydownloadRSS?digest=xxxxSince tvbutler is aimed at downloading new episodes of currently running shows as they are released, it excludes by default all torrents it recognizes as archives of entire seasons. It does so by excluding all torrents whose description matches a regular expression. If you have aglobal_exclude_regexentry in the[main]section of your configuration it will be applied, if it’s missing, nothing will be filtered. The default looks like so:global_exclude_regex=(all.month|month.of|season[\s\d]*complete)InstallationJust installeasy_installlike so:easy_install tvbutlerAlternatively, download the archive, expand it and run:python setup.py installThis will install the command line executabletvbutlermentioned above.TODOarchive or delete files older thanndays (perhaps base decision on tag)remove torrents from transmission that have reached their seeding limitHistory0.1.b1 - 2011-01-12Documentation cleanup (thanks toclaytron)Fix for shows with ‘:’ in their name (thanks toplambert), fixeshttps://github.com/tomster/tvbutler/pull/50.1a5 - 2010-12-17robustness fix (tvbutler would barf on some entries and stop processing the remaining entries)log to stdout, too to get instant feedback when calling tvbutler from console0.1a4 - 2010-12-08added global regular expression filter (defaults to skip archives of
seasons)UPGRADE NOTE: addglobal_exclude_regex=(all.month|month.of|season[\s\d]*complete)to the[main]section your existing config to activate this feature.0.1a3 - 2010-12-05added logging0.1a2 - 2010-12-05Initial release (0.1a was nuked due to packaging foobar)
Package: tvb-widgets
tvb-widgetsTheVirtualBrain - Widgetsis a package with somehow generic GUI components,
but developed forEBRAINS Showcasesin particular.The showcases developed in the last phase of the HBP are meant to illustrate
the full potential of technical and scientific features offered by EBRAINS.
Access by the end users to this showcased functionality should be as easy as
possible. In order to support the usability of the showcases, as well as future
EBRAINS workflows, we develop here a set of modular graphic solutions which
can be easily deployed in the EBRAINS Collaboratory within the JupyterLab.This module is also documented in EBRAINS here:https://wiki.ebrains.eu/bin/view/Collabs/tvb-widgetsThese graphic user interface components enable:Easy setup of models and region specific or cohort simulations. This includes single simulations as well as parameter explorations.Selection of Data sources and their links to models. This will exploit the backend data access functionality to be developed new in TVB.Integration of a subset of TVB analysis and visualisation tools.The GUIs must be designed to be integrated into cells in the notebooks and Jupyter notebook extension for HPC resource usage and job tracking will be also developed as independent panels.InstallationThis module is already available in EBRAINS lab,
but you can also make use of it locally, in which case, execute:jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter labextension install jupyter-matplotlib
pip install tvb-widgetsIn order to install tvb-widgets from the local repo clone, directly in a notebook, run (in the first cell):%pip install --quiet --upgrade pip
%pip install --quiet -e ..
%pip install tvb-dataFor some of our widgets, where connection to EBRAINS storage is necessary,
you should setup as environment variable before launching the local Jupyter Lab instance:export CLB_AUTH = "{Your TokenString copied from EBRAINS Collab}"To retrieve the token string, execute inhttps://lab.ch.ebrains.eu/:clb_oauth.get_token()AcknowledgmentsThis project has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).
Package: tvchannellist
tvchannellistPython Package for programmatically retrieving the LCN of television channels. This was originally designed forHome Assistant.NOTE:Many TV channel list providers has no official API; therefore, this library may stop
working at any time without warning.CreditsOriginally inspired byAlexaControlledSamsungTV.Additional scaffolding fromalexapyandsimplisafe-pythonContributingCheck for open features/bugsorinitiate a discussion on one.Fork the repository.Install the dev environment:python3 -m venv venv.Enter the virtual environment:source venv/bin/activateInstall requirements:pip3 install -r requirements.txtCode your new feature or bug fix.Write a test that covers your new functionality.UpdateREADME.mdwith any new documentation.Run tests and ensure 100% code coverage for your contribution:make coverageEnsure you have no linting errors:make lintAdd yourself toAUTHORS.md.Submit a pull request!LicenseGPLv30. By providing a contribution, you agree the contribution is licensed under GPLv3.
Package: tvdbAPI
tvdbAPIThis is an API library for use when connecting to The TVDB.How to UseImport Packagefrom tvdbAPI import TVDBUse the Modulet = TVDB()Get Basic Info About a Given Show"""Gets basic info about the show with given 'name'.

Arguments:
    name {String} -- The name of the show you are searching for.

Raises:
    InvalidInput: Raises if a non string is entered for name.
    ShowNotFound: Raises if no show was found for the given name/alias.

Returns:
    dict -- Returns a dictionary containg basic data about the show.
"""
t.getShow("Mythbusters")Get a List of all Episodes of a Given Show"""Gets a list of all the episodes for a given show.

Arguments:
    name {String} -- The name of the show being searched for.

Keyword Arguments:
    accuracy {float} -- If no show with title found, how accurate should a match to the alias be. (default: {0.8})

Raises:
    InvalidInput: Raises if a non string is inputed for name.
    InvalidShowID: Raises if a show was not found.

Returns:
    list -- Returns a list of all the episodes for a given show.
"""
t.getEpisodes("Mythbusters", 0.8)Get a Specific Episode's Name"""Gets an episode by its name, based on the show name, season number, and episode number, and cleaned of any special characters so it can be used to name files without error.

Arguments:
    name {String} -- The name of the show being searched for.
    seasonNum {integer} -- The season number which the episode is in.
    epNum {integer} -- The episode number in the season.

Keyword Arguments:
    order {String} -- Specifies the episode number ordering to parse ("DVD", "Aired")
    accuracy {float} -- If no show with title found, how accurate should a match to the alias be. (default: {0.8})
    id -- Optional input of show id when searching for episode names.

Raises:
    InvalidInput: Raises if a non string is inputed for name.
    InvalidShowID: Raises if a show was not found.

Returns:
    String -- Returns the name of the episode searched for, cleaned of all special characters.
"""
t.getEpisodeName("Scrubs", 1, 1, "Aired", 0.8)
Package: tv-db-api
tv-db-apiPython wrapper for interacting with the tv db apiFree software: MIT licenseDocumentation:https://tv-db-api.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-06-27)First release on PyPI.
Package: tvdb-api-client
tvdb_api_clientis an unofficial API for the TVDB.In a nutshellInstallationThe easiest way is to usepoetryto manage your dependencies and addtvdb_api_clientto them.[tool.poetry.dependencies]tvdb_api_client="*"UsageInitialise the client (example using the django cache):fromdjango.core.cacheimportcachefromtvdb_api_clientimportTVDBClientclient=TVDBClient("username","user_key","api_key",cache)Once the client has been initialised, you can use it to get the following info:get TV series by TVDB idget TV series by IMDb idfind identifying info for a TV series by its nameget episodes by TV series using its TVDB idLinksDocumentationChangelog