Saravanan666/pypi-org huggingface dataset:
Package: tecoroute-proxy
TecoRoute ProxyTecoRoute Proxy is a reverse proxy server that allows easy authentication to theTecoRouteservice using a simple POST request. It also saves the credentials
and automatically logs the user in when the TecoRoute session expires.How it worksThe server has one control endpoint (/TR_PROXYby default) that is used
to log in and log out of the user from the proxy. All other requests are proxied
to TecoRoute. The proxy session is stored only in the user's session cookies, so
the server is completely stateless and no database is required.The control endpoint acceptsPOSTrequests withapplication/x-www-form-urlencodedcontent type (HTML form).Log the user inThe user logs in with the following data:NameValueactionloginusernameTecoRoute usernamepasswordTecoRoute passwordplcTecoRoute PLCExample of a HTML form that logs the user in:<formmethod="post"action="https://tecoroute-proxy.cze.tech/TR_PROXY"><buttonname="action"value="login">Open PLC</button><inputtype="hidden"name="username"value="BroukPytlik"/><inputtype="hidden"name="password"value="ferda1"/><inputtype="hidden"name="plc"value="AB_1234"/></form>Log the user outThe user logs out with the following data:NameValueactionlogoutExample of a HTML form that logs the user out:<formmethod="post"action="https://tecoroute-proxy.cze.tech/TR_PROXY"><buttonname="action"value="logout">Logout from TecoRoute</button></form>Logging out will try to log the user out from TecoRoute and delete the login
data from the user's cookies.Usage optionsTecoRoute Proxy can be used as a service, standalone application or as a Python
library.Use as a serviceServer is deployed athttps://tecoroute-proxy.cze.tech. The control URL is:https://tecoroute-proxy.cze.tech/TR_PROXYFeel free to use the service for testing or simple production purposes.Install fromPyPIRequirements:Python(version 3.8 or later)pipor another package installer for PythonInstallation using pip is done with:pipinstalltecoroute-proxyStart the server with:tecoroute-proxyOn an unprivileged port (e.g. 8080), start the server with:tecoroute-proxy-p8080For all options, runtecoroute-proxy --help.It is possible to use the package as a Python library and run the server
synchronously or asynchronously in the event loop. Example of use:fromloggingimportINFO,basicConfigfromtecoroute_proxyimportProxyServerbasicConfig(level=INFO)server=ProxyServer(port=8080)server.run()See full documentation athttps://tecoroute-proxy.readthedocs.io.Run from Docker HubRun the image from Docker Hub:dockerrun-p80:80czetech/tecoroute-proxyInstall to Kubernetes using HelmSetup Helm repository:helmrepoaddczetechhttps://charts.cze.tech/Install Helm chart:helminstalltecoroute-proxyczetech/tecoroute-proxy\--setingress.enabled=true\--setingress.hosts[0]=<ingress-host>see thechartfor more options.Source codeThe source code is available athttps://github.com/czetech/tecoroute-proxy.
Package: tecount
TEcountA package to count reads mapping on transposable elements (TEs) subfamilies, families and classes in bulk RNA-seq.FeaturesTEcount counts high-throughput sequencing reads aligned on TEs on a reference genome.
It reports TE counts by subfamily, family and class on separate outputs.
In case of reads aligning on multiple TE loci, it counts only one alignment occurrence for each feature (i.e. subfamily, family or class).
It can use single or paired end BAM files, count in strand-specific manner and filter by a minimum read-TE overlap, among other features that can be discovered by runningTEcount --help.InstallUsing conda  (recommended)We recommend using conda, as it will install all the required packages along TEcount.condacreate-ntecount-cconda-forge-cbiocondatecountUsing pipIf for any reason it's not possible or desiderable to use conda, it can be installed with pip and the following requirements must be installed manually:python>=3.7,samtools>=1.14andbedtools>=2.30.0.pipinstalltecountUsageRequires a BAM file sorted by coordinates and a bed6+3 file with subfamily, family and class in columns 7, 8 and 9.TEcount-b<sorted_by_coord.bam>-r<rmsk.bed>Example using test dataset:TEcount-b./test/Aligned.sortedByCoord.out.bam-r./test/rmsk.GRCm38.chr19.bed.gzSee all available parameters with:TEcount--helpAcknowledgementsBased on the transposable elements RNA-seq quantification strategy implemented in1.References1. Marasca, F., Sinha, S., Vadalà, R. et al. LINE1 are spliced in non-canonical transcript variants to regulate T cell quiescence and exhaustion. Nat Genet 54, 180–193 (2022).https://doi.org/10.1038/s41588-021-00989-7
Package: tecton
Tecton is the fastest way to build operational machine learning applications. It helps automate real-time decision making like fraud detection, product recommendations, and search result ranking in production applications.Tecton is a fully-managed feature platform built to orchestrate the complete lifecycle of features, from transformation to online serving, with enterprise-grade SLAs.With Tecton, you can:Define and manage features using code in a git repository.Automatically compute and orchestrate batch, streaming, and real-time feature
transformations.Store and serve feature data for training and inference.Share, discover, and manage access to features.Contents of thetectonPyPI packageThetectonPyPI package bundles Tecton's Python SDK, which is used for defining and testing feature pipelines and services, and generating datasets for training and batch inference. For more information, see theSDK reference.Getting started resourcesTo get started using Tecton, see these resources, which are found in thedocumentation:Machine Learning Application ConceptsTecton ConceptsSetting up TectonTecton Fundamentals TutorialLicenseCopyright (2023) Tecton, Inc. This library (the "Software") may not be used except in connection with the Licensee's use
of the Tecton Platform Services (defined below) pursuant to an Agreement (defined below) between Licensee (defined
below) and Tecton, Inc. ("Tecton"). This Software shall be deemed part of the "Services" under the Agreement, or if the
Agreement does not define Services, then the term in such Agreement that refers to the applicable Tecton Platform
Services (as defined below) shall be substituted herein for "Services." Licensee's use of the Software must comply at
all times with any restrictions applicable to the Services, generally, and must be used in accordance with any
applicable documentation. If you have not agreed to an Agreement or otherwise do not agree to these terms, you may not
use the Software. This license terminates automatically upon the termination of the Agreement or Licensee's breach of
these terms. Agreement: the agreement between Tecton and Licensee governing the use of the Tecton Platform Services,
which shall be, with respect to Tecton, the Tecton Platform-as-a-Service Agreement located
athttps://www.tecton.ai/tecton_online-tos/, unless Licensee has entered into a separate written agreement with Tecton
governing the use of the Tecton Platform Services. Tecton Platform Services: the Tecton services according to where the
Software is used. Licensee: the user of the Software, or, if the Software is being used on behalf of a company, the
company.
Package: tecton-client
Python Client Library for Tecton Online Feature StoreA simple Python client for the Feature Server HTTP API that helps customers integrate with Tecton easily.DocumentationFetching Online FeaturesFeatureServer API ReferenceTecton Python Client API ReferenceTecton Python Client Example CodeTroubleshootingIf you have any questions or need help, pleaseopen an Issueor reach out to us on Slack!Installing the clientThe client can be installed usingpip.pipinstalltecton-clientThe client can then be used as below:tecton_client=TectonClient(url,api_key)get_features_request_data=GetFeaturesRequestData(join_key_map={"user_id":"123","merchant":"xyz"},request_context_map={"amt":500.00},)get_features_request=GetFeaturesRequest(workspace_name="<Your-workspace>",feature_service_name="fraud_detection_feature_service",request_data=get_features_request_data,)get_features_response=tecton_client.get_features(get_features_request)print([feature.feature_valueforfeatureinget_features_response.feature_values.values()])For more information, please refer to Tecton documentation on the Python Client Library.LicenseThe project is licensed
underApache License 2.0
Package: tectonics
Bravo!  You have received a Mercantilism Diploma in "fortunes" fromthe Orbital Convergence University International Air and Water
Embassy of the Tangerine Planet (the planet that is one ellipse further from
the Sun than Earth's ellipse).You are now officially certified to include "fortunes" in your practice.tectonicstaps (OCHLV)taps.season_3.super_hero_trend🎭 stats (options)stats.aggregate_break_evenstats.aggregate_PC_ratio🌩️ cloudsclouds.Coinbase.API.products.candles
Package: ted2mkv
ted2mkv as the name suggests, downloads and converts TED videosavailable on ted.com to mkv files.Features:- Can resume download at any stage.- Downloads and embeds all available subtitles in the mkv file.- Downloads and embeds the video summary text and other metadata in themkv file.Requirements:- mkvtoolnix (it uses mkvmerge to create the mkv file)For usage information, run ted2mkv with -h option.
Package: ted2zim
ted2zimGet the best :bulb: TED videos offline :arrow_down:An offliner to create ZIM :package: files from TED talksTED (Technology, Entertainment, Design) is a global set of conferences under the slogan "ideas worth spreading". They address a wide range of topics within the research and practice of science and culture, often through storytelling. The speakers are given a maximum of 18 minutes to present their ideas in the most innovative and engaging ways they can. One can eaisly find all the TED videoshere.This project is aimed at creating a sustainable solution to make TED accessible offline by creating ZIM files providing these videos in a similar manner like online.Getting started :rocket:Install the dependenciesMake sure that you havepython3,unzip,ffmpeg,wgetandcurlinstalled on your system before running the scraper (otherwise you'll get a warning to install them).Setup the packageOne can easily install the PyPI version but let's setup the source version.First, clone this repository.If you do not already have it on your system, install hatch to build the software and manage virtual environments (you might be interested by our detailedDeveloper Setupas well).pip3installhatchStart a hatch shell: this will install software including dependencies in an isolated virtual environment.hatchshellThat's it. You can now runted2zimfrom your terminalted2zim--topics[TOPICS]--name[NAME]For the full list of arguments, seethisfile or run the followingted2zim--helpExample usageted2zim--topics="augmented reality"--debug--name="augumented_reality"--format=mp4--title="Augmented Reality"--description="TED videos in AR category"--creator="TED"--publisher="openzim"--output="output"--keep--low-qualityThis project can also be run with docker. Use the providedDockerfileorpre-build imagesto run it with Docker. See stepshere.Features :robot:You can create ZIMs for multiple topics (should be same as givenhere), choose between different video formats (webm/mp4), different compression rates, and even use an S3 based cache.Want more flexibility? There's a multitoolted2zim-multiis an extra command available that allows you to do much more with the scraper. It falls back toted2zimif normal commands are passed. It supports creation of multiple ZIMs with single command for both playlists and topics and even getting metadata from a specified JSON file. It supports the following extra arguments ---indiv-zims- Allows you to create one zim/topic or one zim/playlist--{name|description|zim-file|title}-format- Allows you to add custom format for the equivalentted2zimarguments. You can add{identity}as a placeholder in these values to get the playlist ID / topic name in it's place (spaces replaced by-). You can now also add{slug}to get the topic/playlist slug.--metadata-from- Path to a JSON file containing the metadata.Should be of the following format:{"<playlist-id/topic-name-with-underscores>":{"name":"sample_name_{identity}","description":"Sample description","title":"Custom title","zim-file":"sample.zim","tags":"tag","creator":"Yourself","build-dir":"/custom_build_dir"}}Seeted2zim-multi --helpfor details.License :book:GPLv3or later, seeLICENSEfor more details.
Package: teddr
Nonparametric Estimation and Comparison of Distance Distributions from Censored DataTable of ContentsTable of ContentsAbstractInstallationLicenseAbstractTransportation distance data is a powerful resource, but granular location records are often censored due to privacy concerns or regulatory mandates. We describe thetransportation event distance distribution reconstructionproblem, which aims to handle this obstacle and has broad application to public health informatics, logistics, and more. We propose numerical methods to approximate, sample from, and compare distributions of distances between censored loscation pairs. We validate empirically on synthetic simulated data and demonstrate applicability to practical geospatial data analysis tasks. Our code is onGitHub.InstallationFrom PyPIpipinstallteddrFrom GitHubClone the repo fromhere(this repo).Install requirements:pipinstall-rrequirements.txtCitingIf you found our work helpful, please consider citing it with:@article{mccabe2023nonparametric,title={Nonparametric Estimation and Comparison of Distance Distributions from Censored Data},author={McCabe, Lucas H},journal={arXiv preprint arXiv:2311.02658},year={2023}}LicenseMIT
Package: teddy
# Teddy~~Teddy is not Pandas. Teddy is lightweight and snuggly.~~~~Teddy is straightforward and understands you. Teddy loves you.~~More importantly, Teddy is not ready for prime time.## Feature overviewTeddy loves you so much, it will work with vanilla Python types.```pythonfrom collectionsrecord = namedtupledata = list()```## Python WTFs### `__getattr__` also gets called for third-party AttributeErrorsDefault attribute access does also forward to your `__getattr__` if a property descriptor raisesan unrelated `AttributeError`.```In [6]: class A:...:     @property...:     def result(self):...:         return [1,2].a...:...:     def __getattr__(self, key):...:         print('hah')...:In [7]: A().resulthah```### `inspect.signature` doesn't get along with `@staticmethod` in custom object```Python 3.7.1 (default, Dec 10 2018, 22:54:23) [MSC v.1915 64 bit (AMD64)]Type 'copyright', 'credits' or 'license' for more informationIPython 7.2.0 -- An enhanced Interactive Python. Type '?' for help.In [1]: import inspectIn [2]: class A:...:     @staticmethod...:     def __call__(x,y):...:         return y...:In [3]: inspect.signature(A())Out[3]: <Signature (y)>In [4]: inspect.signature(A().__call__)Out[4]: <Signature (x, y)>```## TODOs* [ ] the code could become simpler if it used key, value pairs everywhere internally?* [ ] tests for attr_mapping* [ ] prettyprinter is a bit meh sometimes* [x] I need a read-only wrapper around dicts for using attrs instead of indexing.* [x] get rid of no_value?* [x] find another name for IndexedMapping?* [x] support indexing by another Teddy instance or an IndexedMapping.* [x] we need a sequential type that exposes a dict interface but stores a list internally...* ~~[ ] figure out a structure for POPO vs other possible impls~~* [x] add tests* [ ] figure out a name for different kinds of predicates* [x] add support for implicit lambdas* [x] support for a literal function that allows for accessing any constant in a dict that has special meaning for teddy.
Package: teddy-portal-gun
teddy-portal-gunAwesome Portal GunUsage:$teddy-portal-gun[OPTIONS]COMMAND[ARGS]...Options:--install-completion: Install completion for the current shell.--show-completion: Show completion for the current shell, to copy it or customize the installation.--help: Show this message and exit.Commands:load: Load the portal gunshoot: Shoot the portal gunteddy-portal-gun loadLoad the portal gunUsage:$teddy-portal-gunload[OPTIONS]Options:--help: Show this message and exit.teddy-portal-gun shootShoot the portal gunUsage:$teddy-portal-gunshoot[OPTIONS]Options:--help: Show this message and exit.
Package: tedent
TedentKeep your multi-line templated strings lookin' good :sunglasses:This is a python version oftedentTable of ContentsWhat is it?What does the name stand for?Why create it?Simple UsageQuestions about how the indentation works?Important Usage Notesinput requirementsTestWhat is it?A function similar todedentjust with different semanticsWhat does the name stand for?Template stringindentationnames are hardWhy create it?dedent didn't handle the following case like I wantedformattedBoroughs=f"""\['Brooklyn','Manhattan',]"""print(dedent(f"""\New York boroughs${formattedBoroughs}"""))## expected:# New York boroughs# [#     'Brooklyn',#     'Manhattan',# ]## actual:#         New York boroughs#         [#     'Brooklyn',#     'Manhattan',# ]#Simple Usageimporttedentfrom'tedent'## *note the lack of the backslash#print(tedent("""This will be indentedas you expect"""))# writes:# This will be indented#   as you expectQuestions about how the indentation works?Because the indentation logic is both young and convoluted, please refer tothe codeandtestsfor details. The
library is not that big and if you have any questions please create a
github issue.Important Usage NotesFirst of all, this library doesn't handle tabs. I will accept a PR
with supportSecondly, if you always usetedentlike the followingtedent("""some text""")then you shouldn't run into any issues. However we all know input can be
tricky sotedenthas a few input requirements in order to format your
string properly.input requirementsif the argument isn't a string then an error will be thrownif you pass a string with three or more newlines, thenthe first and last lines must contain only whitespacethe second line must contain a non-whitespace characteran error will be thrown if the above two conditions are not metif you pass a string with fewer than 3 newlinesif they only contain whitespace then an empty string is returnedotherwise an error is thrownfinally, all trailing whitespace from the result is strippedIf you have questions please raise a github issue.Test## you must have poetry installed#$poetryshell$poetryinstall$pythonrunTests.py
Package: tedfulk-kb-pycrawler
Python Web ScraperThis script utilizes thePlaywright libraryfor scraping websites and generating a knowledgebase. It is capable of outputting the scraped data in multiple formats, including JSON, plain text, and PDF.Main Functionsscrape_it(config: Config)Parametersconfig: An object containing URLs to scrape, output file names, file types, and a limit on pages to crawl.WorkflowLaunch Chromium Browser: Uses Playwright to start a new browser instance.URL Iteration: For each URL in theConfigobject:Sitemap Processing:Navigate tositemap.xml.RaiseNoSitemapErrorif not found, else extract URLs.Page Processing: For each URL in the sitemap:Stop ifmax_pages_to_crawlis reached.Navigate to the URL and extract the page content.Create aWebPageobject with URL and content.Clean content and add toKnowledgebaseobject.Output Generation:Callswrite_to_file(knowledgebase: Knowledgebase, config: Config)to generate outputs.Supports multiple output formats as specified inconfig(JSON, TXT, PDF).write_to_file(knowledgebase: Knowledgebase, config: Config)PurposeWrites the scraped data fromKnowledgebaseto files in the specified formats.FunctionalityDirectory Creation: Ensures the directory for storing files exists.File Generation:JSON Output: Stores theKnowledgebaseobject in a JSON file.Text Output: Creates a text file with all the page contents.PDF Output: Generates a PDF file with the content of each webpage.Unique FilenamesUsesget_output_filename(file_name: str, file_type: str)to ensure unique file names for each output file.string_to_pdf(knowledgebase: Knowledgebase, file_name)PurposeConverts the content of webpages in aKnowledgebaseto a PDF file.FunctionalityHandles UTF-8 encoding and falls back to DejaVu font if necessary.Generates a PDF with each page's URL and content.Error HandlingThe script raises aNoSitemapErrorif a sitemap.xml file is not found for a given URL.Errors and statuses are printed to the console for debugging and monitoring.RequirementsPlaywright for PythonFPDF library for PDF generationPydantic for data model validationUsagepoetryaddtedfulk-kb-pycrawlerplaywrightinstallimportasynciofromtedfulk_kb_pycrawlerimportscrape_it,Configconfig=Config(urls=["https://jxnl.github.io/instructor/",],output_file_name=["instructor"],output_file_type=["pdf"],max_pages_to_crawl=100,)asyncio.run(scrape_it(config))Activate your virtual environment:poetryshellRun the script:pythonmain.py
Package: tednet
tednettednetis a toolkit for tensor decomposition networks. Tensor decomposition networks are neural networks whose layers are decomposed by tensor decomposition, including CANDECOMP/PARAFAC, Tucker2, Tensor Train, Tensor Ring and so on. For a convenience to do research on it,tednetprovides excellent tools to deal with tensorial networks.Now,tednetis easy to be installed bypip:pipinstalltednetMore information could be found inDocument.Quick StartOperationThere are some operations supported intednet, and it is convinient to use them. First, import it:importtednetastdtCreate matrix whose diagonal elements are ones:diag_matrix=tdt.eye(5,5)A way to transfer the Pytorch tensor into numpy array:diag_matrix=tdt.to_numpy(diag_matrix)Similarly, the numpy array can be taken into Pytorch tensor by:diag_matrix=tdt.to_tensor(diag_matrix)Tensor Decomposition Networks (Tensor Ring for Sample)To use tensor ring decomposition models, simply calling the tensor ring module is enough.importtednet.tnn.tensor_ringastr# Define a TR-LeNet5model=tr.TRLeNet5(10,[6,6,6,6])
Package: ted-talk-video-downloader
ted_talk_video_downloaderAwesome ted_talk_video_downloader created by WagnoLeaoSergioInstall it from PyPIpipinstallted_talk_video_downloaderUsagefromted_talk_video_downloader.downloaderimportTED_Downloaderted_downloader=TED_Downloader()ted_downloader.process_mp4_filename("https://www.ted.com/talks/bozoma_saint_john_the_creative_power_of_your_intuition/up-next")ted_downloader.download_and_save("new_video","~/Downloads")$python-mted_talk_video_downloader[URL][OUTPUT_PATH]#or$ted_talk_video_downloader[URL][OUTPUT_PATH]DevelopmentRead theCONTRIBUTING.mdfile.
Package: tee
Python library to tee stderr / stdout to a fileInstallationpipinstallteeQuick Starttee_test.pyimportsysfromteeimportStdoutTee,StderrTeewithStdoutTee("mystdout.txt"),StderrTee("mystderr.txt"):sys.stdout.write("[stdout] hello\n")sys.stderr.write("[stderr] hello\n")sys.stdout.write("[stdout] world\n")sys.stderr.write("[stderr] world\n")sys.stdout.write("[stdout] not going to be written to file\n")sys.stderr.write("[stderr] not going to be written to file\n")$pythontee_test.py[stdout]hello[stderr]hello[stdout]world[stderr]world[stdout]notgoingtobewrittentofile[stderr]notgoingtobewrittentofile$catmystdout.txt[stdout]hello[stdout]world$catmystderr.txt[stderr]hello[stderr]worldFiltersStdoutTee and StderrTee take filters as parameters which run before
writing to a file or the stream. These filters are callables that take
the message to be written as input and return either None or a new
message.I find them particularly useful when you want to write colorized output
to the stream, but strip out the control characters when writing to a
file, especially when using fabric.importreimportteefromfabric.apiimportrundef_remove_control_chars(message):returnre.sub(r'(\x9B|\x1B\[)[0-?]*[ -\/]*[@-~]',"",message)defecho_color():withtee.StdoutTee("fabout.txt",mode="a",file_filters=[_remove_control_chars]):run("""echo -e "\E[1;32mHello World \E[4;31mLets add some\E[0m\E[1;34m color" && tput sgr0""")fab-Hlocalhostecho_color