Saravanan666/pypi-org huggingface dataset:
Package: udc
UDCThe Universal Data ClientWhat is UDC?Universal Data is an open source initiative to build
a decentralized, cryptographically-secure ecosystem
containerizing both structured and unstructured data.UDC is the initial (alpha) client for that system.
It currently supports both Quilt and Benchling URIs.
For example:udc list "quilt+s3://quilt-example#package=examples/wellplates"udc patch "benchling+https://dtt.benchling.com?name=Update#type=Entry&id=etr_123"InstallationNOTE: UDC requires Python 3.10 or higher.
You can check your version withpython3 --version.
If you have an older version, you will need to update your environment to anewer version of Python.Production PackageFrom PyPi:python3-mpipinstalludc# ORpython3-mpipinstall--upgradeudc
whichudcDevelopment Branchpython3-mpipinstallgit+https://github.com/data-yaml/udc.git@mainClonedWhen cloned fromGitHub:poetryinstall<--aliasudc="poetry run udc"-->Usageudc# prints helpList contents of a specific package instanceudclist"quilt+s3://quilt-example#package=examples/wellplates@6782cf98a2"e.g.,"quilt+s3://quilt-example#package=examples/wellplates@6782cf98a2&path=README.md""quilt+s3://quilt-example#package=examples/wellplates@6782cf98a2&path=autoplate_H1N1.csv""quilt+s3://quilt-example#package=examples/wellplates@6782cf98a2&path=data_products.ipynb""quilt+s3://quilt-example#package=examples/wellplates@6782cf98a2&path=neutralisation-altair.json""quilt+s3://quilt-example#package=examples/wellplates@6782cf98a2&path=neutralisation.json""quilt+s3://quilt-example#package=examples/wellplates@6782cf98a2&path=quilt_summarize.json""quilt+s3://quilt-example#package=examples/wellplates@6782cf98a2&path=render.html"List all versions of a packageudclist"quilt+s3://quilt-example#package=examples/wellplates"|head-n1e.g.,"quilt+s3://quilt-example#package=examples/wellplates@6782cf98a2c600f4c519efd5de868d5ef1e05ac92fcb0fa56044bb8c925c5f02"List all packages in a registryudclistquilt+s3://quilt-example|head-n1e.g.,"quilt+s3://quilt-example#package=akarve/amazon-reviews:latest"Get a package into a specific directoryYou can also useput(replace) andpatch(merge) if you have write access.udcget"quilt+s3://quilt-example#package=examples/wellplates@6782cf98a2"--dir/tmp/wellplatesChecking the download directory:ls/tmp/wellplatesShould reveal the following output:README.md
autoplate_H1N1.csv
data_products.ipynb
neutralisation-altair.json
neutralisation.json
quilt_summarize.json
render.htmlDevelopmentTestingmaketestContinuous MonitoringmakewatchCreate PackageWARNING: Do this only if you are the maintainer of the package.Be sure you to first set your~/.pypircusingpoetry config pypi-token.pypi <pypi-api-token># merge PRmaketag
makepypi
poetrypublish
Package: udchinese
UD-ChineseTokenizer, POS-Tagger, and Dependency-Parser for Chinese (简体/繁體/文言文), working onUniversal Dependencies.Basic usage>>>importudchinese>>>zh=udchinese.load()>>>s=zh("我把这本书看完了。吾既讀是書也。")>>>print(s)# newdoc# newpar# sent_id = 1# text = 我把这本书看完了。1我我PRONn,代名詞,人称,止格Person=1|PronType=Prs6nsubj_SpaceAfter=No2把把ADPBB_5case_SpaceAfter=No3这這DETDT_4det_SpaceAfter=No4本本NOUNn,名詞,描写,形質_5clf_SpaceAfter=No5书書NOUNn,名詞,主体,書物_6obl:patient_SpaceAfter=No6看看VERBv,動詞,行為,動作_0root_SpaceAfter=No7完完VERBv,動詞,変化,性質_6flat:vv_SpaceAfter=No8了了PARTUH_6discourse_SpaceAfter=No9。。PUNCTs,記号,句点,*_6punct_SpacesAfter=\n# sent_id = 2# text = 吾既讀是書也。1吾吾PRONn,代名詞,人称,起格Person=1|PronType=Prs3nsubj_SpaceAfter=No2既既ADVv,副詞,時相,完了AdvType=Tim|Aspect=Perf3advmod_SpaceAfter=No3讀讀VERBv,動詞,行為,動作_0root_SpaceAfter=No4是是PRONn,代名詞,指示,*PronType=Dem5det_SpaceAfter=No5書書NOUNn,名詞,主体,書物_3obj_SpaceAfter=No6也也PARTp,助詞,句末,*_3discourse:sp_SpaceAfter=No7。。PUNCTs,記号,句点,*_3punct_SpacesAfter=\nUsage via spaCyIf you have already installedspaCy2.1.0 or later, you can use UD-Chinese via spaCy Language pipeline.>>>importudchinese.spacy>>>zh=udchinese.spacy.load()>>>d=zh("我把这本书看完了。吾既讀是書也。")>>>print(type(d))<class'spacy.tokens.doc.Doc'>>>>print(udchinese.spacy.to_conllu(d))# text = 我把这本书看完了。1我我PRONn,代名詞,人称,止格_6nsubj_SpaceAfter=No2把把ADPBB_5case_SpaceAfter=No3这這DETDT_4det_SpaceAfter=No4本本NOUNn,名詞,描写,形質_5clf_SpaceAfter=No5书書NOUNn,名詞,主体,書物_6obl:patient_SpaceAfter=No6看看VERBv,動詞,行為,動作_0root_SpaceAfter=No7完完VERBv,動詞,変化,性質_6flat:vv_SpaceAfter=No8了了PARTUH_6discourse_SpaceAfter=No9。。PUNCTs,記号,句点,*_6punct__# text = 吾既讀是書也。1吾吾PRONn,代名詞,人称,起格_3nsubj_SpaceAfter=No2既既ADVv,副詞,時相,完了_3advmod_SpaceAfter=No3讀讀VERBv,動詞,行為,動作_0root_SpaceAfter=No4是是PRONn,代名詞,指示,*_5det_SpaceAfter=No5書書NOUNn,名詞,主体,書物_3obj_SpaceAfter=No6也也PARTp,助詞,句末,*_3discourse:sp_SpaceAfter=No7。。PUNCTs,記号,句点,*_3punct__Installation for LinuxBinary-wheel is available for Linux, and is installed by default when you usepip:pipinstalludchineseInstallation for CygwinMake sure to getgcc-g++python37-pippython37-develpackages, and then:pip3.7installudchineseUsepython3.7command inCygwininstead ofpython.Installation for Jupyter Notebook (Google Colaboratory)!pipinstalludchineseAuthorKoichi Yasuoka (安岡孝一)
Package: ud-compatibility
ud-compatibilityUtility for converting Universal Dependencies–annotated corpora to UniMorphTheUniversal Dependencies (UD)andUniversal Morphology (UniMorph)projects each present schemata for annotating the morphosyntactic details of a language.
Each project also provides corpora of annotated text in many languages—UD at the token level and UniMorph at the type level.
As each corpus is built by different annotators,language-specific decisions hinder the goal of universal schemata.To ease this interoperability, we present a deterministic mappingfrom Universal Dependencies v2 features into the UniMorph schema.Prerequisitestermcolor:pip install termcolorPython 3.5 or later; Anaconda is a simple way to install it.UsageThe driver of the entire endeavor is the filemarry.py, which marries a UD dataset to its affiliated UniMorph.ConversionTo convertonefile to UniMorph, give the path (and optionally the specific language converter you'd like to use).pythonmarry.pyconvert--udmy/ud/path/rw-ud-dev.conllu
pythonmarry.pyconvert--udmy/ud/path/da-ud-dev.conllu-ldaTo convert your UD dataset to UniMorph, list the languages you'd like to convert:pythonmarry.pyconvert--langsherodeitno_bokmaal(You'll need to update the paths inpaths.pyto reflect where your UD (and UniMorph, if evaluating) data are stored.)When the input looks like this:# sent_id = es-train-001-s21
# text = Tiene 2 madres.
1	Tiene	tener	VERB	_	Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin	0	root	_	_
2	2	2	NUM	_	NumType=Card	3	nummod	_	_
3	madres	madre	NOUN	_	Gender=Masc|Number=Plur	1	obj	_	SpaceAfter=No
4	.	.	PUNCT	_	_	1	punct	_	_The output will look like this:# sent_id = es-train-001-s21
# text = Tiene 2 madres.
1	Tiene	tener	VERB	_	PRS;V;FIN;3;IND;SG	0	root	_	_
2	2	2	NUM	_	NUM	3	nummod	_	_
3	madres	madre	NOUN	_	N;PL;MASC	1	obj	_	SpaceAfter=No
4	.	.	PUNCT	_	_	1	punct	_	_EvaluationTo assess a conversion (either of the includedTranslatorobjects or your own), the syntax is similar:pythonmarry.pyevaluate--langsherodeitno_bokmaal(You'll need to update the paths inpaths.pyto reflect where your UD (and UniMorph, if evaluating) data are stored.)ReplicationTo replicate the experiments from the paper, use:pythonmarry.pyreplicateDataThe individual datasets for Universal Dependencies v2 and UniMorph can be downloaded from their respective projects on GitHub.ContributingYou're welcome to submit a pull request, harmonizing your UD dataset with the corresponding UniMorph.Write your ownTranslatorsubclass.Register it in thelanguages.pylist.Submit the Pull Request.LicenseThis project is licensed under the GNU GPL v3 license; see theLICENSE.mdfile for details.Citation@InProceedings{mccarthy2018udw,
  author = 	"McCarthy, Arya D.
		and Silfverberg, Miikka
		and Cotterell, Ryan
		and Hulden, Mans
		and Yarowsky, David",
  title = 	"Marrying {U}niversal {D}ependencies and {U}niversal {M}orphology",
  booktitle = 	"Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)",
  year = 	"2018",
  publisher = 	"Association for Computational Linguistics",
  pages = 	"91--101",
  location = 	"Brussels, Belgium",
  url = 	"http://aclweb.org/anthology/W18-6011"
}
Package: ud-dist-meta
Distribution metadataThis package is meant to provide information scrappers on distributions / repositories installed in the running environment.
It provides a command line api and python sdk, i.e.:Command lineTo gather metadata information from a distribution (e.g. spam) installed in the same environment asdist_meta:gen_distinfo_cmd spam path\to\local\folder\configurations.yamlThis instruction will generate a YAML configuration file at the given locationPythonThe base content of the package can be directly leverage, either through a script interface:importdist_metadist_meta.gen_distinfo_py("spam",r"path\to\local\folder\configurations.yaml")Or directly leveraging the core package:importdist_metainfo_spam=dist_meta.DistInfo.from_env("spam")print(info_spam.entry_points)print(info_spam.environment)print(info_spam.libraries)print(info_spam.requirements)info_spam.to_yaml(r"path\to\local\folder\configurations.yaml")
Package: uddsketch
uddsketchdata structure.Simple examplefromuddsketchimportUDDSketchhist=UDDSketch(initial_error=0.01)foriinrange(0,100):hist.add(0.1*i)q=hist.quantile(0.5)print('quantile:{}'.format(q))InstallationInstallation process is simple, just:$ pip install uddsketchChanges0.0.1Initial release.
Package: uddup
UDdup - URLs Deduplication ToolThe tool gets a list of URLs, and removes "duplicate" pages in the sense
of URL patterns that are probably repetitive and points to the same web template.For example:https://www.example.com/product/123
https://www.example.com/product/456
https://www.example.com/product/123?is_prod=false
https://www.example.com/product/222?is_debug=trueAll the above are probably points to the same product "template".
Therefore it should be enough to scan only some of these URLs by our various scanners.The result of the above after UDdup should be:https://www.example.com/product/123?is_prod=false
https://www.example.com/product/222?is_debug=trueWhy do I need it?Mostly for better (automated) reconnaissance process,
with less noise (for both the tester and the target).ExamplesTake a look atdemo.txtwhich is the raw URLs file which results indemo-results.txt.InstallationWith pip (Recommended)pipinstalluddupManual (from code)# Clone the repository.gitclonehttps://github.com/rotemreiss/uddup.git# Install the Python requirements.cduddup
pipinstall-rrequirements.txtUsageuddup -u demo.txt -o ./demo-result.txtMore Usage Optionsuddup -hShort FormLong FormDescription-h--helpShow this help message and exit-u--urlsFile with a list of urls-o--outputSave results to a file-s--silentPrint only the result URLs-fp--filter-pathFilter paths by a given RegexFilter Paths by RegexAllows filtering custom paths pattern.
For example, if we would like to filter all paths that starts with/productwe will need to run:# Single Regexuddup-udemo.txt-fp"^product"Input:https://www.example.com/
https://www.example.com/privacy-policy
https://www.example.com/product/1
https://www.example2.com/product/2
https://www.example3.com/product/4Output:https://www.example.com/
https://www.example.com/privacy-policyAdvanced Regex with multiple path filtersuddup-udemo.txt-fp"(^product)|(^category)"ContributingFeel free to fork the repository and submit pull-requests.SupportCreate new GitHub issueWant to say thanks? :) Message me onLinkedinLicenseMIT license
Package: udemydl
A Python script to download lectures from udemy.com.PrerequisitesPython (2 or 3)pip(Python Install Packager)Python modulerequestsIf missing, it's automatically installed bypipPreinstallIf you don't havepipinstalled, look at theirinstall doc.
On Mac it's simple as just running:brew install pipInstall udemy-dludemydlcan be installed usingpippip install udemydlor you can clone the repo and install it withmakegit clone https://github.com/lestex/udemydl
cd udemy-dl &&  make allUsageSimply calludemydlwith the full URL to the course page.udemydl https://www.udemy.com/COURSE_NAMEudemydlwill ask for your udemy username (email address) and password then start downloading the videos.By default,udemydlwill create a subdirectory based on the course name (be shure that no traling / sign is uncluded).  If you want to download files to a specific location, use the-o /path/to/directory/parameter.You can include the username/email and password on the command line using the -u and -p parameters.udemydl -u user@domain.com -p $ecRe7w0rd https://www.udemy.com/COURSE_NAMEFor information about all available parameters, use the--helpparameterudemydl --helpRun indebugmodeudemydl https://www.udemy.com/COURSE_NAME --debugUninstalludemydlcan be uninstalled usingpip:pip uninstall udemydlor from source code directory with:make u
Package: udemy-python
UDEMY-PYTHONA better python wrapper for the Udemy Instructor APIInstallInstall$ pip install udemy-pythonUse>>> from udemy import ApiAuthenticateIn order to use this module, you must supply an instructor API token.
This can be created by logging into Udemy and creating an instructor API client.
Once you have generated the token, the API can be set up with:>>> udemy_api = Api(<TOKEN>)UseHere's a few examples of ways you can use this wrapper.CoursesHere's a simple script to get all the unanswered questions from each course.for course in udemy_api.get_all_courses():
    print("Getting questions for", course.title)
    for question in course.get_questions(status="unanswered"):
        print("Unanswered question from", question.user.title)
        print(question.title)
        print(question.content)ReviewsOr to get all your bad reviews.for review in udemy_api.get_all_reviews(stars=[1, 2, 3], page_size=100):
    print("\nBad review from", review.user.name, ":(")
    print(review.stars, "stars")
    print(review.content)Everything ElseThese are just a couple examples of what you can accomplish with this wrapper. Anything
you can see on the API is possible, and I will try my best to update this if any changes
are made. If you have any issues or errors, please log a issue on the GitHub repo. Want
to add a feature? Submit a pull request!PhilosophyThe design philosophy behind this API wrapper was to abstract away as much of the
annoying raw json processing as possible. Unlike every other Udemy API wrappers, each
model is represented as an object with easily accessible attributes holding the data you
want. This wrapper also handles pagination for you, wrapping it up in a generator you can
easily iterate through. However, another one of my goals was to not remove any functionality
of the original API. Every option shown in the docs is still available, including setting
manual pages, page sizes, ordering, and filtering. Most of the names in the API are
unchanged, however a few were changed to more sensible alternatives, such as the Answer
'body' to 'content' in order to stay consistent with the other models.TODOMake additional helper functionsAdd bad argument errorsWrite docsAdd C O M M E N T SAdd Affiliate APIAdd private API?Figure out the meaning of Life
Package: udemy-scraper
Titleudemy ScraperDescrptionWith udemy Scraper you can scrape search results and extract the contents produced by the search result.In udemy scraper it will scrape the data present of the website and give json data which contains the details of the contents on the website.JSON sample data{"Scraper_lecture_min":[{"column_0":[{"text":"Course Learning Contest00:16"},{"text":"Course IntroductionPreview01:39"},{"text":"Introduction to AWS NetworkingPreview19:11"},{"text":"AWS Networking fundamentals7 questions"}],"section_title":"Introduction","text_section_hidden_on_mobile_content":"3 lectures \u2022 21min"}Click Here for moreRun Scraperfromudemy_scraperimport*link="https://www.udemy.com/course/aws-certified-advanced-networking-specialty-ans/"data=run_udemy_scraper(link)How it works?It takes URL of udemy page with a search keyword to scrape the data.It generates the json data which contains the information of the udemy search result.It gives the every detail present inside website in the form of json data.ExamplesBelow are some of the examples of URLs using which you can scrape:Example 1Example2Queries/ FeedbackIf you have some queries or feedback please contact us at followingTelegramEmail
Package: udeploy
udeployПростой скрипт на питоне, который поможет развернуть ваше приложение.
Данная утилита представляет собой обёртку над такой стандартной
утилитойsystemdв *nix подобных системах.Установкаuser@server$pipinstalludeploy--upgradeИспользованиеПревратить файл в демонНапример у вас есть проект телеграм бота на python, где главная программа называется bot.py.
Чтобы превратить ваше приложение в демон, перейдите в папку вашего проекта:user@server$ cd $YOUR_WORK_DIRECTORYИ наберите простую коммандуuser@server$udeploy-fbot.pyИЛИuser@server$udeploybot.py-fДанная комманда автоматом создаст service из вашего приложения, и переместит в нужную директорию,
сделав его видимым для systemd. Причём рабочей дирректорией демона по умолчанию ставится дирректория
вашего проекта. Это важно, если в проекте вы использовали относительные пути. Также названием демона
будет являтся название папки вашего проекта. Данная комманда только создаёт демон, его ещё надо
запустить, добавить в автозапуск и тд.Превратить Django проект в демонuser@server$udeploy--djangoДанная комманда просматривает директорию проекта и ищет там файлmanage.py.Дальше при помощи него запускает сервер по адресу0.0.0.0:80.То естьНЕна стандартном127.0.0.1:8000.Это сделано потому что, если хоститить на localhost,то не получится подключиться к приложению извне.Также нельзя забывать, что в настойках Djangosettings.pyвнутри списка ALLOWED_HOSTS нужно указыватьдоменилиip, так как это уже продакшен.Запустить демонДля этого опять же в папке проекта просто выполните:user@server$udeploystartИ не нужно указывать никакого названия демона. Программа сама автоматом определит имя папки
и на основании этого запустит нужный процесс. Это комманда только один раз запустит ваше приложение
как демон. То есть теперь вы можете отключиться от сервера и приложение все ещё будет работать как
независимый процесс.Добавить в автозапускuser@server$udeployenableТеперь при перезагрузке сервера, вместе со всеми программами будет стартовать и ваше приложение.Остановить демонuser@server$udeploystopУбрать демон из автозапускаuser@server$udeploydisableУдалить демонuser@server$udeployremoveПолезно, если вы ошиблись в параметрах при создании демона и не хотите захломлять сервер ненужными
нерабочими демонами. Данная коммада удаляет service файлы демона и перезагружает systemd,
а если ваше приложение в это время работало, то ещё и останавливает его.Показать подсказкуuser@server$udeploy--help
Package: udf
Pyplatform is a data analytics platform architeture built around Google BigQuery in a hybrid cloud environment.the platorm:provides fast, scalable and reliable SQL database solutionabstracts away the infrastuture by builiding data pipelines with serverless compute solutions in python runtime environmentssimplifies development environment by using jupyter lab as the main toolInstallationpipinstallpyplatformSetting up development environmentgit clone https://github.com/mhadi813/pyplatform
cd pyplatform
conda env create -f pyplatform_dev.ymlEnvironment variablesimportosos.environ['GOOGLE_APPLICATION_CREDENTIALS']='path/to/default_service_account.json'os.environ['DATASET']='default_bigquery_dataset_name'os.environ['STORAGE_BUCKET']='default_storage_bucket_id'Usagecommon data pipeline architectures:-Http sources-On-prem servers-Bigquery integration with Azure Logic Apps-Event driven ETL process-Streaming pipelinesExploring modulesimportpyplatformaspyppyp.show_me()
Package: udft
UDFT: Unitary Discrete Fourier Transform (and related)This module implements unitary discrete Fourier transform, that is orthonormaldet(F) = 1andF⁻¹ = F^*. This module existed before the introduction of thenorm="ortho"keyword and is now a very (very) thin wrapper around Numpy,pyFFTW, and Scipy (maybe others in the
future), mainly done for my personal usage. There is also functions related to
Fourier and convolution likeir2fr.It is useful for convolution [1]: they respect the Perceval equality, e.g., the
value of the null frequency is equal to1/√N * ∑ₙ xₙ.[1] B. R. Hunt "A matrix theory proof of the discrete convolution theorem", IEEE
Trans. on Audio and Electroacoustics, vol. au-19, no. 4, pp. 285-288, dec. 1971If you are having issues, please let me knowfrancois.orieux AT l2s.centralesupelec.frInstallation and documentationUDFT is just the fileudft.pyand depends onnumpyand Python 3.7+ only.
Optional dependency arepyFFTWandscipy. Documentation ishere. I recommend usingpoetryfor installationpoetry add udftorpoetry add udft[fftw]to installpyFFTW, or pippip install udftFor a quick and dirty installation, just copy theudft.pyfile: it is
quite stable, follow theSemantic
Versioning, and major changes are
unlikely.The code is hosted onGitHub.LicenseThe code is in the public domain.
Package: udict
udict.pyA simple module to get pages from Urban DictionaryInstallingPython 3.5 or higher is required# Linux/macOS
python3 -m pip install -U udict.py

# Windows
py -3 -m pip install -U udict.pyExample UsageimportudictmyPage=udict.get_page("Python")topDef=myPage.definitions[0]print(topDef.meaning)#Prints top definition of a term.
Package: udiff
UdiffUdiffis a simple library for parsing unified diff.>>>importudiff>>>d=udiff.UdiffParser('diff --git a/sample b/sample\nindex 0000001..0ddf2ba\n--- a/sample\n+++ b/sample\n@@ -1 +1 @@\n-test\n+test1r\n')>>>d.object{'options':{'encoding':None,'dst_prefix':None,'src_prefix':None,'diff_max_changes':None,'diff_max_line_length':None,'diff_too_big_message':None},'files':[{'deleted_lines':1,'added_lines':1,'is_git_diff':True,'checksum_before':'0000001','checksum_after':'0ddf2ba','old_name':'sample','language':'','new_name':'sample','is_combined':False,'blocks':[{'old_start_line':1,'old_start_line_2':None,'new_start_line':1,'header':'@@ -1 +1 @@','lines':[{'source_line_no':1,'target_line_no':None,'line_type':'-','content':'-test'},{'source_line_no':None,'target_line_no':1,'line_type':'+','content':'+test1r'}]}]}]}>>>d.getitem('sample').added_lines1>>>d.getitem('sample').deleted_lines1Installing Requests and Supported VersionsUdiff is available on PyPI:$python-mpipinstalludiffUdiff supports Python 2.7 & 3.5+.UsageParse from file:>>>fromudiffimportUdiffParser>>>d=UdiffParser.from_filename(path_to_file)Parse string:>>>fromudiffimportUdiffParser>>>withcodecs.open(path_to_file,'r',encoding='utf-8')asdiff:>>>d=UdiffParser.from_filename(diff.read())Options:src_prefix: add a prefix to all source (before changes) filepaths, default is''. Should match the prefix used whengenerating the diff.dst_prefix: add a prefix to all destination (after changes) filepaths, default is''. Should match the prefix used
whengenerating the diffdiff_max_changes: number of changed lines after which a file diff is deemed as too big and not displayed, default isNonediff_max_line_length: number of characters in a diff line after which a file diff is deemed as too big and not
displayed, default isNonediff_too_big_message: message for file diff too big, defaultDiff too big to be displayed