Saravanan666/pypi-org huggingface dataset:
Package: engine-function-extension
# Python worker extension for running OL engine functionMore details about the Python worker extension: [azure function doc](https://docs.microsoft.com/en-au/azure/azure-functions/functions-reference-python?tabs=asgi%2Cazurecli-linux%2Capplication-level#python-worker-extensions)## Using this extension
This extension process that runs in Azure Functions lets us do some setup in our [engine function app](https://github.com/OpenLearningNet/OpenLearningEngine):django.setup()copy hosts alias to the function running vmTo use this extension, you only need tofrom engine_function_extension import AppExtensionto your function script file, see [this example](https://github.com/OpenLearningNet/OpenLearningEngine/blob/master/AZ_TaskHandler/main.py).
Package: engine-grpc
Command interface examplesUEI().command_parser(cmd=GRPCInterface.method_object_add_component,params=[target,ASSEMBLE_NEARINTERACTIONGRABBABLE,True])UEI().command_parser(cmd=GRPCInterface.method_object_add_component,params=[target,ASSEMBLE_MESHCOLLIDER,True])UEI().command_parser(cmd=GRPCInterface.method_object_set_reference_value,params=[collision_mesh_asset,ASSETBLE_MESHFILTER,"sharedMesh",target,ASSEMBLE_MESHCOLLIDER,"sharedMesh"])UEI().command_parser(cmd=GRPCInterface.method_object_set_value,params=[target,ASSEMBLE_MESHCOLLIDER,"convex",True])
Package: engineio-client
Python implementation of the engine.io client.Design & goalsThis implementation is inspired by the JavaScriptengine.io-clientimplementation.Protocol parser is copied in parts and at least largely inspired from the
packagepython-engineiowritten byMiguel Grinberg.This engine.io client is usingGeventfor now. This is not a strict design
choice but a simplification for this first implementaion. Other asynchronous
frameworks are welcome for future versions.Examplefrom engineio_client.client import Client

import gevent
import gevent.monkey
gevent.monkey.patch_all()

eio = Client('localhost', 8000)

@eio.on('message')
def on_message(message):
    print message
    eio.close()

@eio.on('open')
def on_open():
    eio.send("Hello")

eio.open()
gevent.wait()LinksAnother engine.io/socket.io client:socketIO_client
Package: eng_join
This module is pretty self-explanatory.::>>> from eng_join import join>>> join(['blue', 'red', 'yellow'], conj='or', im_a_moron=True)
'blue, red or yellow'
>>> join(['blue', 'red', 'yellow'], conj='or')
'blue, red, or yellow'
>>> join(['blue', 'red'], conj='or')
'blue or red'
>>> join(['blue', 'red'], conj='and')
'blue and red'
>>> join(['blue'], conj='and')
'blue'
>>> join(['blue', 'red', 'yellow', 'green', 'yello'], conj='and')
'blue, red, yellow, green, and yello'
Package: englewood
Englewood is a python library for generating maps from maps.Currently only dot-density maps can be generated, but hopefully other types of maps (contigous and non-contigous cartograms, scaled centroids markers, etc) will follow.InstallationIt’s easy!pip install englewoodUsageSee demo.py for an example of how to generate a dot density map.Note: data for the demo is sourced fromReshaping Chicago’s political map. The Chicago Wards shapefile the City of Chicago’sdata portal.AuthorsChristopher Groskopf (@onyxfish)LicenseMIT
Package: english-asr
english_asrAn Automatic Speech Recognition (ASR) models in TensorFlow Lite (TFLite) for Speech-To-Text conversion.Installationpip3 install english-asrSupported ModelConformer TransducerusingLibriSpeechdataset.How To Runfrom english_asr.conformer import get_text_from_speech
out = get_text_from_speech('audio.wav')```
Package: english-checkers
The package realizesEnglish chekersgame simulation.Basic usageLet’s create a game with RandomAI:fromcheckers.gameimportGame,GameState,Colorfromcheckers.moveimportMovefromcheckers.ai.random_aiimportRandomAIdefmain():size=8game=Game(size)ai_color=Color.WHITEai=RandomAI(size,ai_color)ifai_color==Color.BLACK:move=Nonewhilegame.state==GameState.UNFINISHED:print(game.board)print('b:',game.black_count,'w:',game.white_count)print(game.turn)ifgame.turn==ai_color:move=ai.make_move(move)else:moves=game.get_all_moves()fori,moveinenumerate(moves):print(i+1,':',move)print('Input number of the move for move:')try:move_number=int(input().strip())exceptValueError:print('Wrong input. Number of the move must be int')try:move=moves[move_number-1]exceptIndexError:print('Wrong move')continuegame.make_move(move)else:print(game.board)print(game.state)if__name__=='__main__':main()
Package: english-define
English DictionaryUsageimportedictionaryaseditdefine(word:str, limit:int=50:)Argumentsword- Astrof a single word to define.Requiredlimit-intthat limits how many results, ordered by popularity.Default:50ReturndictExample>>>edict.define("hello")[{'partOfSpeech':'exclamation','definitions':[{'definition':'used as a greeting or to begin a phone conversation.','example':'hello there, Katie!','synonyms':[],'antonyms':[]}]},{'partOfSpeech':'noun','definitions':[{'definition':'an utterance of ‘hello’; a greeting.','example':'she was getting polite nods and hellos from people','synonyms':[],'antonyms':[]}]},{'partOfSpeech':'verb','definitions':[{'definition':'say or shout ‘hello’.','example':'I pressed the phone button and helloed','synonyms':[],'antonyms':[]}]}]phonetics(word:str, limit:int=50:)Argumentsword- Astrof a single word to find phonetics for.Requiredlimit-intthat limits how many results, ordered by popularity.Default:50ReturndictExample>>>edict.phonetics("hello")[{'text':'həˈləʊ','audio':'//ssl.gstatic.com/dictionary/static/sounds/20200429/hello--_gb_1.mp3'},{'text':'hɛˈləʊ'}]
Package: english-detector
English DetectorA python package to detect english text.Table of ContentsInstallationUsageSupportContributingInstallationInstall the package using pip:pipinstallenglish-detectorUsagefromenglish-detectorimportdetect_englishtext='''Hansel and Gretel are a brother and sister abandoned in a forest, where they fall into the hands of a witch who lives in a house made of gingerbread, cake, and candy. The cannibalistic witch intends to fatten Hansel before eventually eating him, but Gretel pushes the witch into her own oven and kills her.'''print(detect_english(text))# prints TrueSupportPleaseopen an issuefor support.ContributingPlease contribute usingGithub Flow. Create a branch, add commits, andopen a pull request.
Package: englishdictionary
English DictionaryUsagedefine(word:str, limit:int=50:)Argumentsword- Astrof a single word to define.Requiredlimit-intthat limits how many results, ordered by popularity.Default:50ReturndictExample>>>edict.define("hello")[{'partOfSpeech':'exclamation','definitions':[{'definition':'used as a greeting or to begin a phone conversation.','example':'hello there, Katie!','synonyms':[],'antonyms':[]}]},{'partOfSpeech':'noun','definitions':[{'definition':'an utterance of ‘hello’; a greeting.','example':'she was getting polite nods and hellos from people','synonyms':[],'antonyms':[]}]},{'partOfSpeech':'verb','definitions':[{'definition':'say or shout ‘hello’.','example':'I pressed the phone button and helloed','synonyms':[],'antonyms':[]}]}]phonetics(word:str, limit:int=50:)Argumentsword- Astrof a single word to find phonetics for.Requiredlimit-intthat limits how many results, ordered by popularity.Default:50ReturndictExample>>>edict.phonetics("hello")[{'text':'həˈləʊ','audio':'//ssl.gstatic.com/dictionary/static/sounds/20200429/hello--_gb_1.mp3'},{'text':'hɛˈləʊ'}]
Package: english-dictionary
In-memory Python dictionary of the English language for easy access in NLP applications.To my knowledge, this does not exist. Current dictionary packages rely on API calls under the hood, which are obviously extremely slow compared to key lookup.Installpip install english_dictionaryUsagefrom english_dictionary.scripts.read_pickle import get_dict
english_dict = get_dict()

english_dict["xylophone"]  # english_dict is a Python dictionary of English
Package: englishexelog
englishexelogThis is a simple python module to log simple messages into Log.log,Install:
pip install englishexelogImport:
import englishexelog as logUsage:
log.log(" OK ", "Loaded!")log.logfile(" OK ", "Loaded!", "Log.log")log.logadd("User1", " OK ", "Loaded!")log.logaddfile("User1", " OK ", "Loaded!", "Log.log")log.wipe("Log.log")
Package: english-pidgin-dictionary
This is a simple python library that is useful for pidgin and english word interpretation.####How to use the packagesfrom english_pidgin_dict.english_pidgin_dict import getMeaningtyped_desired_word = input("enter yr desired word here")meaning = getMeaning(typed_desired_word)
print(meaning)#printing meaning of the word in consoleif type(meaning) == list:for item in meaning:print(item)else:print(meaning)PIP PACKAGE DEPENDECIESdifflib (built-in python package)KINDLY MAKE PR for UPGRADE....
Package: english-text-normalization
english-text-normalizationCommand-line interface (CLI) and library to normalize English texts.Installationpipinstallenglish-text-normalization--userUsage as CLI# Show supported normalizing operationsnorm-eng-clilist-operations-h# Normalizenorm-eng-clinormalize-hUsage as a libraryfromenglish_text_normalizationimport*Development setup# updatesudoaptupdate# install Python 3.8, 3.9, 3.10, 3.11 & 3.12 for ensuring that tests can be runsudoaptinstallpython3-pip\python3.8python3.8-devpython3.8-distutilspython3.8-venv\python3.9python3.9-devpython3.9-distutilspython3.9-venv\python3.10python3.10-devpython3.10-distutilspython3.10-venv\python3.11python3.11-devpython3.11-distutilspython3.11-venv\python3.12python3.12-devpython3.12-distutilspython3.12-venv# install pipenv for creation of virtual environmentspython3.8-mpipinstallpipenv--user# check out repogitclonehttps://github.com/jasminsternkopf/english_text_normalization.gitcdenglish_text_normalization# create virtual environmentpython3.8-mpipenvinstall--devRunning the tests# first install the tool like in "Development setup"# then, navigate into the directory of the repo (if not already done)cdenglish_text_normalization# activate environmentpython3.8-mpipenvshell# run teststoxFinal lines of test result output:py38: commands succeeded
  py39: commands succeeded
  py310: commands succeeded
  py311: commands succeeded
  py312: commands succeeded
  congratulations :)LicenseMIT LicenseAcknowledgmentsFunded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 416228727 – CRC 1410CitationIf you want to cite this repo, you can use this BibTeX-entry generated by GitHub (seeAbout => Cite this repository).Sternkopf, J. and Taubert, S. (2024). english-text-normalization (Version 0.0.3) [Computer software]. https://doi.org/10.5281/zenodo.10555229
Package: english-words
english-words-pyReturns sets of English words created by combining different words
lists together. Example usage: to get a set of English words from the
"web2" word list, including only lower-case letters, you write the
following:>>>fromenglish_wordsimportget_english_words_set>>>web2lowerset=get_english_words_set(['web2'],lower=True)UsageFrom the main package, importget_english_words_setas demonstrated
above. This function takes a number of arguments; the first is a list of
word list identifiers for the word lists to combine and the rest are
flags. These arguments are described here (in the following order):sourcesis an iterable containing strings
corresponding to word list identifiers (see "Word lists" subsection
below)alpha(defaultFalse) is a flag specifying that all
non-alphanumeric characters (e.g.:-,') should be strippedlower(defaultFalse) is a flag specifying that all upper-case
letters should be converted to lower-caseEach word list is pre-processed to handle the above flags, so using any
combination of options will not cause the function to run slower.Note that some care needs to be used when combining word lists. For
example, only proper nouns in theweb2word list are capitalized, but
most words in thegcideword list are capitalized.Word listsName/URLIdentifierNotesGCIDE 0.53 indexgcideWords found in GNU Collaborative International Dictionary of English 0.53. Most words capitalized (not exactly sure what the capitalization convention is). Contains some entries with multiple words (currently you must use the alpha option to exclude these).Unicode characters are currently unprocessed; for example<ae/is present in the dictionary instead ofæ. Ideally, these should all be converted.web2 revision 326913web2Adding additional word listsTo add a word list, say with identifierx, put the word list (one word
per line), into a plain text filex.txtin theraw_datadirectory at the root of the repository. Then, to process the word list
(and all others in the directory) run the scriptprocess_raw_data.py.InstallationInstall this with pip withpip install english-wordsThis package is unfortunately rather large (~20MB), and will run into
scaling issues if more word lists or (especially) options are added.
When that bridge is crossed, word lists should possibly be chosen by the
user instead of simply including all of them; word lists could also be
preprocessed on the client side instead of being included in the
package.
Package: englisttohindi
[]englisttohindiwhat is englisttohindi ?It converts your English String into Hindi Stringapplication can be to convert dataset into hindi and train NLP ModelsThis Module is based on web scrappingDependenciespipinstallrequestsInstallationpipinstallenglisttohindiUsagefromenglisttohindi.englisttohindiimportEngtoHinditrans=EngtoHindi(message="Hello  Everyone  ")print(trans.convert)AuthorsSoumil Nitin ShahBachelor in Electronic Engineering |
Masters in Electrical Engineering |
Master in Computer Engineering |Website :https://soumilshah.herokuapp.comGithub:https://github.com/soumilshah1995Linkedin:https://www.linkedin.com/in/shah-soumil/Blog:https://soumilshah1995.blogspot.com/Youtube :https://www.youtube.com/channel/UC_eOodxvwS_H7x2uLQa-svw?view_as=subscriberFacebook Page :https://www.facebook.com/soumilshah1995/Email :shahsoumil519@gmail.comLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details
Package: engly
engly概要英語学習支援アプリケーションGUI単語リストやAPIキーなどの各種データは~/.engly_dataに存在しているインストールpip install -U engly使用法Listリストに登録されている単語の表示、単語をリストに登録Testリストに登録されている単語を出題、意味を4択から回答ReadingChatGPTを使って以下の情報を提供する英文に含まれる単語、熟語とその意味の表示英文の翻訳と主語動詞の表示HistoryTestで正解した回数のグラフを表示apikeyapikeyの入力依存関係openai ~= 0.28wxPython ~= 4.2.1plotly ~= 5.16.1ライセンスMITライセンス
Package: eng-m
ENG M 401"Wow this class sucks."Thankfully we have something to do the work for us.eng_mis a simple package which aims to simplify or trivialize the majority of questions from the University of Alberta's ENG M 401 class.Installationpip install git+https://github.com/Mcnuget5/ENG-M-401Documentationnian.ca/docs/eng_m(theres nothing there yet)LicensemitContributingreadcontributing.md
Package: engrave
EngraveVer. 2.1.1Engrave is a tool to generate static website.FeaturesWrite contents in HTML and Markdown.HTML template with Jinja2Changes from Ver. 1.x.xNowengraveonly handle html files. Due to the release ofparcel v2.0which cover many features for other files.Installation$pipinstallengraveUsage$ engrave -h
usage: engrave [-h] {build,dev} ...

Static website generator

positional arguments:
  {build,dev}
    build            Build html
    dev              Build html and watch for changesExampleThis document is made byEngraveand it's a good example how engrave works.
See document's source code atTo build this document and run development server, use command below$gitclonehttps://github.com/nitipit/engrave.git
$cdengrave
$npminstall# Install Node Libraries$engravedevdocs-srcdocs--serverCopyright 2023 Nitipit Nontasuwan
(MIT License)Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"),
to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell copies of the Software, and to permit persons
to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
DAMAGES OR OTHER LIABILITY, 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 IN THE SOFTWARE.
Package: engression
EngressionEngression is a nonlinear regression methodology proposed in the paper "Engression: Extrapolation for Nonlinear Regression?" by Xinwei Shen and Nicolai Meinshausen. This directory contains the Python implementation of engression.Consider targets $Y\in\mathbb{R}^k$ and predictors $X\in\mathbb{R}^d$; both variables can be univariate or multivariate. Engression can be used toestimate the conditional mean $\mathbb{E}[Y|X=x]$ (as in least-squares regression),estimate the conditional quantiles of $Y$ given $X=x$ (as in quantile regression), andsample from the fitted conditional distribution of $Y$ given $X=x$ (as a generative model).The results in the paper show the advantages of engression over existing regression approaches in terms of extrapolation.InstallationThe latest release of the Python package can be installed through pip:pipinstallengressionThe development version can be installed from github:pipinstall-e"git+https://github.com/xwshen51/engression#egg=engression&subdirectory=engression-python"Usage ExamplePythonBelow is one simple demonstration. Seethis tutorialfor more details on simulated data andthis tutorialfor a real data example. We demonstrate inanother tutorialhow to fit a bagged engression model, which also helps with hyperparameter tuning.fromengressionimportengressionfromengression.data.simulatorimportpreanm_simulator## Simulate datax,y=preanm_simulator("square",n=10000,x_lower=0,x_upper=2,noise_std=1,train=True,device=device)x_eval,y_eval_med,y_eval_mean=preanm_simulator("square",n=1000,x_lower=0,x_upper=4,noise_std=1,train=False,device=device)## Fit an engression modelengressor=engression(x,y,lr=0.01,num_epoches=500,batch_size=1000,device="cuda")## Summarize model informationengressor.summary()## Evaluationprint("L2 loss:",engressor.eval_loss(x_eval,y_eval_mean,loss_type="l2"))print("correlation between predicted and true means:",engressor.eval_loss(x_eval,y_eval_mean,loss_type="cor"))## Predictionsy_pred_mean=engressor.predict(x_eval,target="mean")## for the conditional meany_pred_med=engressor.predict(x_eval,target="median")## for the conditional mediany_pred_quant=engressor.predict(x_eval,target=[0.025,0.5,0.975])## for the conditional 2.5% and 97.5% quantilesContact informationIf you meet any problems with the code, please submit an issue or contactXinwei Shen.
Package: eng-spacysentiment
sentiment analysis using spacy pipelinesFeature | Description |— | — |Name|eng_spacysentiment|Version|2.3.0|spaCy|>=3.5.3,<3.6.0|Default Pipeline|textcat|Components|textcat|Vectors| 0 keys, 0 unique vectors (0 dimensions) |Sources| n/a |License|MIT|Author| [Vishnu](https://github.com/Vishnunkumar/eng_spacysentiment) |### Label Scheme<details><summary>View label scheme (3 labels for 1 components)</summary>Component | Labels |— | — |`textcat`|positive,negative,neutral|</details>
Package: eng-syl
ï»¿# English Syllabifier (eng_syl)
This is a GRU-based neural network designed for English word syllabification. The model was trained on data from theWikimorphdataset.UsageUse thesyllabify()function from theSyllabelclass to syllabify your words:>>> from eng_syl.syllabify import Syllabel
>>> syllabler = Syllabel()
>>> syllabler.syllabify("chomsky")
'chom-sky'syllabify()parameterstext:string- English text to be syllabified. Input should only contain alphabetic characters.syllabify()returns the given word with hyphens inserted at syllable boundaries.Onceler (Onset, Nucleus, Coda Segmenter)Theonc_split()function from theOncelerclass splits single syllables into their constituent Onset, Nucleus, and Coda components.>>> from eng_syl.onceler import Onceler
>>> lorax = Onceler()
>>> print(lorax.onc_split("sloan")
'sl-oa-n'text:string- English single syllable word/ component to be segmented into Onset, Nucleus, Coda. Input should only contain alphabetic characters.Phonify (Grapheme sequence to IPA estimation)Theipafy()function from theon_to_phonclass tries to approximate an IPA pronunciation from a sequence of graphemes.>>> from eng_syl.phonify import onc_to_phon
>>> skibidi = onc_to_phon()
>>> print(skibidi.ipafy(['b', 'u', 'tt'])
'bÊŒt'sequence:array of strings- sa sequence of English viable onsets, nuclei, and coda
Package: eng-to-ipa
English to IPA (eng_to_ipa)This Python program utilizes the Carnegie-Mellon University Pronouncing Dictionary to convert English text into theInternational Phonetic Alphabet.Theconvertfunction is used to take English text and convert it to IPA, like so:>>>importeng_to_ipaasipa>>>ipa.convert("The quick brown fox jumped over the lazy dog.")'ðə kwɪk braʊn fɑks ʤəmpt ˈoʊvər ðə ˈleɪzi dɔg.'Note that words that cannot be found in the CMU dictionary are simply reprinted with an asterisk.convertparameterstext:string- The input string of English text to be converted to IPA notation.keep_punct:boolean, optional (default=True)- Determines whether or not the punctuation marks from the input string
should be retained or not.retrieve_all:boolean, optional (default=False)- Given that some words might have more than one transcription,
this parameter determines whether or not a list of all possible combinations of transcriptions should be returned (True)
or just the string of one transcription (False).stress_marks:string, optional (default='both')- Determines whether or not the primary and secondary stress
markings (ˈ, ˌ) should be retained. Understood arguments are:"primary" - retains primary stress only"secondary" - retains secondary stress only"both" - to keep both primary and secondary stress markers.mode:string, optional (default='sql')- Accepts "sql" or "json", depending on which version of the database you'd like to use.
As another option for JSON users, simply use the functionjonvertinstead ofconvert.Theipa_listfunction returns a list of each word as a list of all its possible transcriptions. It has all the same
optionalstress_marksandkeep_punctparameters asconvert.>>>ipa.ipa_list("The record was expensive.")[['ði','ðə'],['rəˈkɔrd','rɪˈkɔrd','ˈrɛkərd'],['wɑz'],['ɪkˈspɛnsɪv.']]Theisin_cmufunction takes a word (or list of words) and checks if it is in the CMU pronouncing dictionary (returnsTrueorFalse). If a list of words is provided, thenTruewill only be returned ifeveryprovided word is in the dictionary.>>>ipa.isin_cmu("The dentist opened a new practice.")True>>>ipa.isin_cmu("emoji")FalseTheget_rhymesfunction returns a list of rhymes for a word or set of words.>>>ipa.get_rhymes("rhyming function")[['climbing','diming','liming','priming','timing'],['compunction','conjunction','dysfunction','injunction','junction','malfunction']]Use thejhymesfunction instead to force usage of the JSON database.Thesyllable_countfunction returns an integer, corresponding to the number of syllables in a word. Returns a list of
syllable counts if more than one word is provided in the input string.>>>ipa.syllable_count("computer programming")[3,3]For another Python package that offers support for rhyming and syllable counts (as well as other cool things), seepronouncingpy.Note: I'd like to make this project easily available/installable from pip, but I don't know how. Looking for help!Releaseversion: 0.0.2How to releasepipinstallsetuptoolswheeltwine
pythonsetup.pysdist
python-mtwineupload--repositorytestpypidist/*
python-mtwineupload--repositorypypidist/*
rm-rfdist*.egg-infoMANIFEST