Saravanan666/pypi-org huggingface dataset:
Package: corona-chan-gob-mx
corona_chan_gob_mxcorona chan scraper for gob mxFree software: WTFPLDocumentation:https://corona-chan-gob-mx.readthedocs.io.Featuresle los pdf publicados encasos de covid-19 en mexicotransforma las tablas de los pdf en listas de dicionarios para poder
ser procesadas en python de manera mas facilHow to useel uso basico seria conimportcorona_chan_gob_mximportget_today_cases()table=get_today_cases()forrowintable:assertisinstance(row,dict)se puede adquirir la lista de pdfs conimportcorona_chan_gob_mx.scraperimportlist_of_pdfslinks=list_of_pdfs.get()print(links.native)# [#       'https://www.gob.mx/cms/uploads/attachment/file/544087/'#       'Tabla_casos_sospechosos_COVID-19_2020.03.29.pdf',#       'https://www.gob.mx/cms/uploads/attachment/file/544086/'#       'Tabla_casos_positivos_COVID-19_resultado_InDRE_2020.03.29.pdf' ]forlinkinlinks.native:table=link.get()forrowintable.native:assertisinstance(row,dict)para leer pdf sin descargarlosimportcorona_chan_gob_mx.scraperimportpdf_to_dictstabla=pdf_to_dict("/path/to/pdf/tabla.pdf")forrowintable:assertisinstance(row,dict)History0.0.1 (2020-03-28)First release on PyPI.
Package: corona-dashboard
Coronavirus DashboardThis project contains dashboard(s) and other visualizations to monitor the outbreak.Currently includes:A dashboard that detects hotspots based on growth (not total cases) and maps them.Uses third-party data:Coronavirus data from the New York Times.This is an experimental project. The numbers may be wrong. It should not be relied
on for any decision making without additional review.Installationpip install corona-dashboardUsagecorona-dashboardLicense and AcknowledgmentApache 2. See LICENSE file for details.A project ofB.Next.
Package: corona-info
Corona InfoA quick and easy way to get the latest coronavirus news without having to digInstallationRun the following to install:pipinstallcorona-infoUsageNote: None of these functions return, they simply printimportcorona_infoasci#Get fast numbers:ci.get_stats()#Get breakdown by country:ci.get_table()#Get a latest (n) bullets of news; default is 15:ci.get_news(5)#Get advice on how to stay safe:ci.get_advice()
Package: corona-moova
django-moovaStartingThese instructions will allow you to install the library in your python project.Current featuresCreate shipment.Get tracking info.Get shipping label.Pre-requisitosPython >= 3.7Django >= 3requests >= 2InstallationTo get the latest stable release from PyPi:pip install corona-moovaorFrom a buildgit clone git@gitlab.com:linets-projects/python-libraries/django-moova.gitcd {{project}}python setup.py sdistand, install in your project djangopip install {{path}}/django-moova/dist/{{tar.gz file}}Settings in django projectDJANGO_MOOVA = {
    'MOOVA': {
        'BASE_URL': '<MOOVA_BASE_URL>',
        'SECRET': '<MOOVA_SECRET_TOKEN>',
        'KEY': '<MOOVA_SECRET_KEY>',
        'CURRENCY': 'CLP',
        'TYPE': 'regular',
        'FLOW': 'manual',
    },
    'REMITENTE': {
        'ADDRESS': '<ADDRESS>',
        'COUNTRY': 'CHL',
        'INSTRUCTIONS': 'Call before delivery',
        'FIRST_NAME': '<FIRST_NAME>',
        'LAST_NAME': '<LAST_NAME>',
        'EMAIL': '<EMAIL>',
        'PHONE': '<PHONE>',
    },
}UsageCreate shipment:from moova.handler import MoovaHandler

handler = MoovaHandler()

default_data = handler.get_default_payload(instance)
default_data["currency"] = "CLP" # eg. (Optional)
response = handler.create_shipping(default_data)Get tracking info:from moova.handler import MoovaHandler

handler = MoovaHandler()

tracking_info = handler.get_tracking(<identifier>)Get shipping label:from moova.handler import MoovaHandler

handler = MoovaHandler()

label_info = handler.get_shipping_label(<shipping_id>)

Output:
'https://moova-user-data-test.s3.amazonaws.com/etiqueta-10x15.pdf'
Package: corona.py
corona.pyAn asynchronous wrapper for theNovelCOVID APIwritten in Python.This is designed for bots usingdiscord.pywith optional discord.py plugin.RequirementsPython 3.6 or aboveaiohttp (python3 -m pip install -U aiohttp)InstallationUsing pip (recommended)python3 -m pip install -U corona_apiSupportGet support for this over onDiscordand ping NANI#0001.
Package: corona-python
corona_pythonA Python API Wrapper for coronavirus statsInstallationTo install corona-python, do:pip install corona-pythonorpython -m pip install corona-pythonUsagefromcorona_pythonimportCountrycountry=Country("USA")print(country.total_cases())More examples are provided in theexamples folderDocumentationDocumentation can be foundhereFunctionsCountry:last_updated()
flag()
total_cases()
today_cases()
total_deaths()
today_deaths()
recovered()
today_recovered()
active()
critical()
cases_per_one_million()
deaths_per_one_million()
total_tests()
tests_per_one_million()
population()
continent()
one_case_per_people()
one_death_per_people()
one_test_per_people()
get_all()World:last_updated()
total_cases()
today_cases()
total_deaths()
today_deaths()
recovered()
today_recovered()
active_cases()
critical_cases()
cases_per_one_million()
deaths_per_one_million()
total_tests()
population()
affected_countries()
tests_per_one_million()
one_case_per_people()
one_death_per_people()
one_test_per_people()
active_per_million()
recovered_per_million()
critical_per_million()
get_all()More detailed explanations of the functions can be found inthe docsErrorsThe module will raiseKeyErrorif the given country is invalidIf the module doesn't return anything, there might be a problem with the API. To report the problem, contact me, the developer or create an issue.
Package: coronatracker
coronatrackerThis is a simple tracker for COVID19 which uses the data forhttps://bing.com/covidand wraps it into usable data for Python programs.>>>importcoronatracker>>>ct=coronatracker.CoronaTracker()>>>ct.fetch_results()>>>ct.get_country('United Kingdom')Country(total_stats=TotalStats(confirmed=1551,deaths=53,recovered=19),id='unitedkingdom',last_updated=datetime.datetime(2020,3,16,23,0,12,91000),areas=[],name='United Kingdom',lat=53.943838,long=-2.550564)RequirementsRequestsaiohttpInstall$python-mpipinstall-Ucoronatracker
Package: corona-tracker-india
Corona Tracker IndiaStay Home ,
Be Safe !MotiveAs a developer, I also want to do something during this huge pandemic situation, so I develop a python script to automatically trace out the required data for faster analysis and work out with it. This is a small thanks from my side to all those who are working out onto the field and fighting with Covid19.FeaturesCross-platform python scriptStatewise informationLog the data into a txt filePrerequisitespython3 should be installed onto the system.pip3 should be installed onto the system.pip3 and python3 should be inside the environment variables (only for windows users)Installationpip3 install corona-tracker-indiaorpip install corona-tracker-indiaUsageCreate a new python file and write these lines into it.Create this file in the directory where you want to store the data.Importing the package :from corona_tracker_india.tracker import track_corona_indiaUsing the functionality :track_corona_india()Now simply execute the script with this command:python3 name_of_the_file.pyIf there is no error then everthing is perfect.Check for the corona-data folder in the same repository.Inside this folder the today's latest data will be fetched automatically as soon as you run the script.Feedback and ComplaintsGithubTwitterWordpress
Package: coronaweeklyJP
corona-weekly-JPThis package displays the rate of increase in corona cases in Japanese prefectures.Example of use$ pip install coronaweeklyJP
$ coronaweeklyJP HokkaidoUsage: coronaweeklyJP PrefecturePositional arguments:Prefecture: Japanese prefecture name in English(e.g. Hokkaido, Tokyo, Osaka, Okinawa etc.)Output
Package: coroner
Wraps python invocations in code to do post-mortem debugging
and sundry usefulnesses.coronerwas adapted from a stackoverflow.comresponsebysynthesizerpatel.Usagecoroneris meant to be used as a command line module,
invoked with code like:python -m coroner some_sketchy_module.pyWhen the sketchy script invariably throws an exception,coronerwill initiate apdb.pmpost-mortem debugging session.You can also use it in the other ways thatpythoncan be used
on the command line, e.g. with-cor-.BugsThat’s all, folks.  I might add more sometime soon.Oh yeah, it doesn’t do recursive module invocation yet.Also I haven’t really tested it, just tried it with-ca couple of times.
Package: coroutines
This is a small package which provides tools to runasyncfunctions and
generators without anasyncioevent loop.Thecoroutinesmodule provides functions such ascoroutines.run(),coroutines.gather(), andcoroutines.sleep()that work just like theirasynciocounterparts, but without scheduling any tasks in an external event
loop. For example, the following code was adaptedfrom the asyncio
documentation:importcoroutinesasyncdeffactorial(name,number):f=1foriinrange(2,number+1):print(f"Task{name}: Compute factorial({number}), currently i={i}...")awaitcoroutines.sleep()# CHANGED: no argumentf*=iprint(f"Task{name}: factorial({number}) ={f}")returnfasyncdefmain():# Schedule three calls *concurrently*:L=awaitcoroutines.gather(factorial("A",2),factorial("B",3),factorial("C",4),)print(L)coroutines.run(main())# Expected output:##     Task A: Compute factorial(2), currently i=2...#     Task B: Compute factorial(3), currently i=2...#     Task C: Compute factorial(4), currently i=2...#     Task A: factorial(2) = 2#     Task B: Compute factorial(3), currently i=3...#     Task C: Compute factorial(4), currently i=3...#     Task B: factorial(3) = 6#     Task C: Compute factorial(4), currently i=4...#     Task C: factorial(4) = 24#     [2, 6, 24]The example produces the same result as theasynciocode by simply calling,
suspending, and resuming the coroutines until they are completed. Practically,
the only difference between thecoroutinesandasyncioexamples is thatcoroutines.sleep()does not take an argument. Because there is no external
event loop, a call tocoroutines.sleep()cannot suspend the current chain of
coroutines for a fixed amount of time, but only until it is resumed at the next
iteration.Running coroutinescoroutines.run(coro)#Run a coroutine from synchronous code.Suspending coroutinesawaitablecoroutines.sleep()#Suspend the current chain of coroutines, allowing another coroutine to run
concurrently.Concurrent executionawaitablecoroutines.gather(*aws)#Run the given awaitablesawsconcurrently.Returns a coroutine that loops overaws, resuming each awaitable in
turn until it is suspended again or finished.  Execution is suspended
after each pass overaws, so that other coroutines can run while the
result ofgather()is being awaited.The result of awaitinggather()is the aggregate list of awaited results
fromawsin the same order.Creating awaitablesThecoroutinesmodule contains a number of helper functions that turn
regular objects into awaitable variants of themselves.awaitablecoroutines.awaitable(obj=None)#Create an awaitable variant ofobj.  Returns a coroutine that awaitscoroutines.sleep()before returningobj.awaitablecoroutines.aiterable(iterable)#Create an awaitable variant of an iterable.  Returns an asynchronous generator
that awaitscoroutines.sleep()before each item initerable.awaitablecoroutines.arange(stop)#awaitablecoroutines.arange(start, stop[, step])Create an awaitable variant ofrange().  Returns an asynchronous generator
that awaitscoroutines.sleep()before each number.
Package: corpcrawl
# About
This is a python scraper for the Securities and Exchange Commission EDGAR database. It looks at the Form 10k
filings that publicly held corporations are required to file with the SEC. It then attempts to extract the
subsidiary relationships from the 10k exhibit 21.1.Corpcrawl is a storage agnostic scraper, so you’ll need to implement your own storage scheme.# Installation
You can get the package from PyPi through Pip.pip install corpcrawlFrom a python console you can tryimport corpcrawlIf it works, then you can get started# How to use## Running it
First import the required piecesfrom corpcrawl.crawler import CorpCrawl
from corpcrawl.backend import Backenddef main()my_backend = MyBackend()
crawler = CorpCrawl(cache_path = ‘/an/absolute/path/to/some/dir’, backend = my_backend)
c.crawl(years = [2011, 2012], quarters = [1, 2, 3, 4])class MyBackend(Backend):def get_company(self, name):passdef add_company(self, comp):print “Adding %s” % str(comp)This is about as simple of a backend as can be. This code will crawl the filings and print out the name of each
company and subsidiary that it finds for all quarters of years 2011 and 2012.
Obviously you will want to hook it up to a database or something.# Notes
The SEC data is very unstructured. As such, there are a lot of errors. This is version 0.0.1, so instead of
implementing your own methods to massage the data that it returns, it would be better if you could contribute
so make the core parser better.
Package: corpona
corponacorpona is a library for processing corpora formats (e.g. XML and JSON).
The library is installable via PIP:pip install -U corpona.ExamplesReading NewsML XML formatfromcorponaimportXMLd=XML.parse_xml('2660341.xml')print(f"Guid:{d.guid}")# access tag attributes as Python attributesprint(f"Language:{d.attributes['xml:lang']}")# in case of special characters, access them directlycontentMeta=d['contentMeta'][0]print(f"Urgency:{contentMeta['urgency']}")print(f"Headline:{contentMeta['headline']}")print(f"Subject:{contentMeta['subject'][0]['name']}")print("Genres:{}".format(", ".join(g['name'].textforgincontentMeta['genre'])))print()content_body=d['contentSet'][0]['inlineXML'][0]['html'][0]['body'][0]print("Content: ")forpincontent_body['p']:print(p)Getting a Summary of an XML/JSONfromcorponaimportXMLfromcorponaimportsummarizefrompprintimportpprintd=XML.parse_xml('data.xml',namespaces={'http://www.w3.org/XML/1998/namespace':'xml',})pprint(summarize(d),indent=4)pprint(summarize([{'key1':'hello1','key2':1},{'key1':'hello2','key2':2},{'key1':'hello3','key2':3},{'key1':'hello4','key2':4},]),indent=4)Find childrenfromcorponaimportfind_childdata={"key":["list_item",{"key2":"oo"},{"key2":"bbb"}]}print(find_child(data,["key","key2"]))print(find_child(data,["key","key3"],default_value="ok"))>>['oo','bbb']>>['ok']CiteIf you use the library in an academic paper, please cite it:Alnajjar, K. & Hämäläinen, M., (2021)Corpona – The Pythonic Way of Processing Corpora. InHämäläinen, M., Partanen, N. & Alnajjar, K. (eds.) Multilingual Facilitation. University of Helsinki, p. 25−30@inbook{3bd164164c8648b986cb14a4a8524423,
title = "Corpona – The Pythonic Way of Processing Corpora",
author = "Khalid Alnajjar and Mika H{\"a}m{\"a}l{\"a}inen",
year = "2021",
language = "English",
pages = "25−30",
editor = "Mika H{\"a}m{\"a}l{\"a}inen and Niko Partanen and Khalid Alnajjar",
booktitle = "Multilingual Facilitation",
publisher = "University of Helsinki",
address = "Finland",
}Need for NLP solutions for your business?Our company,Rootroo offers consulting related to multilingual NLP tasks. We have a strong academic background in the state-of-the-art AI solutions for every NLP need. Just contact us, we won't bite.
Package: corporate-reputation
Reputation Analysis of Companies and CEOsUnraveling Corporate and CEO Reputation using Aspect-Based Sentiment Analysis and Signal ModelingDocumentation:https://corporate-reputation.entelecheia.aiGitHub:https://github.com/entelecheia/corporate-reputationPyPI:https://pypi.org/project/corporate-reputationThe reputation of a company and its CEO forms an intangible asset that significantly influences organizational success. With digital media becoming the central stage for public opinion formation, understanding and quantifying reputation has become more complex yet critically important. This study proposes an innovative approach to disentangle and measure the reputation of a company and its CEO using Aspect-Based Sentiment Analysis (ABSA) coupled with a signal model to trace the evolution of sentiment over time.The research first leverages ABSA, through a generative language modeling approach, to separately analyze sentiments associated with a company and its CEO. ABSA's granular nature allows us to differentiate aspects associated with the CEO from those linked to the company, thereby creating distinct reputation metrics.Next, a signal model, built upon principles from the Stochastic Process Theory and probability density functions, is used to analyze sentiment evolution over time, providing insights into the dynamic nature of reputation. This model further quantifies the mutual information between the sentiment scores of the CEO and the company, assessing their interdependence and the influence they exert on each other.While recognizing the challenges associated with employing ABSA and the proposed signal model, this research highlights the potential of these tools to inform strategic decision-making. Whether it's for companies striving to manage their reputation, investors evaluating corporate reputation, regulators ensuring compliance, or media organizations reporting on trends, the ability to separate and track the reputations of a company and its CEO could offer profound insights.The study, therefore, presents a new frontier in reputation analysis and management, enabling a granular, dynamic, and nuanced understanding of corporate and CEO reputations in the digital age.ChangelogSee theCHANGELOGfor more information.ContributingContributions are welcome! Please see thecontributing guidelinesfor more information.LicenseThis project is released under theMIT License.
Package: corpus2alpino
PyPi/corpus2alpinoCHAT, FoLiA, PaQu metadata, plaintext and TEI to Alpino XML or PaQu metadata formatConvertsCHAT,FoLiA,PaQu metadata, plaintext andTEIXML files toAlpinoXML files. Each sentence in the input file is parsed separately.UsageCommand Linepipinstallcorpus2alpino
corpus2alpino-slocalhost:7001folia.xml-oalpino.xmlOr from project root:python-mcorpus2alpino-slocalhost:7001folia.xml-oalpino.xmlLibraryfromcorpus2alpino.converterimportConverterfromcorpus2alpino.annotators.alpinoimportAlpinoAnnotatorfromcorpus2alpino.collectors.filesystemimportFilesystemCollectorfromcorpus2alpino.targets.memoryimportMemoryTargetfromcorpus2alpino.writers.lassyimportLassyWriteralpino=AlpinoAnnotator("localhost",7001)converter=Converter(FilesystemCollector(["folia.xml"]),# Not needed when using the PaQuWriterannotators=[alpino],# This can also be ConsoleTarget, FilesystemTargettarget=MemoryTarget(),# Set to merge treebanks, also possible to use PaQuWriterwriter=LassyWriter(True))# get the Alpino XML output, combined into one treebank XML fileparses=converter.convert()print(''.join(parses))# <treebank><alpino_ds ... /></treebank>EnrichmentIt is possible to add custom properties to (existing) Lassy/Alpino files. This is done using a csv-file containing the node attributes and values to look for and the custom properties to assign.For example:python-mcorpus2alpinotests/example_lassy.xml-etests/enrichment.csv-oflassySeecorpus2alpino.annotators.enrich_lassyfor more information.DevelopmentUnit Testpython-munittestUpload to PyPiSee:https://packaging.python.org/tutorials/packaging-projects/#generating-distribution-archivesMake suresetuptoolsandwheelare installed. Then from the virtualenv:pythonsetup.pysdistbdist_wheel
twineuploaddist/*RequirementsAlpino parserrunning as a server:Alpino batch_command=alpino_server -notk server_port=7001Python 3.7 or higherlibfolia-devlibxml2-devInstallation Instructions for Ubuntusudoaptinstalllibfolia-devlibxml2-dev
pipinstall-rrequirements.txt
Package: corpus-base
corpus-baseAdd justice, decision, citation, voting, opinion, and segment tables to database; utilized in theLawSQL dataset.DocumentationSeedocumentation, building on top ofcorpus-paxDevelopmentCheckout code, create a new virtual environment:poetryaddcorpus-base# python -m pip install corpus-basepoetryupdate# install dependenciespoetryshellRun tests:pytest
Package: corpus-builder
Corpus ConstructorTool for semi-auromatic corpus construction for training NLP models.
This project allows for a definition of a domain corpus by using a
context-free grammar in the BNF form. This grammar is used to produce
sample sentences belonging to this defined language. That are later
expanded by textual data augmentation to form a specific domain
corpus that could be used to train machine learning models such as
intent classifier and NER.InstallationCurenttly the only instalation method is by clonning this repo and
installing dependencies manually.UsageA simple example with a syntetic language is as follows:fromcorpus_builder.ruleimportRulefromcorpus_builder.grammarimportGrammarfromcorpus_builder.builderimportCorpusBuilderroot_rule=Rule('<S>',('<A>','<B>'))a_rule=Rule('<A>',['a','<B>'])b_rule=Rule('<B>',('<A>','b','<C>'))c_rule=Rule('<C>','c')rule_set=[root_rule,a_rule,b_rule,c_rule]grammar=Grammar(rule_set,'<S>')builder=CorpusBuilder(grammar,{'<S>':['A','B']},{'<C>':'C'})print(builder.create_sentence())print(builder.create_corpus(5,0))A more complex example with natural language in the AskUbuntu corpus
domain is in theask_ubuntu.pyfile.It is also possible to create a grammar from a text file inthisformat. Then create the corpus builder as use it as followsfromcorpus_builder.builder_importerimportfrom_text_filebuilder=from_text_file('simple_domain.txt')print(builder.create_sentence())print(builder.create_corpus(5,0))Or by using the command line utilitypython-mcorpus_builder--inputsimple_domain.txt-n5VisualizationYou can visualize thee grammar you created and produceimages like thisby doingfromcorpus_builder.visualizeimportplot_grammarplot_grammar(grammar)
Package: corpus-clavicle
corpus-clavicleThis is a python package used to provide models and auxilaries for the corpus package for dealing with total proteomics dataInstallationpipinstalldjango-postgres-extracorpus-claviclepsycopg2UsageimportosINSTALLED_APPS=[#...'django.contrib.postgres',# Required for 'psqlextra'psqlextra','clavicle',#...]DATABASE_ROUTERS=['clavicle.dbrouter.ClavicleRouter']DATABASES={'default':{'ENGINE':'django.db.backends.sqlite3','NAME':'db.sqlite3',},'clavicle':{'ENGINE':'psqlextra.backend','NAME':os.environ.get('POSTGRES_NAME','clavicle'),'USER':os.environ.get('POSTGRES_USER','admin'),'PASSWORD':os.environ.get('POSTGRES_PASSWORD',"testpostgrest"),'HOST':os.environ.get('POSTGRES_HOST','localhost'),'PORT':int(os.environ.get('POSTGRES_PORT','5432')),}}pythonmanage.pymigrate--database=clavicle
Package: corpuscula
RuMor: Russian Morphology projectCorpuscula: a python NLP library for corpus processingA part ofRuMorproject. It contains tools to simplify corpus
processing. Highlights are:fullCoNLL-Usupport
(includesCoNLL-U Plus)wrappers for known corpora of Russian languageparser and wrapper for Russian part ofWikipediaCorpus Dictionarythat can be used for further morphology processingsimple database to keep named entitiesInstallationpipCorpusculasupportsPython 3.5or later. To install it viapip, run:$pipinstallcorpusculaIf you currently have a previous version ofCorpusculainstalled, use:$pipinstallcorpuscula-UFrom SourceAlternatively, you can also installCorpusculafrom source of thisgit
repository:$gitclonehttps://github.com/fostroll/corpuscula.git
$cdcorpuscula
$pipinstall-e.This gives you access to examples and data that are not included to thePyPIpackage.SetupAfter installation, you need to specify a directory where you prefer to store
downloaded corpora:>>>importcorpuscula.corpus_utilsascu>>>cu.set_root_dir(<path>)# We will keep corpora hereNB:it will create/update config file.rumorin your home directory.If you won't set the root directory,Corpusculawill keep corpora
in the directory where it's installed.UsageCoNLL-USupportManagement of CorporaWrapper forWikipediaCorpus DictionaryUtilitiesItemsdatabaseExamplesYou can find examples in the directoryexamplesof ourCorpusculagithub
repository.LicenseCorpusculais released under the BSD License. See theLICENSEfile for
more details.