Saravanan666/pypi-org huggingface dataset:
Package: covid19-detection
Explainable Detection of COVID-19 from Chest X-Ray ImagesThis project is part of the course DD2424 Deep Learning in Data Science at KTH. The goal is to train a classifier for COVID-19 detection from chest X-ray (CXR) images and boost it with explainability. More information can be found in thereport. Also check out ourpresentation.This package provides:Anapplicationwith a graphical user interface (GUI). This application can be used to make predictions on your images using trained models.Asuite of toolswith a command-line interface (CLI). These tools can be used to train and test new models.Several modules with a Keras-like API. These modules can be used in Python code.1. SetupThe recommended installation is the following:wget https://raw.githubusercontent.com/franco-ruggeri/dd2424-covid19-detection/master/scripts/install.sh -O install.sh
bash -i install.shFollowing the prompt, you can get a ready-to-use installation that uses thebest models we trained.The package is distributed onPyPi, so can be installed also with:pip install covid19-detectionHowever, in this case you have to provide the trained models to the application. You can decide either to download thebest models we trainedor to train your own models with thecommand-line tools.2. ApplicationIf you have done the recommended installation, you can launch the application by searching it among the applications. Otherwise, you can launch it from the terminal:covid19-detector3. Command-line suiteThe command-line suite is available under thecovid19-detectioncommand. It provides several subcommands. The list can be retrieved with:covid19-detection -hMore information about each subcommand can be obtained with:covid19-detection <subcommand> -h4. PackageYou can import the package in your Python code with:import covid19The covid19 package is composed of the following sub-packages:covid19.datasets: contains utilities for generating COVIDx, HAM10000 and for building an input pipeline with tf.data.covid19.models: contains ResNet50 and COVID-Net, two deep convolutional neural networks.covid19.explainers: contains Grad-CAM and IG, two explainable AI methods, with some utilities for plotting the explanations.covid19.layers: contains layers used by models in covid19.models.covid19.metrics: contains utilities for computing and plotting metrics.covid19.gui: contains graphical user interface implemented withQt.covid19.cli: contains command-line interface.Each subpackage provides interesting modules. For example, you can create a COVID-Net as follows:from covid19.models import COVIDNet

model = COVIDNet(n_classes=3)For more information about each class, see the comments.
Package: covid-19-dryampy
covid_19 Data PackagesAn assortment of Data Scraping packages from publicly available git repos and website scrapingsGetting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.InstallingThis package is hosted on Pypipython3 -m pip install covid-19-dryampyExample Scriptsfrom covid_19.us_tests_cdc import us_tests_cdc

scraper = us_tests_cdc()
print(scraper.fetch())or something a bit more complex;from covid_19.csse_data import csse_retrieve
from covid_19.us_tests_cdc import us_tests_cdc
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


[data, dim_loc, dim_metric] = csse_retrieve().fetch()
test_dat = us_tests_cdc().fetch()

US_ids = dim_loc[dim_loc['Country/Region'] == 'US'].index.tolist()
US_data = data[data['loc_id'].isin(US_ids)]
US_case_data = US_data[US_data['metric_id'] == 0]
US_conf_cases = US_case_data[['counts', 'date']].groupby(['date']).agg(['sum'])
US_conf_cases.columns = ['total_counts']
US_conf_cases['daily_counts'] = np.gradient(US_conf_cases['total_counts'])

US_tests = test_dat[test_dat['status'] == 'complete']
US_tests['test_add'] = US_tests['cdc'] + US_tests['other']
US_tests = US_tests[['date', 'test_add']].groupby(['date']).agg(['sum'])
US_tests.columns = ['daily_counts']
US_tests['total_counts'] = US_tests['daily_counts'].cumsum()

final = US_conf_cases.join(US_tests, lsuffix='_cases', rsuffix='_tests')

ax = plt.gca()
plt.title('US tests and confirmed cases over time')

final2 = final.reset_index()
#final2.plot(kind='line',x='date',y='total_counts_tests', ax=ax, logy=True)
#final2.plot(kind='line',x='date',y='total_counts_cases', color='red', ax=ax, logy=True)
final2.plot(kind='line',x='date',y='total_counts_tests', color='blue', ax=ax)
final2.plot(kind='line',x='date',y='total_counts_cases', color='red', ax=ax)

plt.show()SourcesUS Test Data scraped from;https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/testing-in-us.htmlWorld Confirmed/Recovered/Death data fromhttps://github.com/CSSEGISandData/COVID-19AuthorsSteven YampolskyLicenseThis project is licensed under the GNU GENERAL PUBLIC LICENSE - see theLICENSE.mdfile for details
Package: covid19forecast
Covid19  Forecasting For all Cases in future using fbprophet modelaboutPython Package to get Covid19  Forecasting For all Cases  in futureInstallationpip install covid19forecast
or in colab google cloud
!pip install covid19forecastUsage for get first cases (confirmed,recoverd,deaths for any countery)from covid19forecast import cases# country name
country='Iraq'
#path for covide19 dataset   
path_csv='covid_19_data.csv'
#name of  folder  
dir_name='Iraq'
# name of out result for all cases in new dataset 
out_csv='iraq.csv'
# what days number to forecasting for any case 
next_periods=10
# what name of dataset for all forecasting for any case
csv_name='iraq.csv'# to get all cases for country 
all=cases.all_cases(country,path_csv,dir_name,out_csv)

# to get confirmed cases for country 
confirmed=cases.confirmed_case(country,path_csv,dir_name,out_csv)
#to get deaths  cases for country 
deaths=cases.deaths_case(country,path_csv,dir_name,out_csv)

#to get recoverd cases for country 
recoverd=cases.recoverd_case(country,path_csv,dir_name,out_csv)Usage for get Forcasting for all  cases (confirmed,recoverd,deaths for any countery)from covid19forecast import forecast# to get forecasting for country confirmed case
forecast.confirmed_forecast(confirmed,next_periods,dir_name,csv_name,country)
# to get forecasting for country deaths case
forecast.deaths_forecast(deaths,next_periods,dir_name,csv_name,country)
# to get forecasting for country recoverd case
forecast.recoverd_forecast(recoverd,next_periods,dir_name,csv_name,country)CheckingTo verify the retrieved data within this library
go to web site to get download new covid19 datasethttps://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-datasetTutorialu can see tutorial in colabhttps://colab.research.google.com/drive/12TnzM0E-oZgsrwlaQ5vYCdhsym6UDNUa
Package: covid19-id
covid19-idPython module for getting data from Indonesian government (covid19.go.id)Readme Bahasa IndonesiaInstallYou can install or upgrade covid19-id with:pipinstallcovid19-id--upgradeOptional Dependenciescovid19-id can be installed with optionalujsondependency.pipinstallcovid19-id[ujson]It will then be used for JSON decoding, which can bring speed up compared to the standardjsonlibrary.ExampleGet Dataimportcovid19_iddata=covid19_id.get_data()print("View symptom data")print(f"Recorded data{100-data.kasus.gejala.missing_data:.2f}%")print(f"Unrecorded data{data.kasus.gejala.missing_data:.2f}%")forgejalaindata.kasus.gejala.list_data:print(f"{gejala.doc_count:.2f}% with symptoms{gejala.key.capitalize()}")Get Updatesimportcovid19_idall_update=covid19_id.get_update()total=all_update.update.totalprint(f"covid19; positive cases in Indonesia :{total.jumlah_positif}")print(f"covid19; patients treated in Indonesia{total.jumlah_dirawat}")print(f"covid19; patients recovered in Indonesia{total.jumlah_sembuh}")print(f"covid19; patients died in Indonesia{total.jumlah_meninggal}")Provinsiimportcovid19_iddata_provinsi=covid19_id.get_prov()forprovinsiindata_provinsi.list_data:print(f"Province :{provinsi.key}")print(f"Cases{provinsi.jumlah_kasus}")print(f"Recovered{provinsi.jumlah_sembuh}")print(f"Died{provinsi.jumlah_meninggal}")forumurinprovinsi.kelompok_umur:print(f"Age{umur.key}:{umur.doc_count}")penambahan=provinsi.penambahanprint(f"Additional Positive Cases{penambahan.positif}")print(f"Additional Recovered{penambahan.sembuh}")print(f"Additional Died{penambahan.meninggal}")print("")Vaccinatedimportcovid19_idpemeriksaan_vaksinasi=covid19_id.get_pemeriksaan_vaksinasi()vaksinasi_total=pemeriksaan_vaksinasi.vaksinasi.totalprint(f"vaccinated population (first one){vaksinasi_total.jumlah_vaksinasi_1}")print(f"vaccinated population (second time){vaksinasi_total.jumlah_vaksinasi_2}")
Package: covid19-il
covid19-ilpython package which brings a "Facade" interface for using official covid19 israeli data gov's data.DependenciespandasnumpyrequestsHow to UseRequirements: Python must already be installed.Install requirements via CMD/Terminal:pip install -r requirements.txtInstall covid19-il package via CMD/Terminal:pip install covid19-ilExampleSimple example of using the package's API:from covid19_il.api_handler.api_factory.api_enum import ApiEnum
from covid19_il.api_handler.api_factory.api_factory import ApiFactory
from covid19_il.data_handler.data_handlers_factory.data_handler_factory import DataHandlerFactory
from covid19_il.data_handler.enums.resource_id import ResourceId


api_client = ApiFactory.create_api_client(ApiEnum.api_data_il)
if api_client:
    data = api_client.get_data_by_resource_id(enum_resource_id=ResourceId.CITIES_POPULATION_RESOURCE_ID,
                                              limit=1000,
                                              include_total=True)
    cities_data_handler = DataHandlerFactory.get_instance(
        ResourceId.CITIES_POPULATION_RESOURCE_ID,
        data)
    # Printing results from a generator function
    for city in cities_data_handler.top_cases_in_cities():
        print(city)Output:('Cumulative_verified_cases', defaultdict(<class 'int'>, {'אבו סנאן': 587, 'אבו גוש': 223, "אבו ג'ווייעד (שבט)": 14}))
('Cumulated_recovered', defaultdict(<class 'int'>, {'אבו סנאן': 564, 'אבו גוש': 215, "אבו ג'ווייעד (שבט)": 14}))
('Cumulated_deaths', defaultdict(<class 'int'>, {'אבו סנאן': 14, "אבו ג'ווייעד (שבט)": 0, 'אבו גוש': 0}))
('Cumulated_number_of_tests', defaultdict(<class 'int'>, {'אבו סנאן': 7608, 'אבו גוש': 5139, "אבו ג'ווייעד (שבט)": 290}))
('Cumulated_number_of_diagnostic_tests', defaultdict(<class 'int'>, {'אבו סנאן': 7130, 'אבו גוש': 4965, "אבו ג'ווייעד (שבט)": 288}))# TODO:Documentation of the package's API for ease of use using Sphinx.
Package: covid19-inference
Bayesian inference and forecast of COVID-19, code repositoryThis is a Bayesian python toolbox for inference and forecast of the spread of the Coronavirus. We also provide a variety of data download utilities which allow quick and easy data exploration of Coronavirus related datasets.DocumentationGetting startedExamplesContributingSource codeThe latest stable version isv0.4.0!Please take notice of ourdisclaimer.Installation:Can be installed from pypi withpip install covid19_inferenceRelated PublicationsThese research articles use this package:Inferring change points in the spread of COVID-19 reveals the effectiveness of interventionsThis research articleis available on arXiv(updated on April 13 2020) and was publishedin Science.The code used to produce the figures is available in the other repositoryhere.Mutational signatures and transmissibility of SARS-CoV-2 Gamma and Lambda variantsThis research articleis available on arXiv(updated on August 21 2021).If you are interested in our efforts and like to help us feel free to contact us.We are looking for support
Package: covid19it
Covid-19-ItalyA python Class that provide you all the latest informations about COVID-19 from "protezione civile" (https://github.com/pcm-dpc/COVID-19)Installpip install covid19itDependenciesrequests
jsonHow to useImport the Class:from covid19it import covid19

c = covid19.Covid()Here the list of class arguments:# Variazione rispetto al giorno precedente
c.nuovi_deceduti 
c.nuovi_guariti
c.nuovi_tamponi
c.nuovi_positivi

# Incremento percentuale
c.incremento_p_contagiati
c.incremento_p_deceduti
c.incremento_p_tamponi
c.incremento_p_guariti

# Rapporto tamponi-positivi
c.rapp_tamponi_positivi

# Others
c.stato
c.ricoverati_con_sintomi
c.terapia_intensiva
c.totale_ospedalizzati 
c.isolamento_domiciliare 
c.totale_positivi 
c.variazione_totale_positivi 
c.nuovi_positivi 
c.dimessi_guariti 
c.deceduti 
c.nuovi_deceduti
c.casi_da_sospetto_diagnostico 
c.casi_da_screening 
c.totale_casi
c.tamponi 
c.casi_testati 
c.noteHere the list of class functions:c.get_media_settimanale()
c.get_media_mensile()
c.getSmallReport()
c.getFullReport()getSmallReport()will provide you some minimal but important information about latest Covid19 news (from Italy)getFullReport()will provide you all the latest available information about Covid19 (from Italy)get_media_settimanale()will provide you last 7 days numbersget_media_mensile()will provide you last 30 days numbersExample codeSourcefrom covid19it import covid19
c = covid19.Covid()

print(c.getSmallReport())OutputTotale positivi: 21932
Nuovi positivi: 1411
Guariti: 206554
Deceduti: 35463
Nuovi Deceduti: 5
Tamponi: 8313445
Package: covid19-morocco
Covid19 Cases Tracking in Morocco in Real TimeaboutA small package of Python to access and track the number of cases of confirmed and deaths and  recovered people
from the Coronavirus for MoroccoInstallationpip install covid19_morocco
or in colab google cloud
!pip install covid19_moroccoUsagefrom covid19_morocco import covid19
country_name=Morocco #name of country# to get confirmed count for country in real time
confirmed_count=covid19.confirmed_people()
#to get deaths count for country in real time 
deaths_count=covid19.deaths_people()
#to get recoverd count in real time 
recoverd_count=covid19.recoverd_people()
# to get confirmed cases by regions (SoussMassa, CasaSettat, FesMeknes, MarrakechSafi,....)
confirmed_by_regions=covid19.confirmed_people_regions(regions)
#Plot cases 
covid19.plot_cases()
#Plot histogram
covid19.hist_cases()CheckingTo verify the retrieved data within this library
go to worldmeters web site for checking for any coutryhttps://www.worldometers.info/coronavirusplot caseshttps://raw.githubusercontent.com/aboullaite/Covid19-MA/master/stats/MA-times_series.csv
Package: covid19-open-api
Covid19-Open-APIExample usagefromcovid19openapiimportThaiCovid19if__name__=='__main__':thai=ThaiCovid19()today=thai.get('today')timeline=thai.get('timeline')all_field=thai.get_all()print(today)print(all_field)You can pass your source to the constructor
also you will need to somehow register api in config.pyDefault source is "covid19.th-stat.com"Get api based on your source field. e.g.todaytimelinecasescases_sumarearequests.get is used so you can pass kwargs as documented athttps://requests.readthedocs.io/en/master/user/quickstart/REFERENCES:https://covid19.th-stat.com/
Package: covid19-outbreak-simulator
COVID19 Outbreak SimulatorCOVID-19 Outbreak Simulator is a population-based simulator that simulates the spread of SARS-CoV-2 virus in a dynamic and heterogeneous population, subject to preventative measures and post-outbreak action plans such as quarantine, testing, and removal of symptomatic cases. Outputs from the simulator can be used to obtain quantitative measures of the effectiveness of these actions.Please refer to ourhomepagefor details and citeBo Peng, Rowland W Pettit, Christopher I Amos, Population simulations of COVID-19  outbreaks provide tools for risk assessment and continuity planning, JAMIA Open, 2021;, ooaa074,https://doi.org/10.1093/jamiaopen/ooaa074if you have used the simulator for your work. You can also have a look atPeng B, Zhou W, Pettit RW, et al, Reducing COVID-19 quarantine with SARS-CoV-2 testing: a simulation study BMJ Open 2021;11:e050473.https://doi.org/10.1136/bmjopen-2021-050473for an example for the application of the simulator.
Package: covid19pandas
covid19pandasThis package presents COVID-19 data fromJohns Hopkins UniversityandThe New York Timesin pandas dataframes, to make analysis and visualization easier in a Python environment.We provide this resource because it is important for anyone to be able to easily access data. Nevertheless, you should always follow the COVID-19 safety protocols established by authorities in your area rather than relying on any conclusions you may divine from these data about the state of the world. Established guidelines often include staying home as much as possible, avoiding contact with people outside your household, washing your hands frequently, and wearing a mask in public.For some great insights on problems that exist with COVID-19 data, as well as other things to consider when working with these data, readthis article.InstallationInstall the package via pip:pip install covid19pandasUsageAfter importing the package, you can quickly access data with a simple function call:TutorialsOurtutorialsshow you how to create plots like the one below. Using our built-in functions, you can create many plot types in just two to five function calls.
Package: covid19.py
covid19.pyTrack the COVID-19 data in all the countries in the world from the command line.It is apythonscript inspired byahmadawais/corona-cliand it used the data fromNovelCOVIDInstallationpip install covid19.pyRunShow the first 10 countries by cases (excluded China):covid19.py -m 10Show the help:covid19.py -hLicenceDO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE, version 2, December 2004.
Package: covid19pyclient
Exampleimportjsonfromcovid19pyclientimportCovidDatacovid=CovidData()data=covid.germany_total()print(json.dumps(data,sort_keys=True,indent=4,ensure_ascii=False)){"cases":3682911,"casesPer100k":4428.3475391975035,"casesPerWeek":29248,"deaths":88595,"delta":{"cases":1785,"deaths":153,"recovered":11714},"meta":{"contact":"MarlonLueckert(m.lueckert@me.com)","info":"https://github.com/marlon360/rki-covid-api","lastCheckedForUpdate":"2021-06-01T08:17:40.241Z","lastUpdate":"2021-05-31T23:00:00.000Z","source":"RobertKoch-Institut"},"r":{"date":"2021-05-27T00:00:00.000Z","value":0.91},"recovered":3498382,"weekIncidence":35.16791712491793}Featured DataTotal number of COVID-19 related cases, deaths, incidence, recovered (country, by state, by district, by demographics)Timeseries data (country, districts, vaccinations, tests)And data derived therefromInstallationYou can installcovid19pyclientwith pip.$pipinstallcovid19pyclientAlternatively, you can download and installcovid19pyclientfrom github:$pipinstallgit+https://github.com/NiklasTiede/Covid19PyClientHow to useCovidData's class methods return the requested as dictionary.fromcovid19pyclientimportCovidDatacovid=CovidData()Here's a list of all the methods which can be used to access different data about COVID-19 in germany:covid.germany_total()covid.germany_timeseries()covid.germany_by_agegroups()covid.districts_total()covid.districts_timeseries()covid.states_total()covid.states_by_agegroups()covid.vaccinations_total()covid.vaccinations_timeseries()covid.testing_timeseries()For more information about these function, seeDocumentation.
Package: covid19-supplier-recovery
covid-19-supplier-economic-recoveryDISCLAIMERWe are not economists. We are not epidemiologists. All core assumptions that
are related to each of these fields are taken from experts. This is not an
academic paper and has not gone through a peer review process. As a pandemic
in modern times is a unique event, it is not possible to model resulting
economics events with any degree of certainty. The best we can likely hope
for is to maintain multiple plausible scenarios that can be used to inform high
level strategic planning.Any examples provided in this paper or documentation are NOT to be taken as
ready for use in any particular use case.Install# ALWAYS create a virtualenv first

# then pip install
pip install covid19-supplier-recoveryStep 1: Read the paperIt'shereRead it and understand it. It does not make sense to use this without
understanding the assumptions and structure.Step 2: See the API usageSay that you have a 10 week sales forecast in which you will sell
100 euros each week. You would express this forecast using a simple
python list:forecast=[100]*10Assuming a single 3-week lockdown starting on the second week:lockdowns=[{'start':1,'length':3,'immediate_loss':0.9,'client_deaths':generic_linear_func(x_intercept=0.05,slope=0.05),'lockdown_growth':generic_linear_func(x_intercept=0.0,slope=0.01)}]And the corresponding recovery:recoveries=[{'client_recovery':generic_linear_func(x_intercept=0.1,slope=0.01),'market_appetite':generic_linear_func(0.2,0.02)}]You would model your forecast like so:rm=RecoveryModel(normal_forecast=forecast,lockdowns=lockdowns,recoveries=recoveries)adjusted_forecasts=rm.model_recovery()And your adjusted forecasts should look like so:[# start business as usual100.0,# start lockdown10.0,11.0,12.0,# start recovery30.0,# .12 + ( (.85 + .10) * .20 )32.0,# .12 + ( (.85 + .11) * .22 )34.0,# .12 + ( (.85 + .12) * .24 )36.0,# .12 + ( (.85 + .13) * .26 )38.0,# .12 + ( (.85 + .14) * .28 )41.0,# forecast end]Step 3: Make your own forecastThis part will be hard. Selecting the parameters for the model is insanely
difficult and requires a TON of industry knowledge. You should probably
maintain several different scenarios and update them as more data, policy,
and other information becomes available.
Package: covid19umbria
Covid19UmbriaCovid-19 statistics for Regione Umbria, based on thepublic API.Cf. also theDashboard COVID-19 Regione Umbria.Partially inspired by thecovidpackage.Installationpip install covid19umbriaUsageSee theexample.Get All Datafromcovid19umbriaimportCovid19Umbriacovid19=Covid19Umbria()covid19.get_data()Get Currently Active casescurrent_active=covid19.get_current_active_cases()Get Total Confirmed casesnew_active=covid19.get_total_confirmed_cases()Get Total Recovered casesrecovered=covid19.get_total_recovered()Get Total Deathsdeaths=covid19.get_total_deaths()