Cubic Spline of current data from WHO

covid.py

import urllib.request, json 
import matplotlib.pyplot as plt
import numpy
from scipy.interpolate import *

with urllib.request.urlopen("https://pomber.github.io/covid19/timeseries.json") as url:
    data = json.loads(url.read().decode())

    date = []

    for report in data['US']:
        date.append(report["date"])

    confirmed = [0] * len(date)
    deaths = [0] * len(date)
    recovered = [0] * len(date)

    for country in data:
        _confirmed = []
        _deaths = []
        _recovered = []
        for report in data[country]:
            _confirmed.append(report["confirmed"])
            _deaths.append(report["deaths"])
            _recovered.append(report["recovered"])
        for i in range(0, len(date)):
            confirmed[i] = (confirmed[i] + _confirmed[i])
            deaths[i] = (deaths[i] + _deaths[i])
            recovered[i] = (recovered[i] + _recovered[i])

    days_from_start = numpy.arange(len(date))

    confirmed_f = CubicSpline(days_from_start, confirmed, extrapolate=True)
    deaths_f = CubicSpline(days_from_start, deaths, extrapolate=True)
    recovered_f = CubicSpline(days_from_start, recovered, extrapolate=True)

    year = numpy.arange(100)

    plt.axhline(y=0, color='black')
    plt.axvline(x=days_from_start[-1], color='black')
    plt.axhline(y=7794798739, color='black')

    plt.plot(year, confirmed_f(year), color='y')
    plt.plot(year, deaths_f(year), color='r')
    plt.plot(year, recovered_f(year), color='b')
    plt.ylim(-7794798739, 7794798739)
    
    plt.ticklabel_format(style='plain')
    plt.ylabel("People")
    plt.xlabel(f"Days since {date[0]}")
    plt.show()