[Python least squares polynomial fitting method to calculate the predicted update] diagnosed with pneumonia Wuhan national trend comparison chart (the latest as of 2020-2-6)

import numpy as np 

import matplotlib.dates as mdates

import pylab as plt

import datetime as dt

 

 

plt.mpl.rcParams [ 'font.sans-serif'] = [ 'SimHei'] 

plt.mpl.rcParams['axes.unicode_minus'] = False 

 

date2_1 = dt.datetime(2020,1,23)

1. date2_2 = dt.datetime(2020,2,7)

delta2 = dt.timedelta (days = 1 )

dates2 = mdates .drange (date2_1, date2_2, delta2)

 

x = np.arange(1, 16, 1)
y = np.array([105,180,323,371,1291,840,1032,1220,1347,1921,2103,2345,3156,2987,2447])
y1 = np.array([ 250,444,680,760,1771,1450,1737,1982,2102,2580,2829,3235,3887,3694,3143])

z1 = np.polyfit(x, y, 6)

p1 = np.poly1d(z1)

z2 = np.polyfit(x, y1, 6)

p2= np.poly1d(z2)

 

plt.figure (figsize = (12, 5.5 )) # width 800 length 600
plt.subplot (121) # line two, which is the first in FIG
plt.plot ( dates2 , Y, 'B', label = 'Wuhan diagnosis')

plt.plot ( dates2 , P1 (X), 'G', label = 'Wuhan trendline')

plt.title ( 'Wuhan number of people diagnosed with pneumonia curve')
plt.legend ()

 

max = plt.gca ()

for label in ax.xaxis.get_ticklabels():

        label.set_rotation(90)

 

ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d'))

ax.xaxis.set_major_locator(mdates.DayLocator())

 

plt.subplot (122)

plt.plot ( dates2 , Y1, 'B', label = 'National confirmed')

plt.plot ( dates2 , P2 (X), 'G', label = 'National trend line ")

plt.title ( 'the country the number of diagnosed pneumonia curve')
plt.legend ()

 

max = plt.gca ()

for label in ax.xaxis.get_ticklabels():

        label.set_rotation(90)

 

ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d'))

ax.xaxis.set_major_locator(mdates.DayLocator())

 

#plt.savefig('data3/Kuaizi_Classification1_loss={0:.2f}_accur={1:.2f}_epoch={2}.jpg'.format(val_loss[-1], val_acc[-1], epochs[-1]))

 

plt.show()