def getsir(params):
df_cn2, df_hubei2, df_wuhan, df_ca, df_it, df_sk, df_sg, df_uk = g.main()
mydf = df_wuhan
I = mydf.loc[:, 'confirmed'].values
R = mydf.loc[:, 'cured'].values
D = mydf.loc[:, 'dead'].values
N = np.full(len(I), 37.59E+6)
I = I - R - D
S = N - I
t = range(0, len(S))
Shat, Ihat, Rhat = sir(mydf, *params)
Dhat = N - Shat - Ihat - Rhat
# -------------check
#mat = pd.DataFrame(list(zip(S, I, R)), columns=['S', 'I', 'R'])
#print(mat)
# -------------plot things
# fig, axs = plt.subplots(2)
# fig.suptitle('Wuhan')
# plt.figure(11)
# #axs[0].set_yscale('log')
# #axs[1].set_yscale('log')
# #axs[0].plot(t, S, label='S')
# axs[0].plot(t, I, label='I')
# axs[0].plot(t, R, label='R')
# axs[0].legend()
# #axs[1].plot(t, Shat, label='Shat')
# axs[1].plot(t, Ihat, label='Ihat')
# axs[1].plot(t, Rhat, label='Rhat')
# plt.title('Doing a SIR')
# axs[1].legend()
# fig.show()
#plt.figure()
#plt.plot(t, S)
#plt.plot(t, Shat)
#plt.show()
return S, I, R, D, Shat, Ihat, Rhat, Dhat
import numpy as np
import matplotlib.pyplot as plt
import getfiles as g
from scipy.optimize import minimize, NonlinearConstraint
from scipy import interpolate
import pandas as pd
global I, R, D, title, population
df_cn2, df_hubei2, df_wuhan, df_ca, df_it, df_sk, df_sg, df_uk = g.main()
mydf = df_wuhan
title = 'Wuhan'
population = 1.18E+7
I = mydf.loc[:, 'confirmed'].values
R = mydf.loc[:, 'cured'].values
D = mydf.loc[:, 'dead'].values
I = I - R - D
def geterror(params):
""""Returns total errors"""""
print('params', params)
S, I, R, D, Shat, Ihat, Rhat, Dhat = getsir(params) # get key series
N = params[-1]
Imax = max(I)
where = np.where(I == Imax)[0]
where_hat = np.where(Ihat == max(Ihat))[0]
error_i = ((Imax - max(Ihat))/Imax)**2
k = ((where+1)/(where_hat+1)) # +1 to keep denominator non-zero
t = np.arange(0, len(Rhat)) # these generated by simulated dt
treal = np.arange(0, len(R)) # these are in days
#!usr/bin/env python3
import getfiles
import sys
if __name__ == "__main__":
filters = dict()
if len(sys.argv) > 2:
i = iter(sys.argv[2:])
filters = dict(zip(i, i))
if '-r' not in filters:
filters['-r'] = 0.0
if '-g' not in filters:
filters['-g'] = None
if '-y' not in filters:
filters['-y'] = 0
if '-l' not in filters:
filters['-l'] = 0
getfiles.main(sys.argv[1],filters)
def main():
df_cn2, df_hubei2, df_wuhan, df_ca, df_it, df_sk, df_sg, df_uk = g.main()
slope, intercept, r_value, p_value, std_err, plt = linlog_fit(df_wuhan, 'only pre-quarantine')
popt, pcov = sigmoidfit(df_wuhan)
popt, pcov = optimise()
def getsir(params):
df_cn2, df_hubei2, df_wuhan, df_ca, df_it, df_sk, df_sg, df_uk = g.main()
mydf = df_wuhan
I = mydf.loc[:, 'confirmed'].values
R = mydf.loc[:, 'cured'].values
D = mydf.loc[:, 'dead'].values
N = 37.59E+6
I = (I - R - D) / N
S = 1 - I
R = R / N
D = D / N
t = range(0, len(S))
Shat, Ihat, Rhat = sir(mydf, *params)
Dhat = 1 - Shat - Ihat - Rhat
print('maxI:{}, lastDeath:{}'.format(max(I), D[-1]))
print('maxIhat:{}, lastDHat:{}'.format(max(Ihat), Dhat[-1]))
fig, axs = plt.subplots(2, 2)
axs[0, 0].plot(t, S, label='S')
axs[0, 0].plot(t, Shat, linestyle='--', label='Shat')
axs[0, 0].set_title('Susceptible')
axs[0, 0].legend()
axs[0, 1].plot(t, I, label='I')
axs[0, 1].plot(t, Ihat, linestyle='--', label='Ihat')
axs[0, 1].set_title('Infected')
axs[0, 1].legend()
axs[1, 0].plot(t, R, label='R')
axs[1, 0].plot(t, Rhat, linestyle='--', label='Rhat')
axs[1, 0].set_title('Recovered')
axs[1, 0].legend()
axs[1, 1].plot(t, D, label='D')
axs[1, 1].plot(t, Dhat, linestyle='--', label='Dhat')
axs[1, 1].set_title('Dead')
axs[1, 1].legend()
fig.suptitle('Wuhan')
fig.show()
fig.savefig('plots/Wuhan_fitted.png')
#
# plt.figure()
# plt.plot(t, I, label='I', color='r')
# plt.plot(t, R, label='R', color='g')
# plt.plot(t, D, label='D', color='k')
# plt.plot(t, Ihat, label='Ihat', linestyle='--', color='r')
# plt.plot(t, Rhat, label='Rhat', linestyle='--', color='g')
# plt.plot(t, Dhat, label='Dhat', linestyle='--', color='k')
# plt.ylim(1E-10,0.002)
# #plt.yscale('log')
# plt.legend()
# plt.show()
#plt.figure()
#plt.plot(t, I, label='I')
#plt.plot(t, Ihat, label='Ihat')
#plt.legend()
#plt.show()
# -------------check
#mat = pd.DataFrame(list(zip(S, Shat, I, Ihat, R, Rhat, D, Dhat)), columns=['S', 'Shat', 'I', 'Ihat', 'R', 'Rhat', 'D', 'Dhat'])
#print(mat)
return S, I, R, D, Shat, Ihat, Rhat, Dhat
# this is a sheet to simulate the SIR model and inspect its fit with the data
# lots of plots in this sheet to visually inspect fit
import matplotlib.pyplot as plt
import getfiles as g
df_cn2, df_hubei2, df_wuhan, df_ca, df_it, df_sk, df_sg, df_uk = g.main()
print(df_wuhan.head(1))
for df in g.main():
df.reset_index(inplace=True)
x = range(0, df.shape[0])
sus = df.loc[:, 'suspected']
conf = df.loc[:, 'confirmed']
recov = df.loc[:, 'cured']
dead = df.loc[:, 'dead']
fig, axs = plt.subplots(2, 2)
print('-----')
try:
if not str(df.loc[0, 'provinceCode']) == 'nan':
title = "%s %d %d" % (df.loc[0, 'countryCode'],
int(df.loc[0, 'provinceCode']),
int(df.loc[0, 'cityCode']))
fig.suptitle(title)
else:
title = "%s" % (df.loc[0, 'countryCode'])
fig.suptitle(df.loc[0, 'countryCode'])
except KeyError:
title = "%s" % (df.loc[0, 'countryCode'])
fig.suptitle(df.loc[0, 'countryCode'])
print(title)
axs[0, 0].plot(x, sus)