<jupyter_start><jupyter_text>Parece que esse indice pode ser utilizado para inferir qual seria o momento da saída do comportamento exponencial.<jupyter_code>import pickle

pickle.dump(infect, open('../data/processed/infect.pkl', 'wb'))
pickle.dump(breakdown, open('../data/processed/breakdown.pkl', 'wb'))

breakdown['China']
df.loc['China'][4:].rolling(7).mean().diff().diff().plot()
fig, ax = plt.subplots(3, 1, figsize=(22, 10), sharex=True)
countries = ['Korea, South', 'Italy', 'Brazil']
for c in countries:
    infect[c].Infect.diff().plot(ax=ax[0],lw=4)
    infect[c].Infect.plot(ax=ax[1],lw=4)
    infect[c].New.plot(ax=ax[2], lw=4);
    try:
        val = infect[c].Infect.loc[pd.Timestamp(breakdown[c].index[0])]
        ax[1].scatter(breakdown[c].index[0], val, s=80)
    except KeyError:
        pass
        
ax[0].set_ylim([-0.1, 0.1])   
ax[0].legend(countries)
ax[1].legend(countries)
ax[2].legend(countries)

ax[0].set_title('Aceleração')
ax[1].set_title('Velocidade');
ax[2].set_title('Novos casos');<jupyter_output><empty_output>
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