def get_fitting_data(args, df, country):
df = cu.get_country_data(df, country)
dates = df['date'].to_list()
data = df['confirmed'] - df['recovered'] - df['deaths']
data.index = df['date'].to_list()
data = data[data > args.start_number]
#return data[start_date:]
return data
def update_graph(country, start_date, end_date, mode, plot_style):
df = get_country_data(dF, country)
df.index = df['date'].to_list()
mask = (df['date'] > start_date) & (df['date'] <= end_date)
df = df.loc[mask]
X = df['confirmed']
Y = df['recovered']
Z = df['deaths']
if mode == 'Cumulative':
x = X
y = Y
z = Z
title = country + " Covid-19 (Total Cases)"
if mode == 'Daily':
x = X.diff(periods=1).iloc[1:]
y = Y.diff(periods=1).iloc[1:]
z = Z.diff(periods=1).iloc[1:]
title = country + " Covid-19 (Daily Cases)"
fig1 = px.scatter(x)
fig2 = px.scatter(y)
fig3 = px.scatter(z)
trace1 = fig1['data'][0]
trace2 = fig2['data'][0]
trace3 = fig3['data'][0]
fig = make_subplots(rows=2, cols=1,shared_xaxes=False,\
horizontal_spacing=0.1, vertical_spacing=0.05,\
y_title=country,subplot_titles=([title]))
fig.add_trace(trace1, row=1, col=1)
fig.add_trace(trace2, row=1, col=1)
fig.add_trace(trace3, row=2, col=1)
if plot_style == 'DotLine':
fig.data[0].update(mode='markers+lines')
fig.data[1].update(mode='markers+lines')
fig.data[2].update(mode='markers+lines')
fig['data'][0]['marker']['color'] = "blue"
fig['data'][1]['marker']['color'] = "green"
fig['data'][2]['marker']['color'] = "red"
fig.update_layout(width=1200,
height=600,
margin=dict(l=10, r=10, t=20, b=40))
return fig
default='../data/world_population.csv')
parser.add_argument('-c',
'--country-name',
help='Country name',
default='Italy')
parser.add_argument('-m',
'--epd-model',
help='Epidemiologcal model',
default='SIR')
parser.add_argument('-b', '--beta-model', help='Beta model', default='exp')
args = parser.parse_args()
dF = pd.read_csv(args.input_file)
df = get_country_data(dF, args.country_name)
df['removed'] = df['recovered'] + df['deaths']
df['infected'] = df['confirmed'] - df['removed']
df.index = df['date'].to_list()
data = df[['infected', 'removed']].copy()
data = data[data['infected'] > 25]
days = [int(i) for i in range(0, data.shape[0])]
I0, R0 = data.iloc[0]['infected'], data.iloc[0]['removed']
L = Learner(args.epd_model, beta_model=args.beta_model)
kwargs = {'N': 6.0E07, 'I0': I0, 'R0': R0}
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import sys
from common_utils import get_country_data
if __name__ == "__main__":
dF = pd.read_csv(sys.argv[1])
df1 = get_country_data(dF, 'US')
df2 = get_country_data(dF, 'Brazil')
df3 = get_country_data(dF, 'India')
df4 = get_country_data(dF, 'Russia')
fig = plt.figure(figsize=(12, 12))
ax = fig.add_plot(221)
bx = fig.add_plot(222)
cx = fig.add_plot(222)
dx = fig.add_plot(222)
countries = df_top['country'].to_list()
#with open ("/Users/jayanti/Projects/Softlab/Covid-19/code/output/17-05-2020/bad_fit.txt","r") as fp:
# data = fp.read()
#countries=data.split('\n')
#print(countries)
#sys.exit()
fig = plt.figure(figsize=(12, 18))
col = 1
for c in countries:
try:
dF = get_country_data(df, c)
except:
continue
x = [int(i) for i in range(0, dF.shape[0])]
x = dF['date'].to_list()
y = dF['confirmed'] - dF['deaths'] - dF['recovered']
if dF.shape[0] > 0 and col < 51 and c != 'China':
#lockdown_day = np.max (get_date_diff(dF.iloc[0]['date'], L[c]),0)
print(c, dF.shape, dF.iloc[0]['date'])
ax = fig.add_subplot(10, 5, col)
#fig = plt.figure(figsize=(18,12))
#ax = fig.add_subplot(1,1,1)
#plt.setp(ax.get_xticklabels(), rotation=90, horizontalalignment='right')
#ax.axvline(x=float(lockdown_day),c='k',ls='--')
ax.plot(x, y, 'b')
ax.append(fig.add_subplot(324))
ax.append(fig.add_subplot(325))
ax.append(fig.add_subplot(326))
for a in ax:
a.set_xlim(-1, 67)
a.set_ylim(-8.0, 11.0)
plt.subplots_adjust(wspace=0, hspace=0)
count = 0
for country in countries:
N, L, T = lockdown_info(args.lockdown_file, country)
df1 = get_country_data(df, country)
df1 = df1[df1['confirmed'] > 25]
print(country, df1.shape)
R = BetaSolver(df1, N)
beta = R.solve(gamma_in, sigma_in, alpha)
lockdown = beta.index.get_loc(L, method='ffill')
dates = beta.index
y = beta
days = np.array([i for i in range(0, beta.shape[0])])
print(count + 1, '&', country, '&', N, '&', T, '&', dates[0], '&', L,
' \\\ \hline')