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#!/usr/bin/python
import numpy as np
import pandas as pd
from csv import reader
from csv import writer
from scipy.integrate import solve_ivp
from scipy.optimize import minimize
import matplotlib.pyplot as plt
from datetime import timedelta, datetime
import argparse
import sys
import json
import ssl
import urllib.request
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument(
'--countries',
action='store',
dest='countries',
help='Countries on CSV format. ' +
'It must exact match the data names or you will get out of bonds error.',
metavar='COUNTRY_CSV',
type=str,
default="")
parser.add_argument(
'--download-data',
action='store_true',
dest='download_data',
help='Download fresh data and then run',
default=False
)
parser.add_argument(
'--start-date',
required=False,
action='store',
dest='start_date',
help='Start date on MM/DD/YY format ... I know ...' +
'It defaults to first data available 1/22/20',
metavar='START_DATE',
type=str,
default="1/22/20")
parser.add_argument(
'--prediction-days',
required=False,
dest='predict_range',
help='Days to predict with the model. Defaults to 150',
metavar='PREDICT_RANGE',
type=int,
default=150)
parser.add_argument(
'--S_0',
required=False,
dest='s_0',
help='S_0. Defaults to 100000',
metavar='S_0',
type=int,
default=100000)
parser.add_argument(
'--I_0',
required=False,
dest='i_0',
help='I_0. Defaults to 2',
metavar='I_0',
type=int,
default=2)
parser.add_argument(
'--R_0',
required=False,
dest='r_0',
help='R_0. Defaults to 0',
metavar='R_0',
type=int,
default=10)
args = parser.parse_args()
country_list = []
if args.countries != "":
try:
countries_raw = args.countries
country_list = countries_raw.split(",")
except Exception:
sys.exit("QUIT: countries parameter is not on CSV format")
else:
sys.exit("QUIT: You must pass a country list on CSV format.")
return (country_list, args.download_data, args.start_date, args.predict_range, args.s_0, args.i_0, args.r_0)
def sumCases_province(input_file, output_file):
with open(input_file, "r") as read_obj, open(output_file,'w',newline='') as write_obj:
csv_reader = reader(read_obj)
csv_writer = writer(write_obj)
lines=[]
for line in csv_reader:
lines.append(line)
i=0
ix=0
for i in range(0,len(lines[:])-1):
if lines[i][1]==lines[i+1][1]:
if ix==0:
ix=i
lines[ix][4:] = np.asfarray(lines[ix][4:],float)+np.asfarray(lines[i+1][4:] ,float)
else:
if not ix==0:
lines[ix][0]=""
csv_writer.writerow(lines[ix])
ix=0
else:
csv_writer.writerow(lines[i])
i+=1
def download_data(url_dictionary):
#Lets download the files
for url_title in url_dictionary.keys():
urllib.request.urlretrieve(url_dictionary[url_title], "./data/" + url_title)
def load_json(json_file_str):
# Loads JSON into a dictionary or quits the program if it cannot.
try:
with open(json_file_str, "r") as json_file:
json_variable = json.load(json_file)
return json_variable
except Exception:
sys.exit("Cannot open JSON file: " + json_file_str)
class Learner(object):
def __init__(self, country, loss, start_date, predict_range,s_0, i_0, r_0):
self.country = country
self.loss = loss
self.start_date = start_date
self.predict_range = predict_range
self.s_0 = s_0
self.i_0 = i_0
self.r_0 = r_0
def load_confirmed(self, country):
df = pd.read_csv('data/time_series_19-covid-Confirmed-country.csv')
country_df = df[df['Country/Region'] == country]
return country_df.iloc[0].loc[self.start_date:]
def load_recovered(self, country):
df = pd.read_csv('data/time_series_19-covid-Recovered-country.csv')
country_df = df[df['Country/Region'] == country]
return country_df.iloc[0].loc[self.start_date:]
def load_dead(self, country):
df = pd.read_csv('data/time_series_19-covid-Deaths-country.csv')
country_df = df[df['Country/Region'] == country]
return country_df.iloc[0].loc[self.start_date:]
def extend_index(self, index, new_size):
values = index.values
current = datetime.strptime(index[-1], '%m/%d/%y')
while len(values) < new_size:
current = current + timedelta(days=1)
values = np.append(values, datetime.strftime(current, '%m/%d/%y'))
return values
def predict(self, beta, gamma, data, recovered, death, country, s_0, i_0, r_0):
new_index = self.extend_index(data.index, self.predict_range)
size = len(new_index)
def SIR(t, y):
S = y[0]
I = y[1]
R = y[2]
return [-beta*S*I, beta*S*I-gamma*I, gamma*I]
extended_actual = np.concatenate((data.values, [None] * (size - len(data.values))))
extended_recovered = np.concatenate((recovered.values, [None] * (size - len(recovered.values))))
extended_death = np.concatenate((death.values, [None] * (size - len(death.values))))
return new_index, extended_actual, extended_recovered, extended_death, solve_ivp(SIR, [0, size], [s_0,i_0,r_0], t_eval=np.arange(0, size, 1))
def train(self):
recovered = self.load_recovered(self.country)
death = self.load_dead(self.country)
data = (self.load_confirmed(self.country) - recovered - death)
optimal = minimize(loss, [0.001, 0.001], args=(data, recovered, self.s_0, self.i_0, self.r_0), method='L-BFGS-B', bounds=[(0.00000001, 0.4), (0.00000001, 0.4)])
print(optimal)
beta, gamma = optimal.x
new_index, extended_actual, extended_recovered, extended_death, prediction = self.predict(beta, gamma, data, recovered, death, self.country, self.s_0, self.i_0, self.r_0)
df = pd.DataFrame({'Infected data': extended_actual, 'Recovered data': extended_recovered, 'Death data': extended_death, 'Susceptible': prediction.y[0], 'Infected': prediction.y[1], 'Recovered': prediction.y[2]}, index=new_index)
fig, ax = plt.subplots(figsize=(15, 10))
ax.set_title(self.country)
df.plot(ax=ax)
print(f"country={self.country}, beta={beta:.8f}, gamma={gamma:.8f}, r_0:{(beta/gamma):.8f}")
fig.savefig(f"{self.country}.png")
def loss(point, data, recovered, s_0, i_0, r_0):
size = len(data)
beta, gamma = point
def SIR(t, y):
S = y[0]
I = y[1]
R = y[2]
return [-beta*S*I, beta*S*I-gamma*I, gamma*I]
solution = solve_ivp(SIR, [0, size], [s_0,i_0,r_0], t_eval=np.arange(0, size, 1), vectorized=True)
l1 = np.sqrt(np.mean((solution.y[1] - data)**2))
l2 = np.sqrt(np.mean((solution.y[2] - recovered)**2))
alpha = 0.1
return alpha * l1 + (1 - alpha) * l2
def main():
countries, download, startdate, predict_range , s_0, i_0, r_0 = parse_arguments()
if download:
data_d = load_json("./data_url.json")
download_data(data_d)
sumCases_province('data/time_series_19-covid-Confirmed.csv', 'data/time_series_19-covid-Confirmed-country.csv')
sumCases_province('data/time_series_19-covid-Recovered.csv', 'data/time_series_19-covid-Recovered-country.csv')
sumCases_province('data/time_series_19-covid-Deaths.csv', 'data/time_series_19-covid-Deaths-country.csv')
for country in countries:
learner = Learner(country, loss, startdate, predict_range, s_0, i_0, r_0)
#try:
learner.train()
#except BaseException:
# print('WARNING: Problem processing ' + str(country) +
# '. Be sure it exists in the data exactly as you entry it.' +
# ' Also check date format if you passed it as parameter.')
if __name__ == '__main__':
main()
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