def main():
"""Main function executed at the bottom."""
# Get all US surge data including states.
us_surge = Surge()
# Set parameters
us_surge.end_date = '4/20/20' # set end date wanted
us_surge.end_date = None # get all the data available
us_surge.ignore_last_n_days = 2 # allow for data repo to be updated
# *************************************************************************
# Combine all states into a country
# *************************************************************************
print('******************************************************************')
print('* US *')
print('******************************************************************')
print('# of states/distric: ', len(us_surge.names))
print('# of days: ', us_surge.dates.shape[0])
# Plot the data
us_surge.plot_covid_data(save=True)
n_last_days = 7
print('')
print('Last %i days'%n_last_days, ' # of cumulative cases = ',
np.sum(us_surge.cases, axis=1)[-n_last_days:])
print('Last %i days'%n_last_days, ' # of added cases =',
[b-a for (b, a) in
zip(np.sum(us_surge.cases, axis=1)[-(n_last_days-1):],
np.sum(us_surge.cases, axis=1)[-n_last_days:-1])])
print('')
# Fit data to model function
param_vec = us_surge.fit_data()
print('')
# Plot the fit data to model function of combined US data
us_surge.plot_covid_nlfit(param_vec, save=True, plot_prime=True,
plot_double_prime=True)
# Report critical times
(tcc, dtc) = us_surge.report_critical_times(param_vec, verbose=True)
# Report errors
us_surge.report_error_analysis(param_vec, tcc, dtc)
# 60-day look-ahead
n_prediction_days = 60
last_day = us_surge.dates.size
total_deaths_predicted = int(us_surge.sigmoid_func(n_prediction_days +
last_day, param_vec))
print('')
print('Estimated cumulative deaths in %s days from %s = %6i'%
(n_prediction_days, us_surge.dates[-1], total_deaths_predicted))
print('# of cumulative deaths today, %s = %6i'%
(us_surge.dates[-1], int(np.sum(us_surge.cases[-1, :]))))
print('')
def main():
"""Main function executed at the bottom."""
# Get global surge data
g_surge = Surge(locale='global')
print('# of countries: ', g_surge.cases.shape[1])
print('# of days: ', g_surge.cases.shape[0])
# Set parameters
g_surge.end_date = '4/20/20' # set end date wanted
g_surge.end_date = None # get all the data available
g_surge.ignore_last_n_days = 0 # allow for data repo to be updated
print('******************************************************************')
print('* Single Country *')
print('******************************************************************')
name = 'Brazil'
print(name)
print('')
# Plot the data
g_surge.plot_covid_data(name, save=True)
n_last_days = 7
country_id = g_surge.names.index(name)
print('')
print('Last %i days' % n_last_days, ' # of cumulative cases = ',
g_surge.cases[-n_last_days:, country_id])
print('Last %i days' % n_last_days, ' # of added cases =', [
b - a for (b, a) in zip(g_surge.cases[-(n_last_days - 1):, country_id],
g_surge.cases[-n_last_days:-1, country_id])
])
print('')
# Fit data to model function
param_vec = g_surge.fit_data(name)
print('')
# Plot the fit data to model function
g_surge.plot_covid_nlfit(param_vec,
name,
save=True,
plot_prime=True,
plot_double_prime=True)
# Report critical times
(tcc, dtc) = g_surge.report_critical_times(param_vec, name, verbose=True)
# Report errors
g_surge.report_error_analysis(param_vec, tcc, dtc, name)
# 60-day look-ahead
n_prediction_days = 60
last_day = g_surge.dates.size
total_deaths_predicted = int(
g_surge.sigmoid_func(n_prediction_days + last_day, param_vec))
print('')
print('Estimated cumulative deaths in %s days from %s = %6i'%\
(n_prediction_days, g_surge.dates[-1], total_deaths_predicted))
print('# of cumulative deaths today, %s = %6i'%\
(g_surge.dates[-1], g_surge.cases[-1, g_surge.names.index(name)]))
print('')
def main():
"""Main function executed at the bottom."""
# Get US surge data
us_surge = Surge()
# Set parameters
us_surge.end_date = '4/20/20' # set end date wanted
us_surge.end_date = None # get all the data available
us_surge.ignore_last_n_days = 0 # allow for data repo to be updated
#**************************************************************************
# Single State Case
#**************************************************************************
print('******************************************************************')
print('* Single State *')
print('******************************************************************')
name = 'New Mexico'
print(name)
print('')
# Plot the data
us_surge.plot_covid_data(name, save=True)
n_last_days = 7
state_id = us_surge.names.index(name)
print('')
print('Last %i days' % n_last_days, ' # of cumulative cases = ',
us_surge.cases[-n_last_days:, state_id])
print('Last %i days' % n_last_days, ' # of added cases =', [
b - a for (b, a) in zip(us_surge.cases[-(n_last_days - 1):, state_id],
us_surge.cases[-n_last_days:-1, state_id])
])
print('')
# Fit data to model function
param_vec = us_surge.fit_data(name)
print('')
# Plot the fit data to model function
us_surge.plot_covid_nlfit(param_vec,
name,
save=True,
plot_prime=True,
plot_double_prime=True)
# Report critical times
(tcc, dtc) = us_surge.report_critical_times(param_vec, name, verbose=True)
# Report errors
us_surge.report_error_analysis(param_vec, tcc, dtc, name)
# 60-day look-ahead
n_prediction_days = 60
last_day = us_surge.dates.size
total_deaths_predicted = int(
us_surge.sigmoid_func(n_prediction_days + last_day, param_vec))
print('')
print('Estimated cumulative deaths in %s days from %s = %6i'%\
(n_prediction_days, us_surge.dates[-1], total_deaths_predicted))
print('# of cumulative deaths today, %s = %6i'%\
(us_surge.dates[-1], us_surge.cases[-1, us_surge.names.index(name)]))
print('')