def test_main():
# Get all US surge data including states.
us_surge = Surge()
# Set parameters
us_surge.end_date = '5/15/20' # set end date wanted
us_surge.ignore_last_n_days = 0 # allow for data repo to be corrected/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)
print('')
# Fit data to model function
param_vec = us_surge.fit_data()
#print(list(param_vec))
param_gold = np.array(
[98258.11249350989, 24.16030887578648, -0.09667519121651309])
assert np.allclose(param_vec, param_gold)
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
(tc, dtc) = us_surge.critical_times(param_vec, verbose=True)
#print(tc,dtc)
tc_gold = 32.942382813045036
dtc_gold = 13.622501081744613
assert np.allclose(np.array([tc, dtc]), np.array([tc_gold, dtc_gold]))
# Report errors
us_surge.error_analysis(param_vec, tc, dtc)
def main():
# 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 = 2 # allow for data repo to be corrected/updated
#****************************************************************************
# Single State Case
#****************************************************************************
print(
'********************************************************************')
print(
'* Single State *')
print(
'********************************************************************')
name = 'North Carolina'
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
(tc, dtc) = us_surge.critical_times(param_vec, name, verbose=True)
# Report errors
us_surge.error_analysis(param_vec, tc, 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('')
def main():
# 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 corrected/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
(tc, dtc) = us_surge.critical_times(param_vec, verbose=True)
# Report errors
us_surge.error_analysis(param_vec, tc, 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], np.sum(us_surge.cases[-1, :])))
print('')
