def main(load_inter_flag=False):
if not load_inter_flag:
real_data = list(pd.read_csv(get_data_path(True))['adcode'].unique())
start_date = date(2020, 1, 24)
real_data1 = pd.read_csv(get_data_path(True))
history_real = prepareData(real_data1)
rerun_cities = None
for i in range(get_seed_num()):
total_params = pd.read_csv(
os.path.join(os.path.dirname(__file__),
'./params_foreign{}.csv'.format(int(i))))
for ind, city in enumerate(real_data):
print(i, ind, city, str(get_important_date(city)))
x = get_params(total_params, city)
if rerun_cities is None or city in rerun_cities:
run_simulation2(x,
city,
60,
60,
start_date,
get_important_date(city),
history_real,
unob_flow_num=None,
json_name='data_run_foreign{}.json'.format(
int(i)))
# choose the final parameters
max_data, min_data, mean_data, best_data, \
std_data, middle_data, xmin_data, xmax_data, \
xmean_data, xstd_data = load_and_save('./data_run_foreign{}.json', './data_run_foreign_{}.json')
"""
def disp():
flow_out_data = flowOutData()
for i in range(4):
real_data = pd.read_csv(get_data_path())
data = json.load(open('data_run_no_healed{}.json'.format(int(i))))
infectratio = InfectRatio(1, [[0., 1.]], [True])
touchratio = TouchRatio(1, [[0.0, 0.6]], [True])
touchratiointra = TouchRatio(1, [[0, 1]], [True])
obs = ObservationRatio(1, [[0.0, 0.3]], [True])
dead = DeadRatio(1, [[0., 0.01]], [True])
isoratio = IsolationRatio(1, [[0.0, 0.5]], [True])
dummy = DummyModel(1, [[0, 400]], [True, True])
cure_ratio = InfectRatio(1, [[0., 0.1]], [True])
city = 420000
start_date = date(2020, 1, 11)
# set the time of applying touchratio
simulator = Simulator(city,
infectratio,
touchratio,
obs,
dead,
dummy,
isoratio,
touchratiointra,
cure_ratio, [get_important_date(city)],
unob_flow_num=None,
flow_out_data=flow_out_data,
training_date_end=None)
simulator.set_param(data['x'][str(city)])
duration = len(real_data["date"].unique()) - 1
sim_res, _ = simulator.simulate(str(start_date), duration)
plot1(data['real_confirmed'][str(city)], sim_res['observed'], '',
'no_heal{}.pdf'.format(int(i)), 7)
def prepare_data_china(load_inter_flag=False):
real_data = pd.read_csv(get_data_path())['adcode'].unique()
rerun_cities = None
if rerun_cities is not None:
real_data = rerun_cities.copy()
start_date = date(2020, 1, 11)
real_data1 = pd.read_csv(get_data_path())
history_real = prepareData(real_data1)
if not load_inter_flag:
print('re-simulate the progress')
province_travel_dict = flowHubei()
for i in range(get_seed_num()):
total_params = pd.read_csv(
os.path.join(os.path.dirname(__file__),
'./params{}.csv'.format(int(i))))
unob_flow_num = initHubei(
get_params(total_params, 420000),
start_date,
important_date=[get_important_date(420000)],
travel_from_hubei=province_travel_dict)
for ind, city in enumerate(real_data):
print(i, ind, city)
x = get_params(total_params, city)
run_simulation2(x,
city,
90,
90,
start_date,
get_important_date(city),
history_real,
unob_flow_num=unob_flow_num,
json_name='data_run{}.json'.format(int(i)))
else:
print('directly load the pre-simulated data')
for i in range(get_seed_num()):
get_total_province('data_run{}.json'.format(int(i)))
# choose the final parameters
max_data, min_data, mean_data, best_data, std_data, middle_data, \
xmin_data, xmax_data, xmean_data, xstd_data = load_and_save(
'./data_run{}.json', './data_run_{}.json')
"""
def run_simulation(x,
city,
simulate_day1,
simulate_day2,
start_date,
simulate_date,
incubation=3,
unob_flow_num=None,
json_name='data_run.json'):
real_data = pd.read_csv(get_data_path(True))
history_real = prepareData(real_data)
run_simulation2(x, city, simulate_day1, simulate_day2, start_date,
simulate_date, history_real, incubation, unob_flow_num,
json_name)
def train_china():
province_travel_dict = flowHubei()
real_data = pd.read_csv(get_data_path())['adcode'].unique()
province_code_dict = codeDict()
for i in range(1):
all_param = {}
x = run_opt_china(420000,
200000,
start_date=date(2020, 1, 11),
important_dates=[get_important_date(420000)],
repeat_time=3,
training_date_end=training_end_date,
seed=i,
json_name='data_run_lstm_china{}.json'.format(
int(i)),
loss_ord=4.,
touch_range=[0, 0.33])
unob_flow_num = initHubei(x,
start_date=date(2020, 1, 11),
important_date=[get_important_date(420000)],
travel_from_hubei=province_travel_dict)
all_param[420000] = x
real_data = [110000, 440000, 330000, 310000, 320000, 120000]
for ind, item in enumerate(real_data):
print(i, ind, item, province_code_dict[item])
if item == 420000:
continue
x = run_opt_china(item,
40000,
start_date=date(2020, 1, 11),
important_dates=[get_important_date(420000)],
infectratio_range=[0.0, 0.05],
dummy_range=[0, 0.000001],
unob_flow_num=unob_flow_num,
repeat_time=2,
training_date_end=training_end_date,
json_name='data_run_lstm_china{}.json'.format(
int(i)),
loss_ord=4.,
touch_range=[0.0, 0.33],
iso_range=[0.03, 0.12])
all_param[item] = x
all_param_df = pd.DataFrame(all_param)
all_param_df.to_csv('params_lstm_china{}.csv'.format(int(i)),
index=False)
load_and_save('data_run_lstm_china{}.json', 'data_run_lstm_china_{}.json',
1)
def run_opt(city, budget, start_date, important_dates, infectratio_range=None,
dummy_range=None, unob_flow_num=None, repeat_time=1, init_samples=None,
training_end_date=None, json_name='data_run.json', seed=3, loss_ord=0.0,
touch_range=None,iso_range=None):
assert infectratio_range is not None and dummy_range is not None
days_predict = 0
# load data
real_data = pd.read_csv(get_data_path(True))
history_real = prepareData(real_data)
flow_out_data = None
# initialize models
infectratio = InfectRatio(1, [infectratio_range], [True])
if touch_range is None:
touchratio = TouchRatio(1, [[0.999, 1.0000]], [True])
else:
touchratio = TouchRatio(1, [touch_range], [True])
touchratiointra = TouchRatio(1, [[0, 1]], [True])
obs = ObservationRatio(1, [[0.0, 0.3]], [True])
dead = DeadRatio(1, [[0., 0.01]], [True])
if iso_range is None:
isoratio = IsolationRatio(1, [[0.03, 0.12]], [True])
else:
isoratio = IsolationRatio(1, [iso_range], [True])
dummy = DummyModel(1, [dummy_range], [True, True])
cure_ratio = InfectRatio(1, [[0., 0.15]], [True])
simulator = Simulator(city, infectratio, touchratio, obs, dead, dummy, isoratio, touchratiointra, cure_ratio, important_dates,
unob_flow_num=unob_flow_num, flow_out_data=flow_out_data, training_date_end=training_end_date)
test_date = datetime.strptime(history_real['date'].max(), '%Y-%m-%d').date() - timedelta(days_predict)
history_real = history_real[history_real['adcode'] == city]
history_real = history_real[history_real['date'] >= str(start_date)]
history_train = history_real[history_real['date'] <= str(test_date)]
x, y = simulator.fit(history_train, budget=budget, server_num=get_core_num(),
repeat=repeat_time, seed=seed, intermediate_freq=10000, init_samples=init_samples,
loss_ord=loss_ord)
print('best_solution: x = ', x, 'y = ', y)
simulator.set_param(x)
run_simulation(x, city, 60, 60, start_date, get_important_date(city), unob_flow_num=unob_flow_num, json_name=json_name)
return x
def main(load_inter_flag=False):
if not load_inter_flag:
if korea_flag:
train_korea()
else:
train_china()
fmt = 'pdf'
#all_city = pd.read_csv(get_data_path())['adcode'].unique()
all_city = [420000, 110000, 440000, 330000, 310000, 320000, 120000]
if korea_flag:
all_city = [900003]
mlsim_loss_array = []
seir_loss_array = []
lstm_loss_array = []
for city in all_city:
print(city)
torch.manual_seed(6)
if not os.path.exists('./img/{}{}/'.format('./lstm', str(city))):
os.mkdir('./img/{}{}/'.format('./lstm', str(city)))
if not korea_flag:
div_idx = period_it
dataX_list, dataY_list = [], []
mask_list = []
_DataX, _DataY = construct_data_set(prepare_data(), foresee_size)
_DataX, _DataY = np.expand_dims(_DataX,
0), np.expand_dims(_DataY, 0)
dataX_list.append(_DataX)
dataY_list.append(_DataY)
mask_list.append(np.zeros((1, np.shape(_DataY)[1], 1)))
TestDataX, TestDataY = construct_data_set(
get_COVID_new_confirm(cities=city), foresee_size)
mask_list.append(np.zeros((1, np.shape(_DataY)[1], 1)))
dataX_list.append(np.zeros((1, np.shape(_DataY)[1], foresee_size)))
dataY_list.append(np.zeros((1, np.shape(_DataY)[1], 1)))
for i in range(div_idx):
mask_list[-1][0][i][0] = 1
dataY_list[-1][0][i][0] = TestDataY[i][0]
for ii in range(foresee_size):
dataX_list[-1][0][i][ii] = TestDataX[i][ii]
DataX = np.concatenate(dataX_list, 0)
DataY = np.concatenate(dataY_list, 0)
mask = np.concatenate(mask_list, 0)
TestDataX, TestDataY = construct_data_set(
get_COVID_new_confirm(cities=city), foresee_size)
_, real_cum = construct_data_set(
get_COVID_new_confirm('real_cum_confirmed', cities=city),
foresee_size)
_, sim_cum = construct_data_set(
get_COVID_new_confirm('sim_cum_confirmed_deduction_s1',
cities=city), foresee_size)
_, sim_inc = construct_data_set(
get_COVID_new_confirm('sim_confirmed_deduction_s1',
cities=city), foresee_size)
load_model = False
_, sim_inc_seir = construct_data_set(
ChinaSEIRData(city=city)[:-3], foresee_size)
else:
div_idx = period_it
dataX_list, dataY_list = [], []
mask_list = []
for item in pd.read_csv(get_data_path())['adcode'].unique():
_DataX, _DataY = construct_data_set(
get_COVID_new_confirm(cities=int(item)), foresee_size)
_DataX, _DataY = np.expand_dims(_DataX,
0), np.expand_dims(_DataY, 0)
dataX_list.append(_DataX)
dataY_list.append(_DataY)
mask_list.append(np.ones((1, np.shape(_DataY)[1], 1)))
TestDataX, TestDataY = construct_data_set(
get_COVID_new_confirm(file='data_run_lstm_korea0.json',
cities=900003), foresee_size)
mask_list.append(np.zeros((1, np.shape(_DataY)[1], 1)))
dataX_list.append(np.zeros((1, np.shape(_DataY)[1], foresee_size)))
dataY_list.append(np.zeros((1, np.shape(_DataY)[1], 1)))
for i in range(div_idx):
mask_list[-1][0][i][0] = 1
dataY_list[-1][0][i][0] = TestDataY[i][0]
for ii in range(foresee_size):
dataX_list[-1][0][i][ii] = TestDataX[i][ii]
DataX = np.concatenate(dataX_list, 0)
DataY = np.concatenate(dataY_list, 0)
mask = np.concatenate(mask_list, 0)
TestDataX, TestDataY = construct_data_set(
get_COVID_new_confirm(file='data_run_lstm_korea0.json',
cities=900003), foresee_size)
_, real_cum = construct_data_set(
get_COVID_new_confirm(type='real_cum_confirmed',
file='data_run_lstm_korea0.json',
cities=900003), foresee_size)
_, sim_cum = construct_data_set(
get_COVID_new_confirm('sim_cum_confirmed_deduction_s1',
file='data_run_lstm_korea0.json',
cities=900003), foresee_size)
_, sim_inc = construct_data_set(
get_COVID_new_confirm('sim_confirmed_deduction_s1',
file='data_run_lstm_korea0.json',
cities=900003), foresee_size)
_, sim_inc_seir = construct_data_set(HanSEIRData()[:],
foresee_size)
load_model = False
den = 4500
if city < 900000 and not city == 420000:
den = 200
# print(TestDataX)
if korea_flag:
pred_train, pred_test = train_and_pred(DataX,
DataY,
TestDataX[:div_idx][:],
len(sim_cum) - div_idx,
den,
load_model=load_model,
mask=mask)
else:
pred_train, pred_test = train_and_pred(DataX,
DataY,
TestDataX[:div_idx][:],
len(sim_cum) - div_idx,
den,
load_model=load_model,
mask=mask)
pred_test = pred_test * den
pred_test[pred_test < 0] = 0
if not os.path.exists('./img/lstm'):
os.mkdir('./img/lstm')
max_len = len(TestDataY)
plot1(
sim_inc[:max_len],
TestDataY,
pred_test[:max_len],
sim_inc_seir[:max_len],
'The number of newly confirmed cases ',
'./img/{}{}/newly_real_sim.{}'.format('./lstm', str(city), fmt),
date_it=date_it,
loc='upper right',
)
plot1(sim_cum,
real_cum,
np.cumsum(pred_test),
np.cumsum(sim_inc_seir),
'The cumulative number of newly confirmed cases ',
'img/{}{}/cum_real_sim.{}'.format('./lstm', str(city), fmt),
date_it=date_it,
loc='lower right')
if city == 420000:
plot1(
sim_inc,
TestDataY,
pred_test,
sim_inc_seir,
'The number of newly confirmed cases ',
'./img/{}{}/newly_real_sim.{}'.format('./lstm', str(city),
fmt),
date_it=date_it,
loc='upper right',
#y_max2=7000
)
if city == 900003:
plot1(sim_inc[:max_len],
TestDataY,
pred_test[:max_len],
sim_inc_seir[:max_len],
'The number of newly confirmed cases ',
'./img/{}{}/newly_real_sim.{}'.format(
'./lstm', str(city), fmt),
date_it=date_it,
loc='upper left',
interval=12)
train_size = (date_it - start_date).days
print(train_size, len(TestDataY))
test_label = np.array([TestDataY[item] for item in range(train_size)])
loss_ours = np.sqrt(
np.mean(
np.square(
test_label -
np.array([sim_inc[item] for item in range(train_size)]))))
loss_seir = np.sqrt(
np.mean(
np.square(test_label - np.array(
[sim_inc_seir[item] for item in range(train_size)]))))
loss_lstm = np.sqrt(
np.mean(
np.square(
test_label -
np.array([pred_test[item]
for item in range(train_size)]))))
print('training, ', city, ' ours: ', loss_ours, 'seir: ', loss_seir,
'lstm: ', loss_lstm)
mlsim_loss_array.append(loss_ours)
seir_loss_array.append(loss_seir)
lstm_loss_array.append(loss_lstm)
test_label = np.array(
[TestDataY[item] for item in range(train_size, len(TestDataY))])
loss_ours = np.sqrt(
np.mean(
np.square(test_label - np.array([
sim_inc[item] for item in range(train_size, len(TestDataY))
]))))
loss_seir = np.sqrt(
np.mean(
np.square(test_label - np.array([
sim_inc_seir[item]
for item in range(train_size, len(TestDataY))
]))))
loss_lstm = np.sqrt(
np.mean(
np.square(test_label - np.array([
pred_test[item]
for item in range(train_size, len(TestDataY))
]))))
print('testing, ours: ', loss_ours, 'seir: ', loss_seir, 'lstm: ',
loss_lstm)
mlsim_loss_array.append(loss_ours)
seir_loss_array.append(loss_seir)
lstm_loss_array.append(loss_lstm)
print('MLSim ', sep='', end='')
for lsim in mlsim_loss_array:
print('& ${:.2f}$ '.format(lsim), sep='', end='')
print('\\\\')
print('SEIR ', sep='', end='')
for lsim in seir_loss_array:
print('& ${:.2f}$ '.format(lsim), sep='', end='')
print('\\\\')
print('LSTM ', sep='', end='')
for lsim in lstm_loss_array:
print('& ${:.2f}$ '.format(lsim), sep='', end='')
print('\\\\')
pass
def main():
data = {}
code_dict = it_code_dict()
fmt = 'pdf'
#total_cities = pd.read_csv("./data/province_data.csv")['adcode'].unique()
total_cities = list(pd.read_csv(get_data_path(True))['adcode'].unique())
important_cities = total_cities
important_cities = [900003, 900004, 900005, 900006, 900007, 900008]
with open('./data_run_foreign_middle.json', 'r') as f:
data = json.load(f)
with open('./data_run_foreign_xmin.json', 'r') as f:
min_data = json.load(f)
with open('./data_run_foreign_xmax.json', 'r') as f:
max_data = json.load(f)
if not os.path.exists('./img'):
os.mkdir('./img')
if not os.path.exists('./img/0000'):
os.makedirs('./img/0000')
for item in important_cities:
it_path = './img/{}'.format(str(item))
if not os.path.exists(it_path):
os.mkdir(it_path)
print(
"final day: ",
date(2020, 1, 24) + timedelta(days=len(
get_total(data, 'sim_cum_confirmed_deduction_s1', total_cities))))
final_date = date(2020, 1, 24) + timedelta(days=len(
get_total(data, 'sim_cum_confirmed_deduction_s1', total_cities)))
cur_date = date(2020, 1, 24) + timedelta(
days=len(get_total(data, 'real_confirmed', total_cities)))
print('early')
for item in important_cities:
print(code_dict[int(item)], ": ",
data['sim_cum_confirmed_deduction_s3'][str(item)][-1])
print(len(data['real_confirmed'][str(item)]))
print('late')
for item in important_cities:
print(code_dict[int(item)], ": ",
data['sim_cum_confirmed_deduction_s4'][str(item)][-1])
print('3-20')
for item in important_cities:
print(code_dict[int(item)], ": ",
data['sim_cum_confirmed_deduction_s6'][str(item)][-1])
city_d = [900004, 900005, 900006, 900007, 900008, 900003]
print('\n\n===========')
print('country ', sep='', end='')
for item in city_d:
print('& ', code_dict[item], sep='', end='')
print('\\\\')
print('total confirmed cases', sep='', end='')
for item in city_d:
print(' & ',
int(data['sim_cum_confirmed_deduction_s1'][str(item)][-1]),
sep='',
end='')
print('\\\\')
print('total infections', sep='', end='')
for item in city_d:
print(' & ',
int(data['sim_cum_infection_deduction_s1'][str(item)][-1]),
sep='',
end='')
print('\\\\')
print('===========\n\n')
print('Date when $k$ was changed & ', sep='', end='')
for item in city_d:
print(code_dict[item], ' & ', sep='', end='')
print('\\\\')
print('middle &', sep='', end='')
for item in city_d:
print(int(data['sim_cum_confirmed_deduction_s1'][str(item)][-1]),
' & ',
sep='',
end='')
print(int(data['sim_cum_infection_deduction_s1'][str(item)][-1]),
' & ',
sep='',
end='')
print('\\\\')
print('min&', sep='', end='')
for item in city_d:
print(int(min_data['sim_cum_confirmed_deduction_s1'][str(item)][-1]),
' & ',
sep='',
end='')
print(int(min_data['sim_cum_infection_deduction_s1'][str(item)][-1]),
' & ',
sep='',
end='')
print('\\\\')
print('max&', sep='', end='')
for item in city_d:
print(int(max_data['sim_cum_confirmed_deduction_s1'][str(item)][-1]),
' & ',
sep='',
end='')
print(int(max_data['sim_cum_infection_deduction_s1'][str(item)][-1]),
' & ',
sep='',
end='')
print('\\\\')
print(
'country&real confirmed&MLSim confirmed&MLSim infected& proportion of asymptomatic\\\\\\hline'
)
lreal = len(get_total(data, 'real_cum_confirmed', total_cities))
for item in city_d:
real_cum = data['real_cum_confirmed'][str(item)][-1]
sim_cum_confirm = data['sim_cum_confirmed'][str(item)][-1]
sim_cum_infect = data['sim_cum_infection_deduction_s1'][str(item)][
lreal - 1]
print(code_dict[int(item)],
' & ',
real_cum,
' & ',
int(sim_cum_confirm),
' & ',
int(sim_cum_infect),
' & ',
np.round((1 - sim_cum_confirm / sim_cum_infect) * 100, 3),
'$\\%$\\\\ ',
sep='')
#exit(0)
lreal = len(get_total(data, 'real_cum_confirmed', total_cities))
# draw compare to real data for every city and full
print('drawing real/sim compare')
for item in important_cities:
print(item)
plot1_shade(data['real_cum_confirmed'][str(item)],
data['sim_cum_confirmed'][str(item)],
min_data['sim_cum_confirmed'][str(item)],
max_data['sim_cum_confirmed'][str(item)],
'The cumulative number of confirmed cases ',
'img/{}/cum_real_sim.{}'.format(str(item), fmt))
plot1_shade(data['real_confirmed'][str(item)],
data['sim_confirmed'][str(item)],
min_data['sim_confirmed'][str(item)],
max_data['sim_confirmed'][str(item)],
'The newly number of confirmed cases ',
'img/{}/increase_real_sim.{}'.format(str(item), fmt))
print('drawing real/sim compare forecast')
for item in important_cities:
print(item)
plot1_shade(data['real_cum_confirmed'][str(item)],
data['sim_cum_confirmed_deduction_s1'][str(item)],
min_data['sim_cum_confirmed_deduction_s1'][str(item)],
max_data['sim_cum_confirmed_deduction_s1'][str(item)],
'The cumulative number of confirmed cases ',
'img/{}/only_cum_real_sim.{}'.format(str(item), fmt),
18,
date_it=get_important_date(item))
plot1_shade(data['real_confirmed'][str(item)],
data['sim_confirmed_deduction_s1'][str(item)],
min_data['sim_confirmed_deduction_s1'][str(item)],
max_data['sim_confirmed_deduction_s1'][str(item)],
'The newly number of confirmed cases ',
'img/{}/only_increase_real_sim.{}'.format(str(item), fmt),
18,
date_it=get_important_date(item))
plot12_shade(
data['real_confirmed'][str(item)],
data['sim_confirmed'][str(item)],
min_data['sim_confirmed'][str(item)],
max_data['sim_confirmed'][str(item)],
data['sim_new_infection'][str(item)],
min_data['sim_new_infection'][str(item)],
max_data['sim_new_infection'][str(item)],
'The newly number of confirmed cases ',
'img/{}/increase_real_sim_infect.{}'.format(str(item), fmt))
plot12_shade(
data['real_cum_confirmed'][str(item)],
data['sim_cum_confirmed'][str(item)],
min_data['sim_confirmed'][str(item)],
max_data['sim_confirmed'][str(item)],
data['sim_cum_infection_deduction_s1'][str(item)][:lreal],
min_data['sim_cum_infection_deduction_s1'][str(item)][:lreal],
max_data['sim_cum_infection_deduction_s1'][str(item)][:lreal],
'The newly number of confirmed cases ',
'img/{}/cum_real_sim_infect.{}'.format(str(item), fmt))
print('==================================', code_dict[item],
str(cur_date))
print('current total infections and confirmed cases')
print('confirmed')
print('middle: ')
print(
'100\%',
format(int(data['sim_cum_confirmed'][str(item)][lreal - 1]), ','),
'({}-{})'.format(
format(
int(min_data['sim_cum_confirmed'][str(item)][lreal - 1]),
','),
format(
int(max_data['sim_cum_confirmed'][str(item)][lreal - 1]),
',')))
print('min: ')
print(
'100\%',
format(int(min_data['sim_cum_confirmed'][str(item)][lreal - 1]),
','))
print('max: ')
print(
'100\%',
format(int(max_data['sim_cum_confirmed'][str(item)][lreal - 1]),
','))
print('real: ',
format(int(data['real_cum_confirmed'][str(item)][-1]), ','))
print('infected')
print('middle: ')
print(
'100\%',
format(
int(data['sim_cum_infection_deduction_s1'][str(item)][lreal -
1]),
','), '({}-{})'.format(
format(
int(min_data['sim_cum_infection_deduction_s1'][str(
item)][lreal - 1]), ','),
format(
int(max_data['sim_cum_infection_deduction_s1'][str(
item)][lreal - 1]), ',')))
print('min: ')
print(
'100\%',
format(
int(min_data['sim_cum_infection_deduction_s1'][str(item)][lreal
-
1]),
','))
print('max: ')
print(
'100\%',
format(
int(max_data['sim_cum_infection_deduction_s1'][str(item)][lreal
-
1]),
','))
print('asym')
print('middle: ')
print('100\%', format(int(data['current_asym'][str(item)]), ','))
print('min: ')
print('100\%', format(int(min_data['current_asym'][str(item)]), ','))
print('max: ')
print('100\%', format(int(max_data['current_asym'][str(item)]), ','))
print('final asym')
print('middle: ')
print('100\%', format(int(data['final_asym'][str(item)]), ','))
print('min: ')
print('100\%', format(int(min_data['final_asym'][str(item)]), ','))
print('max: ')
print('100\%', format(int(max_data['final_asym'][str(item)]), ','))
print('cum self_cure')
print(
'100\%',
format(
int(data['sim_cum_self_cured_deduction_s1'][str(item)][lreal -
1]),
','), '({}-{})'.format(
format(
int(min_data['sim_cum_self_cured_deduction_s1'][str(
item)][lreal - 1]), ','),
format(
int(max_data['sim_cum_self_cured_deduction_s1'][str(
item)][lreal - 1]), ',')))
print('total infection')
print(
'100\%',
format(
int(data['sim_total_infection_deduction_s1'][str(item)][lreal -
1]),
','), '({}-{})'.format(
format(
int(min_data['sim_total_infection_deduction_s1'][str(
item)][lreal - 1]), ','),
format(
int(max_data['sim_total_infection_deduction_s1'][str(
item)][lreal - 1]), ',')))
print('nosymbol')
print(
'100\%',
format(
int(data['sim_cum_nosymbol_deduction_s1'][str(item)][lreal -
1]), ','),
'({}-{})'.format(
format(
int(min_data['sim_cum_nosymbol_deduction_s1'][str(item)][
lreal - 1]), ','),
format(
int(max_data['sim_cum_nosymbol_deduction_s1'][str(item)][
lreal - 1]), ',')))
# sim_total_isolation_deduction_s1
print('total iso')
print(
'100\%',
format(
int(data['sim_total_isolation_deduction_s1'][str(item)][lreal -
1]),
','), '({}-{})'.format(
format(
int(min_data['sim_total_isolation_deduction_s1'][str(
item)][lreal - 1]), ','),
format(
int(max_data['sim_total_isolation_deduction_s1'][str(
item)][lreal - 1]), ',')))
print('ratio')
print('{:.3f} ({:.3f}-{:.3f})'.format(
1 - data['current_asym'][str(item)],
1 - min_data['current_asym'][str(item)],
1 - max_data['current_asym'][str(item)]))
print('==================================', code_dict[item],
str(cur_date))
"""
plot1(get_total(data, 'real_cum_confirmed',total_cities), get_total(data,'sim_cum_confirmed', total_cities),
'The cumulative number of confirmed cases ',
'img/{}/cum_real_sim.{}'.format('0000', fmt))
plot1(get_total(data, 'real_confirmed',total_cities), get_total(data,'sim_confirmed', total_cities),
'The newly number of confirmed cases ',
'img/{}/increase_real_sim.{}'.format('0000', fmt))
"""
# draw different deduction in Feb 24th
print('drawing different deduction')
for item in important_cities:
print(item)
it_max = None
if item == 900004:
it_max = 85000
if item == 900005:
it_max = 10000
if item == 900006:
it_max = 15000
if item == 900007:
it_max = 20000
if item == 900008:
it_max = 25000
it_min = 0
plot3(data['sim_cum_confirmed_deduction_s1'][str(item)],
data['sim_cum_confirmed_deduction_s2'][str(item)],
data['sim_cum_confirmed_deduction_s3'][str(item)],
data['real_cum_confirmed'][str(item)],
'Prediction of the cumulative number of confirmed cases',
'img/{}/cum_confirmed_prediction.{}'.format(str(item), fmt),
touchratio=data['x'][str(item)][1],
ratio_low=data['touch_ratio_low'][str(item)],
ratio_high=data['touch_ratio_hight'][str(item)],
date_it=get_important_date(item),
start_date_it=date(2020, 2, 15))
plot3(data['sim_confirmed_deduction_s1'][str(item)],
data['sim_confirmed_deduction_s2'][str(item)],
data['sim_confirmed_deduction_s3'][str(item)],
data['real_confirmed'][str(item)],
'Prediction of the cumulative number of confirmed cases',
'img/{}/confirmed_prediction.{}'.format(str(item), fmt),
touchratio=data['x'][str(item)][1],
ratio_low=data['touch_ratio_low'][str(item)],
ratio_high=data['touch_ratio_hight'][str(item)],
date_it=get_important_date(item),
it_max=it_max,
it_min=it_min,
start_date_it=date(2020, 2, 15))
plot3_shade(
data['sim_cum_confirmed_deduction_s1'][str(item)],
min_data['sim_cum_confirmed_deduction_s1'][str(item)],
max_data['sim_cum_confirmed_deduction_s1'][str(item)],
data['sim_cum_confirmed_deduction_s2'][str(item)],
min_data['sim_cum_confirmed_deduction_s2'][str(item)],
max_data['sim_cum_confirmed_deduction_s2'][str(item)],
data['sim_cum_confirmed_deduction_s3'][str(item)],
min_data['sim_cum_confirmed_deduction_s3'][str(item)],
max_data['sim_cum_confirmed_deduction_s3'][str(item)],
data['sim_cum_confirmed_deduction_s8'][str(item)],
min_data['sim_cum_confirmed_deduction_s8'][str(item)],
max_data['sim_cum_confirmed_deduction_s8'][str(item)],
data['real_cum_confirmed'][str(item)],
'Prediction of the cumulative number of confirmed cases',
'img/{}/cum_confirmed_prediction_shade.{}'.format(str(item), fmt),
touchratio=data['x'][str(item)][1],
ratio_low=data['touch_ratio_low'][str(item)],
ratio_high=data['touch_ratio_hight'][str(item)],
date_it=get_important_date(item),
start_date_it=date(2020, 2, 15),
)
plot3_shade(data['sim_confirmed_deduction_s1'][str(item)],
min_data['sim_confirmed_deduction_s1'][str(item)],
max_data['sim_confirmed_deduction_s1'][str(item)],
data['sim_confirmed_deduction_s2'][str(item)],
min_data['sim_confirmed_deduction_s2'][str(item)],
max_data['sim_confirmed_deduction_s2'][str(item)],
data['sim_confirmed_deduction_s3'][str(item)],
min_data['sim_confirmed_deduction_s3'][str(item)],
max_data['sim_confirmed_deduction_s3'][str(item)],
data['sim_confirmed_deduction_s8'][str(item)],
min_data['sim_confirmed_deduction_s8'][str(item)],
max_data['sim_confirmed_deduction_s8'][str(item)],
data['real_confirmed'][str(item)],
'Prediction of the cumulative number of confirmed cases',
'img/{}/confirmed_prediction_shade.{}'.format(
str(item), fmt),
touchratio=data['x'][str(item)][1],
ratio_low=data['touch_ratio_low'][str(item)],
ratio_high=data['touch_ratio_hight'][str(item)],
date_it=get_important_date(item),
it_max=it_max,
it_min=it_min,
start_date_it=date(2020, 2, 15),
loc='upper right')
if item == 900003:
plot3_shade(
data['sim_confirmed_deduction_s1'][str(item)],
min_data['sim_confirmed_deduction_s1'][str(item)],
max_data['sim_confirmed_deduction_s1'][str(item)],
data['sim_confirmed_deduction_s2'][str(item)],
min_data['sim_confirmed_deduction_s2'][str(item)],
max_data['sim_confirmed_deduction_s2'][str(item)],
data['sim_confirmed_deduction_s3'][str(item)],
min_data['sim_confirmed_deduction_s3'][str(item)],
max_data['sim_confirmed_deduction_s3'][str(item)],
data['sim_confirmed_deduction_s8'][str(item)],
min_data['sim_confirmed_deduction_s8'][str(item)],
max_data['sim_confirmed_deduction_s8'][str(item)],
data['real_confirmed'][str(item)],
'Prediction of the cumulative number of confirmed cases',
'img/{}/confirmed_prediction_shade.{}'.format(str(item), fmt),
touchratio=data['x'][str(item)][1],
ratio_low=data['touch_ratio_low'][str(item)],
ratio_high=data['touch_ratio_hight'][str(item)],
date_it=get_important_date(item),
it_max=it_max,
it_min=it_min,
start_date_it=date(2020, 2, 1),
loc='upper right')
print('==================================', code_dict[item],
str(final_date))
print('final total infections and confirmed cases')
print('confirmed')
print('middle: ')
print(
'100\%',
format(
int((data['sim_cum_confirmed_deduction_s1'][str(item)][-1])),
','),
'({}-{})\n'.format(
format(
int((min_data['sim_cum_confirmed_deduction_s1'][str(item)]
[-1])), ','),
format(
int((max_data['sim_cum_confirmed_deduction_s1'][str(item)]
[-1])), ',')),
'60\%',
format(
int((data['sim_cum_confirmed_deduction_s8'][str(item)])[-1]),
','),
'({}-{})\n'.format(
format(
int((min_data['sim_cum_confirmed_deduction_s8'][str(item)]
[-1])), ','),
format(
int((max_data['sim_cum_confirmed_deduction_s8'][str(item)]
[-1])), ',')),
'35\%',
format(
int((data['sim_cum_confirmed_deduction_s2'][str(item)])[-1]),
','),
'({}-{})\n'.format(
format(
int((min_data['sim_cum_confirmed_deduction_s2'][str(item)]
[-1])), ','),
format(
int((max_data['sim_cum_confirmed_deduction_s2'][str(item)]
[-1])), ',')),
'10\%',
format(
int((data['sim_cum_confirmed_deduction_s3'][str(item)])[-1]),
','),
'({}-{})\n'.format(
format(
int((min_data['sim_cum_confirmed_deduction_s3'][str(item)]
[-1])), ','),
format(
int((max_data['sim_cum_confirmed_deduction_s3'][str(item)]
[-1])), ',')),
)
print('min: ')
print(
'100\%',
format(
int((min_data['sim_cum_confirmed_deduction_s1'][str(item)][-1]
)), ','),
'60\%',
format(
int((min_data['sim_cum_confirmed_deduction_s8'][str(item)]
)[-1]), ','),
'35\%',
format(
int((min_data['sim_cum_confirmed_deduction_s2'][str(item)]
)[-1]), ','),
'10\%',
format(
int((min_data['sim_cum_confirmed_deduction_s3'][str(item)]
)[-1]), ','),
)
print('max:')
print(
'100\%',
format(
int((max_data['sim_cum_confirmed_deduction_s1'][str(item)][-1]
)), ','),
'60\%',
format(
int((max_data['sim_cum_confirmed_deduction_s8'][str(item)]
)[-1]), ','),
'35\%',
format(
int((max_data['sim_cum_confirmed_deduction_s2'][str(item)]
)[-1]), ','),
'10\%',
format(
int((max_data['sim_cum_confirmed_deduction_s3'][str(item)]
)[-1]), ','),
)
print('infected')
print('middle: ')
print(
'100\%',
format(
int((data['sim_cum_infection_deduction_s1'][str(item)][-1])),
','),
'({}-{})\n'.format(
format(
int((min_data['sim_cum_infection_deduction_s1'][str(item)]
[-1])), ','),
format(
int((max_data['sim_cum_infection_deduction_s1'][str(item)]
[-1])), ',')),
'60\%',
format(
int((data['sim_cum_infection_deduction_s8'][str(item)])[-1]),
','),
'({}-{})\n'.format(
format(
int((min_data['sim_cum_infection_deduction_s8'][str(item)]
[-1])), ','),
format(
int((max_data['sim_cum_infection_deduction_s8'][str(item)]
[-1])), ',')),
'35\%',
format(
int((data['sim_cum_infection_deduction_s2'][str(item)])[-1]),
','),
'({}-{})\n'.format(
format(
int((min_data['sim_cum_infection_deduction_s2'][str(item)]
[-1])), ','),
format(
int((max_data['sim_cum_infection_deduction_s2'][str(item)]
[-1])), ',')),
'10\%',
format(
int((data['sim_cum_infection_deduction_s3'][str(item)])[-1]),
','),
'({}-{})\n'.format(
format(
int((min_data['sim_cum_infection_deduction_s3'][str(item)]
[-1])), ','),
format(
int((max_data['sim_cum_infection_deduction_s3'][str(item)]
[-1])), ',')),
)
print('min: ')
print(
'100\%',
format(
int((min_data['sim_cum_infection_deduction_s1'][str(item)][-1]
)), ','),
'60\%',
format(
int((min_data['sim_cum_infection_deduction_s8'][str(item)]
)[-1]), ','),
'35\%',
format(
int((min_data['sim_cum_infection_deduction_s2'][str(item)]
)[-1]), ','),
'10\%',
format(
int((min_data['sim_cum_infection_deduction_s3'][str(item)]
)[-1]), ','),
)
print('max:')
print(
'100\%',
format(
int((max_data['sim_cum_infection_deduction_s1'][str(item)][-1]
)), ','),
'60\%',
format(
int((max_data['sim_cum_infection_deduction_s8'][str(item)]
)[-1]), ','),
'35\%',
format(
int((max_data['sim_cum_infection_deduction_s2'][str(item)]
)[-1]), ','),
'10\%',
format(
int((max_data['sim_cum_infection_deduction_s3'][str(item)]
)[-1]), ','),
)
print('==================================', code_dict[item],
str(final_date))
"""
plot3(get_total(data, 'sim_cum_confirmed_deduction_s1',total_cities),
get_total(data, 'sim_cum_confirmed_deduction_s2',total_cities),
get_total(data, 'sim_cum_confirmed_deduction_s3',total_cities),
get_total(data, 'real_cum_confirmed',total_cities),
'Prediction of the cumulative number of confirmed cases',
'img/{}/cum_confirmed_prediction.{}'.format('0000', fmt),
touchratio=1,
ratio_low=0.5,
ratio_high=1.5)
plot3(get_total(data, 'sim_confirmed_deduction_s1', total_cities),
get_total(data, 'sim_confirmed_deduction_s2', total_cities),
get_total(data, 'sim_confirmed_deduction_s3', total_cities),
get_total(data, 'real_confirmed', total_cities),
'Prediction of the cumulative number of confirmed cases',
'img/{}/confirmed_prediction.{}'.format('0000', fmt),
touchratio=1,
ratio_low=0.5,
ratio_high=1.5)
"""
print('drawing different deduction lately')
for item in important_cities:
print(item)
it_max = None
#if item == 900004:
# it_max = 40000
#if item == 900005:
# it_max = 200000
#if item == 900007:
# it_max = 80000
#if item == 900008:
# it_max = 60000
plot3(data['sim_cum_confirmed_deduction_s1'][str(item)],
data['sim_cum_confirmed_deduction_s5'][str(item)],
data['sim_cum_confirmed_deduction_s4'][str(item)],
data['real_cum_confirmed'][str(item)],
'Prediction of the cumulative number of confirmed cases',
'img/{}/cum_confirmed_prediction_simdate.{}'.format(
str(item), fmt),
touchratio=data['x'][str(item)][1],
ratio_low=data['touch_ratio_low'][str(item)],
ratio_high=data['touch_ratio_hight'][str(item)],
date_it=get_important_date(item) + timedelta(15),
start_date_it=date(2020, 2, 15))
plot3(data['sim_confirmed_deduction_s1'][str(item)],
data['sim_confirmed_deduction_s5'][str(item)],
data['sim_confirmed_deduction_s4'][str(item)],
data['real_confirmed'][str(item)],
'Prediction of the cumulative number of confirmed cases',
'img/{}/confirmed_prediction_simdate.{}'.format(str(item), fmt),
touchratio=data['x'][str(item)][1],
ratio_low=data['touch_ratio_low'][str(item)],
ratio_high=data['touch_ratio_hight'][str(item)],
date_it=get_important_date(item) + timedelta(15),
it_max=it_max,
it_min=0,
start_date_it=date(2020, 2, 15))
"""
plot3(get_total(data, 'sim_cum_confirmed_deduction_s1', total_cities),
get_total(data, 'sim_cum_confirmed_deduction_s5', total_cities),
get_total(data, 'sim_cum_confirmed_deduction_s4', total_cities),
get_total(data, 'real_cum_confirmed', total_cities),
'Prediction of the cumulative number of confirmed cases',
'img/{}/cum_confirmed_prediction_simdate.{}'.format('0000', fmt),
touchratio=1,
ratio_low=0.5,
ratio_high=1.5)
plot3(get_total(data, 'sim_confirmed_deduction_s1', total_cities),
get_total(data, 'sim_confirmed_deduction_s5', total_cities),
get_total(data, 'sim_confirmed_deduction_s4', total_cities),
get_total(data, 'real_confirmed', total_cities),
'Prediction of the cumulative number of confirmed cases',
'img/{}/confirmed_prediction_simdate.{}'.format('0000', fmt),
touchratio=1,
ratio_low=0.5,
ratio_high=1.5)
"""
print('output loss')
loss_dict = {}
for item in total_cities:
loss_dict[item] = data['loss'][str(item)]
loss_df = pd.DataFrame(loss_dict, index=[0])
loss_df.to_csv('./loss.csv', index=False)
print('======== cur')
for item in total_cities:
print('{} &'.format(code_dict[item]), sep='', end='')
print('{} &'.format(
format(int(data['real_cum_confirmed'][str(item)][-1]), ',')),
sep='',
end='')
print('{} ({}-{}) &'.format(
format(
int(data['sim_cum_confirmed_deduction_s1'][str(item)][lreal -
1]),
','),
format(
int(min_data['sim_cum_confirmed_deduction_s1'][str(item)][lreal
-
1]),
','),
format(
int(max_data['sim_cum_confirmed_deduction_s1'][str(item)][lreal
-
1]),
','),
),
sep='',
end='')
print('{} ({}-{}) &'.format(
format(
int(data['sim_cum_infection_deduction_s1'][str(item)][lreal -
1]),
','),
format(
int(min_data['sim_cum_infection_deduction_s1'][str(item)][lreal
-
1]),
','),
format(
int(max_data['sim_cum_infection_deduction_s1'][str(item)][lreal
-
1]),
','),
),
sep='',
end='')
print('{} ({}-{}) &'.format(
format(
int(data['sim_total_infection_deduction_s1'][str(item)][lreal -
1]),
','),
format(
int(min_data['sim_total_infection_deduction_s1'][str(item)][
lreal - 1]), ','),
format(
int(max_data['sim_total_infection_deduction_s1'][str(item)][
lreal - 1]), ','),
),
sep='',
end='')
print('{} ({}-{}) \\\\'.format(
format(
int(data['sim_cum_self_cured_deduction_s1'][str(item)][lreal -
1]),
','),
format(
int(min_data['sim_cum_self_cured_deduction_s1'][str(item)][
lreal - 1]), ','),
format(
int(max_data['sim_cum_self_cured_deduction_s1'][str(item)][
lreal - 1]), ','),
),
sep='',
end='')
print('')
print('=======')
# print variables
x_list = construct_x(data, total_cities)
min_x_list = construct_x(min_data, total_cities)
max_x_list = construct_x(max_data, total_cities)
format_out(x_list, min_x_list, max_x_list)
def main():
data = {}
code_dict = it_code_dict()
fmt = 'pdf'
total_cities = pd.read_csv(get_data_path())['adcode'].unique()
#important_cities = [420000,110000,120000,320000,330000,440000,510000]
important_cities = total_cities
with open('./data_run_middle.json', 'r') as f:
data = json.load(f)
with open('./data_run_xmin.json', 'r') as f:
min_data = json.load(f)
with open('./data_run_xmax.json', 'r') as f:
max_data = json.load(f)
if not os.path.exists('./img'):
os.mkdir('./img')
if not os.path.exists('./img/0000'):
os.makedirs('./img/0000')
for item in important_cities:
it_path = './img/{}'.format(str(item))
if not os.path.exists(it_path):
os.mkdir(it_path)
# test
# end test
cum_dead_res = [get_total(data, 'real_cum_dead', total_cities)[-1],
get_total(data, 'sim_cum_dead_s1', total_cities)[-1],
get_total(data, 'sim_cum_dead_s2', total_cities)[-1],
get_total(data, 'sim_cum_dead_s3', total_cities)[-1],
get_total(data, 'sim_cum_dead_s4', total_cities)[-1],
get_total(data, 'sim_cum_dead_s5', total_cities)[-1],
get_total(data, 'sim_cum_dead_s6', total_cities)[-1],
get_total(data, 'sim_cum_dead_s7', total_cities)[-1],
get_total(data, 'sim_cum_dead_s8', total_cities)[-1],
]
d_cum =get_total(data, 'sim_cum_dead_s1', total_cities)[-60:]
print(d_cum[0], d_cum[-1], d_cum[-1]-d_cum[0])
for i in range(2, len(cum_dead_res)):
cum_dead_res[i] = cum_dead_res[i] + cum_dead_res[0] - cum_dead_res[1] + (d_cum[-1]-d_cum[0])
cum_dead_res[1] = cum_dead_res[1] + cum_dead_res[0] - cum_dead_res[1] + (d_cum[-1]-d_cum[0])
print('cum dead res: ',cum_dead_res)
cum_confirm_res = [get_total(data, 'real_cum_confirmed', total_cities)[-1],
get_total(data, 'sim_cum_confirmed_deduction_s1', total_cities)[-1],
get_total(data, 'sim_cum_confirmed_deduction_s2', total_cities)[-1],
get_total(data, 'sim_cum_confirmed_deduction_s3', total_cities)[-1],
get_total(data, 'sim_cum_confirmed_deduction_s4', total_cities)[-1],
get_total(data, 'sim_cum_confirmed_deduction_s5', total_cities)[-1],
get_total(data, 'sim_cum_confirmed_deduction_s6', total_cities)[-1],
get_total(data, 'sim_cum_confirmed_deduction_s7', total_cities)[-1],
get_total(data, 'sim_cum_confirmed_deduction_s8', total_cities)[-1],
]
print('cum confirm case: ', cum_confirm_res)
cum_res = [cum_confirm_res[1],
cum_confirm_res[8],
cum_confirm_res[3],
cum_confirm_res[2],
cum_confirm_res[1],
cum_confirm_res[4],
cum_confirm_res[5],
cum_confirm_res[6],
cum_dead_res[1],
cum_dead_res[8],
cum_dead_res[3],
cum_dead_res[2],
cum_dead_res[1],
cum_dead_res[4],
cum_dead_res[5],
cum_dead_res[6]]
for item in cum_res:
print(int(item), ' & ', end='')
print('\\\\')
cum_infection_res = [np.sum(get_total(data, 'sim_cum_infection_deduction_s1', total_cities)[-1]),
np.sum(get_total(data, 'sim_cum_infection_deduction_s2', total_cities)[-1]),
np.sum(get_total(data, 'sim_cum_infection_deduction_s3', total_cities)[-1]),
np.sum(get_total(data, 'sim_cum_infection_deduction_s4', total_cities)[-1]),
np.sum(get_total(data, 'sim_cum_infection_deduction_s5', total_cities)[-1]),
np.sum(get_total(data, 'sim_cum_infection_deduction_s6', total_cities)[-1]),
np.sum(get_total(data, 'sim_cum_infection_deduction_s7', total_cities)[-1]),
]
print(cum_infection_res)
lreal = len(get_total(data, 'real_cum_confirmed', total_cities))
cum_infection_res = [np.sum(get_total(data, 'sim_cum_infection_deduction_s1', total_cities)[lreal-1]),
np.sum(get_total(data, 'sim_cum_infection_deduction_s2', total_cities)[lreal-1]),
np.sum(get_total(data, 'sim_cum_infection_deduction_s3', total_cities)[lreal-1]),
np.sum(get_total(data, 'sim_cum_infection_deduction_s4', total_cities)[lreal-1]),
np.sum(get_total(data, 'sim_cum_infection_deduction_s5', total_cities)[lreal-1]),
np.sum(get_total(data, 'sim_cum_infection_deduction_s6', total_cities)[lreal-1]),
np.sum(get_total(data, 'sim_cum_infection_deduction_s7', total_cities)[lreal-1]),
]
print(cum_infection_res)
print(get_total(data, 'sim_cum_infection_deduction_s1', total_cities)[lreal-1],
get_total(data, 'sim_cum_confirmed', total_cities)[lreal - 1],
get_total(data, 'real_cum_confirmed', total_cities)[lreal - 1],
)
print(get_total(data, 'sim_cum_infection_deduction_s1', total_cities)[- 1],
get_total(data, 'sim_cum_confirmed_deduction_s1', total_cities)[- 1],
get_total(data, 'real_cum_confirmed', total_cities)[- 1],
)
print("final day: ", date(2020,1,11) + timedelta(days=len(get_total(data, 'sim_cum_confirmed_deduction_s1', total_cities))))
final_date = date(2020,1,11) + timedelta(days=len(get_total(data, 'sim_cum_confirmed_deduction_s1', total_cities)))
cur_date = date(2020,1, 11) + timedelta(days=len(get_total(data, 'real_confirmed', total_cities)))
# draw compare to real data for every city and full
print('drawing real/sim compare')
print('city', 'real_confirmed', 'sim_confirmed', 'sim_infect', 'sim_confirmed / sim_infect')
print('Province&real confirmed&MLSim confirmed&MLSim infected& proportion of asymptomatic\\\\\\hline')
for item in total_cities:
real_cum = data['real_cum_confirmed'][str(item)][-1]
sim_cum_confirm =data['sim_cum_confirmed'][str(item)][-1]
sim_cum_infect = data['sim_cum_infection_deduction_s1'][str(item)][lreal-1]
#print(code_dict[int(item)],
# real_cum,
# sim_cum_confirm,
# sim_cum_infect,
# #np.round(real_cum / sim_cum_infect, 3),
# np.round(sim_cum_confirm / sim_cum_infect, 3),sep='\t\t')
print(code_dict[int(item)], ' & ',
real_cum, ' & ',
int(sim_cum_confirm), ' & ',
int(sim_cum_infect), ' & ',
np.round((1-sim_cum_confirm / sim_cum_infect)*100, 3), '$\\%$\\\\ ',sep='')
print('China\'s mainland', ' & ',
int(get_total(data, 'real_cum_confirmed', total_cities)[-1]), ' & ',
int(get_total(data, 'sim_cum_confirmed', total_cities)[-1]), ' & ',
int(get_total(data, 'sim_cum_infection_deduction_s1', total_cities)[lreal-1]), ' & ',
np.round((1 - (get_total(data, 'sim_cum_confirmed', total_cities)[-1]) / (get_total(data, 'sim_cum_infection_deduction_s1', total_cities)[lreal-1])) * 100, 3), '$\\%$\\\\ ', sep='')
diff_time_confirm = [
int(get_total(data,'sim_cum_confirmed_deduction_s1',total_cities)[-1]),
int(get_total(data, 'sim_cum_confirmed_deduction_s9', total_cities)[-1]),
int(get_total(data, 'sim_cum_confirmed_deduction_s10', total_cities)[-1]),
int(get_total(data, 'sim_cum_confirmed_deduction_s11', total_cities)[-1]),
int(get_total(data, 'sim_cum_confirmed_deduction_s12', total_cities)[-1]),
int(get_total(data, 'sim_cum_infection_deduction_s1', total_cities)[-1]),
int(get_total(data, 'sim_cum_infection_deduction_s9', total_cities)[-1]),
int(get_total(data, 'sim_cum_infection_deduction_s10', total_cities)[-1]),
int(get_total(data, 'sim_cum_infection_deduction_s11', total_cities)[-1]),
int(get_total(data, 'sim_cum_infection_deduction_s12', total_cities)[-1]),
]
print('diff confirm: ', diff_time_confirm)
for item in diff_time_confirm:
print(int(item), ' & ', end='')
print('\\\\')
for item in total_cities:
plot1_shade(data['real_cum_confirmed'][str(item)],
data['sim_cum_confirmed'][str(item)],
min_data['sim_cum_confirmed'][str(item)],
max_data['sim_cum_confirmed'][str(item)],
code_dict[int(item)],
'img/{}/cum_real_sim.{}'.format(str(item), fmt))
plot1_shade(data['real_confirmed'][str(item)],
data['sim_confirmed'][str(item)],
min_data['sim_confirmed'][str(item)],
max_data['sim_confirmed'][str(item)],
code_dict[int(item)],
'img/{}/increase_real_sim.{}'.format(str(item), fmt))
plot1_shade(data['real_cum_confirmed'][str(item)],
data['sim_cum_confirmed_deduction_s1'][str(item)],
min_data['sim_cum_confirmed_deduction_s1'][str(item)],
max_data['sim_cum_confirmed_deduction_s1'][str(item)],
code_dict[int(item)],
'img/{}/cum_real_sim_deduction.{}'.format(str(item), fmt),interval=20)
plot1_shade(data['real_confirmed'][str(item)],
data['sim_confirmed_deduction_s1'][str(item)],
min_data['sim_confirmed_deduction_s1'][str(item)],
max_data['sim_confirmed_deduction_s1'][str(item)],
code_dict[int(item)],
'img/{}/increase_real_sim_dedection.{}'.format(str(item), fmt), interval=20)
plot33_shade(data['sim_cum_confirmed_deduction_s1'][str(item)], min_data['sim_cum_confirmed_deduction_s1'][str(item)], max_data['sim_cum_confirmed_deduction_s1'][str(item)],
data['sim_cum_confirmed_deduction_s2'][str(item)], min_data['sim_cum_confirmed_deduction_s2'][str(item)], max_data['sim_cum_confirmed_deduction_s2'][str(item)],
data['sim_cum_confirmed_deduction_s3'][str(item)], min_data['sim_cum_confirmed_deduction_s3'][str(item)], max_data['sim_cum_confirmed_deduction_s3'][str(item)],
data['sim_cum_confirmed_deduction_s8'][str(item)], min_data['sim_cum_confirmed_deduction_s8'][str(item)], max_data['sim_cum_confirmed_deduction_s8'][str(item)],
data['real_cum_confirmed'][str(item)],
code_dict[int(item)],
'img/{}/cum_confirmed_prediction.{}'.format(str(item), fmt),
touchratio=data['x'][str(item)][1],
ratio_low=data['touch_ratio_low'][str(item)],
ratio_high=data['touch_ratio_hight'][str(item)],
loc='lower right',
date_it=date(2020, 1, 23),
ext_flag=True
)
plot33_shade(data['sim_confirmed_deduction_s1'][str(item)], min_data['sim_confirmed_deduction_s1'][str(item)], max_data['sim_confirmed_deduction_s1'][str(item)],
data['sim_confirmed_deduction_s2'][str(item)], min_data['sim_confirmed_deduction_s2'][str(item)], max_data['sim_confirmed_deduction_s2'][str(item)],
data['sim_confirmed_deduction_s3'][str(item)],min_data['sim_confirmed_deduction_s3'][str(item)], max_data['sim_confirmed_deduction_s3'][str(item)],
data['sim_confirmed_deduction_s8'][str(item)], min_data['sim_confirmed_deduction_s8'][str(item)], max_data['sim_confirmed_deduction_s8'][str(item)],
data['real_confirmed'][str(item)],
code_dict[int(item)],
'img/{}/confirmed_prediction.{}'.format(str(item), fmt),
touchratio=data['x'][str(item)][1],
ratio_low=data['touch_ratio_low'][str(item)],
ratio_high=data['touch_ratio_hight'][str(item)],
loc='upper right',
date_it=date(2020, 1, 23),
ext_flag=True
)
plot33_shade(
get_total(data, 'sim_cum_confirmed_deduction_s1', total_cities), get_total(min_data,'sim_cum_confirmed_deduction_s1',total_cities), get_total(max_data,'sim_cum_confirmed_deduction_s1',total_cities),
get_total(data, 'sim_cum_confirmed_deduction_s2', total_cities), get_total(min_data,'sim_cum_confirmed_deduction_s2',total_cities), get_total(max_data,'sim_cum_confirmed_deduction_s2',total_cities),
get_total(data, 'sim_cum_confirmed_deduction_s3', total_cities), get_total(min_data,'sim_cum_confirmed_deduction_s3',total_cities), get_total(max_data,'sim_cum_confirmed_deduction_s3',total_cities),
get_total(data, 'sim_cum_confirmed_deduction_s8', total_cities), get_total(min_data,'sim_cum_confirmed_deduction_s8',total_cities), get_total(max_data,'sim_cum_confirmed_deduction_s8',total_cities),
get_total(data,'real_cum_confirmed', total_cities),
'China\'s mainland',
'img/{}/cum_confirmed_prediction.{}'.format('0000', fmt),
touchratio=0.1,
ratio_low=0.2,
ratio_high=0.4,
loc='lower right',
date_it=date(2020, 1, 23),
ext_flag=True
)
plot33_shade(
get_total(data, 'sim_confirmed_deduction_s1', total_cities), get_total(min_data,'sim_confirmed_deduction_s1',total_cities), get_total(max_data,'sim_confirmed_deduction_s1',total_cities),
get_total(data, 'sim_confirmed_deduction_s2', total_cities), get_total(min_data,'sim_confirmed_deduction_s2',total_cities), get_total(max_data,'sim_confirmed_deduction_s2',total_cities),
get_total(data, 'sim_confirmed_deduction_s3', total_cities), get_total(min_data,'sim_confirmed_deduction_s3',total_cities), get_total(max_data,'sim_confirmed_deduction_s3',total_cities),
get_total(data, 'sim_confirmed_deduction_s8', total_cities), get_total(min_data,'sim_confirmed_deduction_s8',total_cities), get_total(max_data,'sim_confirmed_deduction_s8',total_cities),
get_total(data,'real_confirmed', total_cities),
'China\'s mainland',
'img/{}/confirmed_prediction.{}'.format('0000', fmt),
touchratio=0.1,
ratio_low=0.2,
ratio_high=0.3,
loc='upper right',
date_it=date(2020, 1, 23),
ext_flag=True
)
print('==================================', str(final_date))
print('Jan 23 different control measure: ')
print('confirmed')
print('middle: ')
print('100\%', format(int(get_total(data, 'sim_cum_confirmed_deduction_s1', total_cities)[-1]), ','),
'190\%', format(int(get_total(data, 'sim_cum_confirmed_deduction_s2', total_cities)[-1]), ','),
'160\%', format(int(get_total(data, 'sim_cum_confirmed_deduction_s3', total_cities)[-1]), ','),
'130\%', format(int(get_total(data, 'sim_cum_confirmed_deduction_s8', total_cities)[-1]), ','), )
print('min: ')
print('100\%', format(int(get_total(min_data, 'sim_cum_confirmed_deduction_s1', total_cities)[-1]), ','),
'190\%', format(int(get_total(min_data, 'sim_cum_confirmed_deduction_s2', total_cities)[-1]), ','),
'160\%', format(int(get_total(min_data, 'sim_cum_confirmed_deduction_s3', total_cities)[-1]), ','),
'130\%', format(int(get_total(min_data, 'sim_cum_confirmed_deduction_s8', total_cities)[-1]), ','), )
print('max:')
print('100\%', format(int(get_total(max_data, 'sim_cum_confirmed_deduction_s1', total_cities)[-1]), ','),
'190\%', format(int(get_total(max_data, 'sim_cum_confirmed_deduction_s2', total_cities)[-1]), ','),
'160\%', format(int(get_total(max_data, 'sim_cum_confirmed_deduction_s3', total_cities)[-1]), ','),
'130\%', format(int(get_total(max_data, 'sim_cum_confirmed_deduction_s8', total_cities)[-1]), ','), )
print('infected')
print('middle: ')
print('100\%', format(int(get_total(data, 'sim_cum_infection_deduction_s1', total_cities)[-1]), ','),
'190\%', format(int(get_total(data, 'sim_cum_infection_deduction_s2', total_cities)[-1]), ','),
'160\%', format(int(get_total(data, 'sim_cum_infection_deduction_s3', total_cities)[-1]), ','),
'130\%', format(int(get_total(data, 'sim_cum_infection_deduction_s8', total_cities)[-1]), ','), )
print('min: ')
print('100\%', format(int(get_total(min_data, 'sim_cum_infection_deduction_s1', total_cities)[-1]), ','),
'190\%', format(int(get_total(min_data, 'sim_cum_infection_deduction_s2', total_cities)[-1]), ','),
'160\%', format(int(get_total(min_data, 'sim_cum_infection_deduction_s3', total_cities)[-1]), ','),
'130\%', format(int(get_total(min_data, 'sim_cum_infection_deduction_s8', total_cities)[-1]), ','), )
print('max:')
print('100\%', format(int(get_total(max_data, 'sim_cum_infection_deduction_s1', total_cities)[-1]), ','),
'190\%', format(int(get_total(max_data, 'sim_cum_infection_deduction_s2', total_cities)[-1]), ','),
'160\%', format(int(get_total(max_data, 'sim_cum_infection_deduction_s3', total_cities)[-1]), ','),
'130\%', format(int(get_total(max_data, 'sim_cum_infection_deduction_s8', total_cities)[-1]), ','), )
print('==================================', str(final_date))
plot33_shade(
get_total(data, 'sim_cum_confirmed_deduction_s1', total_cities),
get_total(min_data, 'sim_cum_confirmed_deduction_s1', total_cities),
get_total(max_data, 'sim_cum_confirmed_deduction_s1', total_cities),
get_total(data, 'sim_cum_confirmed_deduction_s5', total_cities),
get_total(min_data, 'sim_cum_confirmed_deduction_s5', total_cities),
get_total(max_data, 'sim_cum_confirmed_deduction_s5', total_cities),
get_total(data, 'sim_cum_confirmed_deduction_s4', total_cities),
get_total(min_data, 'sim_cum_confirmed_deduction_s4', total_cities),
get_total(max_data, 'sim_cum_confirmed_deduction_s4', total_cities),
get_total(data, 'sim_cum_confirmed_deduction_s6', total_cities),
get_total(min_data, 'sim_cum_confirmed_deduction_s6', total_cities),
get_total(max_data, 'sim_cum_confirmed_deduction_s6', total_cities),
get_total(data, 'real_cum_confirmed', total_cities),
'China\'s mainland',
'img/{}/cum_confirmed_prediction_simdate.{}'.format('0000', fmt),
touchratio=0.1,
ratio_low=0.2,
ratio_high=0.4,
loc='lower right',
date_it=date(2020, 3, 1),
)
plot33_shade(
get_total(data, 'sim_confirmed_deduction_s1', total_cities),
get_total(min_data, 'sim_confirmed_deduction_s1', total_cities),
get_total(max_data, 'sim_confirmed_deduction_s1', total_cities),
get_total(data, 'sim_confirmed_deduction_s5', total_cities),
get_total(min_data, 'sim_confirmed_deduction_s5', total_cities),
get_total(max_data, 'sim_confirmed_deduction_s5', total_cities),
get_total(data, 'sim_confirmed_deduction_s4', total_cities),
get_total(min_data, 'sim_confirmed_deduction_s4', total_cities),
get_total(max_data, 'sim_confirmed_deduction_s4', total_cities),
get_total(data, 'sim_confirmed_deduction_s6', total_cities),
get_total(min_data, 'sim_confirmed_deduction_s6', total_cities),
get_total(max_data, 'sim_confirmed_deduction_s6', total_cities),
get_total(data, 'real_confirmed', total_cities),
'China\'s mainland',
'img/{}/confirmed_prediction_simdate.{}'.format('0000', fmt),
touchratio=0.1,
ratio_low=0.2,
ratio_high=0.3,
loc='upper right',
date_it=date(2020, 3, 1),
)
for item in important_cities:
print(item)
plot33_shade(data['sim_cum_confirmed_deduction_s1'][str(item)],min_data['sim_cum_confirmed_deduction_s1'][str(item)],max_data['sim_cum_confirmed_deduction_s1'][str(item)],
data['sim_cum_confirmed_deduction_s2'][str(item)],min_data['sim_cum_confirmed_deduction_s2'][str(item)],max_data['sim_cum_confirmed_deduction_s2'][str(item)],
data['sim_cum_confirmed_deduction_s3'][str(item)],min_data['sim_cum_confirmed_deduction_s3'][str(item)],max_data['sim_cum_confirmed_deduction_s3'][str(item)],
data['sim_cum_confirmed_deduction_s8'][str(item)],min_data['sim_cum_confirmed_deduction_s8'][str(item)],max_data['sim_cum_confirmed_deduction_s8'][str(item)],
data['real_cum_confirmed'][str(item)],
code_dict[int(item)],
'img/{}/cum_confirmed_prediction.{}'.format(str(item), fmt),
touchratio=data['x'][str(item)][1],
ratio_low=data['touch_ratio_low'][str(item)],
ratio_high=data['touch_ratio_hight'][str(item)],
loc='lower right',
date_it=date(2020,1,23),
ext_flag=True
)
plot33_shade(data['sim_confirmed_deduction_s1'][str(item)],min_data['sim_confirmed_deduction_s1'][str(item)],max_data['sim_confirmed_deduction_s1'][str(item)],
data['sim_confirmed_deduction_s2'][str(item)],min_data['sim_confirmed_deduction_s2'][str(item)],max_data['sim_confirmed_deduction_s2'][str(item)],
data['sim_confirmed_deduction_s3'][str(item)],min_data['sim_confirmed_deduction_s3'][str(item)],max_data['sim_confirmed_deduction_s3'][str(item)],
data['sim_confirmed_deduction_s8'][str(item)],min_data['sim_confirmed_deduction_s8'][str(item)],max_data['sim_confirmed_deduction_s8'][str(item)],
data['real_confirmed'][str(item)],
code_dict[int(item)],
'img/{}/confirmed_prediction.{}'.format(str(item), fmt),
touchratio=data['x'][str(item)][1],
ratio_low=data['touch_ratio_low'][str(item)],
ratio_high=data['touch_ratio_hight'][str(item)],
loc='upper right',
date_it=date(2020,1,23),
ext_flag=True
)
plot33_shade(data['sim_cum_confirmed_deduction_s1'][str(item)],
min_data['sim_cum_confirmed_deduction_s1'][str(item)],
max_data['sim_cum_confirmed_deduction_s1'][str(item)],
data['sim_cum_confirmed_deduction_s5'][str(item)],
min_data['sim_cum_confirmed_deduction_s5'][str(item)],
max_data['sim_cum_confirmed_deduction_s5'][str(item)],
data['sim_cum_confirmed_deduction_s4'][str(item)],
min_data['sim_cum_confirmed_deduction_s4'][str(item)],
max_data['sim_cum_confirmed_deduction_s4'][str(item)],
data['sim_cum_confirmed_deduction_s6'][str(item)],
min_data['sim_cum_confirmed_deduction_s6'][str(item)],
max_data['sim_cum_confirmed_deduction_s6'][str(item)],
data['real_cum_confirmed'][str(item)],
code_dict[int(item)],
'img/{}/cum_confirmed_prediction_simdate.{}'.format(str(item), fmt),
touchratio=data['x'][str(item)][1],
ratio_low=data['touch_ratio_low'][str(item)],
ratio_high=data['touch_ratio_hight'][str(item)],
loc='lower right',
date_it=date(2020, 1, 23),
#ext_flag=True
)
plot33_shade(data['sim_confirmed_deduction_s1'][str(item)], min_data['sim_confirmed_deduction_s1'][str(item)],
max_data['sim_confirmed_deduction_s1'][str(item)],
data['sim_confirmed_deduction_s5'][str(item)], min_data['sim_confirmed_deduction_s5'][str(item)],
max_data['sim_confirmed_deduction_s5'][str(item)],
data['sim_confirmed_deduction_s4'][str(item)], min_data['sim_confirmed_deduction_s4'][str(item)],
max_data['sim_confirmed_deduction_s4'][str(item)],
data['sim_confirmed_deduction_s6'][str(item)], min_data['sim_confirmed_deduction_s4'][str(item)],
max_data['sim_confirmed_deduction_s6'][str(item)],
data['real_confirmed'][str(item)],
code_dict[int(item)],
'img/{}/confirmed_prediction_simdate.{}'.format(str(item), fmt),
touchratio=data['x'][str(item)][1],
ratio_low=data['touch_ratio_low'][str(item)],
ratio_high=data['touch_ratio_hight'][str(item)],
loc='upper right',
date_it=date(2020, 1, 23),
#ext_flag=True
)
plot33_time_shade(
data['sim_confirmed_deduction_s1'][str(item)],min_data['sim_confirmed_deduction_s1'][str(item)],max_data['sim_confirmed_deduction_s1'][str(item)],
data['sim_confirmed_deduction_s9'][str(item)],min_data['sim_confirmed_deduction_s9'][str(item)],max_data['sim_confirmed_deduction_s9'][str(item)],
data['sim_confirmed_deduction_s10'][str(item)],min_data['sim_confirmed_deduction_s10'][str(item)],max_data['sim_confirmed_deduction_s10'][str(item)],
data['sim_confirmed_deduction_s11'][str(item)],min_data['sim_confirmed_deduction_s11'][str(item)],max_data['sim_confirmed_deduction_s11'][str(item)],
data['sim_confirmed_deduction_s12'][str(item)],min_data['sim_confirmed_deduction_s12'][str(item)],max_data['sim_confirmed_deduction_s12'][str(item)],
data['real_confirmed'][str(item)],
code_dict[item],
'img/{}/confirmed_prediction_simdate_diff.{}'.format(str(item), fmt),
touchratio=1,
ratio_high=1.5,
ratio_low=0.5,
loc='upper right')
plot33_time_shade(
data['sim_cum_confirmed_deduction_s1'][str(item)], min_data['sim_cum_confirmed_deduction_s1'][str(item)],
max_data['sim_cum_confirmed_deduction_s1'][str(item)],
data['sim_cum_confirmed_deduction_s9'][str(item)], min_data['sim_cum_confirmed_deduction_s9'][str(item)],
max_data['sim_cum_confirmed_deduction_s9'][str(item)],
data['sim_cum_confirmed_deduction_s10'][str(item)], min_data['sim_cum_confirmed_deduction_s10'][str(item)],
max_data['sim_cum_confirmed_deduction_s10'][str(item)],
data['sim_cum_confirmed_deduction_s11'][str(item)], min_data['sim_cum_confirmed_deduction_s11'][str(item)],
max_data['sim_cum_confirmed_deduction_s11'][str(item)],
data['sim_cum_confirmed_deduction_s12'][str(item)], min_data['sim_cum_confirmed_deduction_s12'][str(item)],
max_data['sim_cum_confirmed_deduction_s12'][str(item)],
data['real_cum_confirmed'][str(item)],
code_dict[item],
'img/{}/cum_confirmed_prediction_simdate_diff.{}'.format(str(item), fmt),
touchratio=1,
ratio_high=1.5,
ratio_low=0.5,
loc='upper right')
print('==================================', str(final_date))
print('March 1 different control measure: ')
print('confirmed')
print('middle: ')
print('100\%', format(int(get_total(data, 'sim_cum_confirmed_deduction_s1', total_cities)[-1]), ','),
'190\%', format(int(get_total(data, 'sim_cum_confirmed_deduction_s5', total_cities)[-1]), ','),
'160\%', format(int(get_total(data, 'sim_cum_confirmed_deduction_s4', total_cities)[-1]), ','),
'210\%', format(int(get_total(data, 'sim_cum_confirmed_deduction_s6', total_cities)[-1]), ','), )
print('min: ')
print('100\%', format(int(get_total(min_data, 'sim_cum_confirmed_deduction_s1', total_cities)[-1]), ','),
'190\%', format(int(get_total(min_data, 'sim_cum_confirmed_deduction_s5', total_cities)[-1]), ','),
'160\%', format(int(get_total(min_data, 'sim_cum_confirmed_deduction_s4', total_cities)[-1]), ','),
'210\%', format(int(get_total(min_data, 'sim_cum_confirmed_deduction_s6', total_cities)[-1]), ','), )
print('max:')
print('100\%', format(int(get_total(max_data, 'sim_cum_confirmed_deduction_s1', total_cities)[-1]), ','),
'190\%', format(int(get_total(max_data, 'sim_cum_confirmed_deduction_s5', total_cities)[-1]), ','),
'160\%', format(int(get_total(max_data, 'sim_cum_confirmed_deduction_s4', total_cities)[-1]), ','),
'210\%', format(int(get_total(max_data, 'sim_cum_confirmed_deduction_s6', total_cities)[-1]), ','), )
print('infected')
print('middle: ')
print('100\%', format(int(get_total(data, 'sim_cum_infection_deduction_s1', total_cities)[-1]), ','),
'190\%', format(int(get_total(data, 'sim_cum_infection_deduction_s5', total_cities)[-1]), ','),
'160\%', format(int(get_total(data, 'sim_cum_infection_deduction_s4', total_cities)[-1]), ','),
'210\%', format(int(get_total(data, 'sim_cum_infection_deduction_s6', total_cities)[-1]), ','), )
print('min: ')
print('100\%', format(int(get_total(min_data, 'sim_cum_infection_deduction_s1', total_cities)[-1]), ','),
'190\%', format(int(get_total(min_data, 'sim_cum_infection_deduction_s5', total_cities)[-1]), ','),
'160\%', format(int(get_total(min_data, 'sim_cum_infection_deduction_s4', total_cities)[-1]), ','),
'210\%', format(int(get_total(min_data, 'sim_cum_infection_deduction_s6', total_cities)[-1]), ','), )
print('max:')
print('100\%', format(int(get_total(max_data, 'sim_cum_infection_deduction_s1', total_cities)[-1]), ','),
'190\%', format(int(get_total(max_data, 'sim_cum_infection_deduction_s5', total_cities)[-1]), ','),
'160\%', format(int(get_total(max_data, 'sim_cum_infection_deduction_s4', total_cities)[-1]), ','),
'210\%', format(int(get_total(max_data, 'sim_cum_infection_deduction_s6', total_cities)[-1]), ','), )
print('==================================', str(final_date))
plot1_shade(get_total(data, 'real_cum_confirmed', total_cities),
get_total(data, 'sim_cum_confirmed', total_cities),
get_total(min_data, 'sim_cum_confirmed', total_cities),
get_total(max_data, 'sim_cum_confirmed', total_cities),
'China\'s mainland',
'img/{}/cum_real_sim.{}'.format('0000', fmt))
plot1_shade(get_total(data, 'real_confirmed', total_cities),
get_total(data, 'sim_confirmed', total_cities),
get_total(min_data, 'sim_confirmed', total_cities),
get_total(max_data, 'sim_confirmed', total_cities),
'China\'s mainland',
'img/{}/increase_real_sim.{}'.format('0000', fmt))
plot1_shade(get_total(data, 'real_cum_dead', total_cities),
get_total(data, 'sim_cum_dead_s1', total_cities),
get_total(min_data, 'sim_cum_dead_s1', total_cities),
get_total(max_data, 'sim_cum_dead_s1', total_cities),
'China\'s mainland',
'img/{}/cum_real_sim_dead.{}'.format('0000', fmt),
interval=20)
plot33_time_shade(
get_total(data, 'sim_cum_confirmed_deduction_s1', total_cities),
get_total(min_data, 'sim_cum_confirmed_deduction_s1', total_cities),
get_total(max_data, 'sim_cum_confirmed_deduction_s1', total_cities),
get_total(data, 'sim_cum_confirmed_deduction_s9', total_cities),
get_total(min_data, 'sim_cum_confirmed_deduction_s9', total_cities),
get_total(max_data, 'sim_cum_confirmed_deduction_s9', total_cities),
get_total(data, 'sim_cum_confirmed_deduction_s10', total_cities),
get_total(min_data, 'sim_cum_confirmed_deduction_s10', total_cities),
get_total(max_data, 'sim_cum_confirmed_deduction_s10', total_cities),
get_total(data, 'sim_cum_confirmed_deduction_s11', total_cities),
get_total(min_data, 'sim_cum_confirmed_deduction_s11', total_cities),
get_total(max_data, 'sim_cum_confirmed_deduction_s11', total_cities),
get_total(data, 'sim_cum_confirmed_deduction_s12', total_cities),
get_total(min_data, 'sim_cum_confirmed_deduction_s12', total_cities),
get_total(max_data, 'sim_cum_confirmed_deduction_s12', total_cities),
get_total(data, 'real_cum_confirmed', total_cities),
'China\'s mainland',
'img/{}/cum_confirmed_prediction_time.{}'.format('0000', fmt),
touchratio=1,
ratio_high=1.5,
ratio_low=0.5,
loc='upper right'
)
plot33_time_shade(
get_total(data, 'sim_confirmed_deduction_s1', total_cities),
get_total(min_data, 'sim_confirmed_deduction_s1', total_cities),
get_total(max_data, 'sim_confirmed_deduction_s1', total_cities),
get_total(data, 'sim_confirmed_deduction_s9', total_cities),
get_total(min_data, 'sim_confirmed_deduction_s9', total_cities),
get_total(max_data, 'sim_confirmed_deduction_s9', total_cities),
get_total(data, 'sim_confirmed_deduction_s10', total_cities),
get_total(min_data, 'sim_confirmed_deduction_s10', total_cities),
get_total(max_data, 'sim_confirmed_deduction_s10', total_cities),
get_total(data, 'sim_confirmed_deduction_s11', total_cities),
get_total(min_data, 'sim_confirmed_deduction_s11', total_cities),
get_total(max_data, 'sim_confirmed_deduction_s11', total_cities),
get_total(data, 'sim_confirmed_deduction_s12', total_cities),
get_total(min_data, 'sim_confirmed_deduction_s12', total_cities),
get_total(max_data, 'sim_confirmed_deduction_s12', total_cities),
get_total(data, 'real_confirmed', total_cities),
'China\'s mainland',
'img/{}/confirmed_prediction_time.{}'.format('0000', fmt),
touchratio=1,
ratio_high=1.5,
ratio_low=0.5,
loc='upper right'
)
print('============== parameter')
for item in total_cities:
print(code_dict[item])
print('RMSE: {} ({}-{})'.format(data['newly_confirmed_loss'][str(item)],
min_data['newly_confirmed_loss'][str(item)],
max_data['newly_confirmed_loss'][str(item)]))
print('RO1: {} ({}-{})'.format(data['R01'][str(item)],
min_data['R01'][str(item)],
max_data['R01'][str(item)]))
print('RO2: {} ({}-{})'.format(data['R02'][str(item)],
min_data['R02'][str(item)],
max_data['R02'][str(item)]))
print('DT1: {} ({}-{})'.format(data['DT1'][str(item)],
min_data['DT1'][str(item)],
max_data['DT1'][str(item)]))
print('DT2: {} ({}-{})'.format(data['DT2'][str(item)],
min_data['DT2'][str(item)],
max_data['DT2'][str(item)]))
print('Death ratio: {} ({}-{})'.format(data['death_rate'][str(item)],
min_data['death_rate'][str(item)],
max_data['death_rate'][str(item)]))
print('============== parameter')
print('==================================', str(final_date))
print('Jan 23 different control date: ')
print('confirmed')
print('middle: ')
print('Jan 23', format(int(get_total(data, 'sim_cum_confirmed_deduction_s1', total_cities)[-1]), ','),
'Jan 24', format(int(get_total(data, 'sim_cum_confirmed_deduction_s9', total_cities)[-1]), ','),
'Jan 26', format(int(get_total(data, 'sim_cum_confirmed_deduction_s10', total_cities)[-1]), ','),
'Jan 28', format(int(get_total(data, 'sim_cum_confirmed_deduction_s11', total_cities)[-1]), ','),
'Jan 30', format(int(get_total(data, 'sim_cum_confirmed_deduction_s12', total_cities)[-1]), ','), )
print('min: ')
print('Jan 23', format(int(get_total(min_data, 'sim_cum_confirmed_deduction_s1', total_cities)[-1]), ','),
'Jan 24', format(int(get_total(min_data, 'sim_cum_confirmed_deduction_s9', total_cities)[-1]), ','),
'Jan 26', format(int(get_total(min_data, 'sim_cum_confirmed_deduction_s10', total_cities)[-1]), ','),
'Jan 28', format(int(get_total(min_data, 'sim_cum_confirmed_deduction_s11', total_cities)[-1]), ','),
'Jan 30', format(int(get_total(min_data, 'sim_cum_confirmed_deduction_s12', total_cities)[-1]), ','), )
print('max:')
print('Jan 23', format(int(get_total(max_data, 'sim_cum_confirmed_deduction_s1', total_cities)[-1]), ','),
'Jan 24', format(int(get_total(max_data, 'sim_cum_confirmed_deduction_s9', total_cities)[-1]), ','),
'Jan 26', format(int(get_total(max_data, 'sim_cum_confirmed_deduction_s10', total_cities)[-1]), ','),
'Jan 28', format(int(get_total(max_data, 'sim_cum_confirmed_deduction_s11', total_cities)[-1]), ','),
'Jan 30', format(int(get_total(max_data, 'sim_cum_confirmed_deduction_s12', total_cities)[-1]), ','), )
print('infected')
print('middle: ')
print('Jan 23', format(int(get_total(data, 'sim_cum_infection_deduction_s1', total_cities)[-1]), ','),
'Jan 24', format(int(get_total(data, 'sim_cum_infection_deduction_s9', total_cities)[-1]), ','),
'Jan 26', format(int(get_total(data, 'sim_cum_infection_deduction_s10', total_cities)[-1]), ','),
'Jan 28', format(int(get_total(data, 'sim_cum_infection_deduction_s11', total_cities)[-1]), ','),
'Jan 30', format(int(get_total(data, 'sim_cum_infection_deduction_s12', total_cities)[-1]), ','), )
print('min: ')
print('Jan 23', format(int(get_total(min_data, 'sim_cum_infection_deduction_s1', total_cities)[-1]), ','),
'Jan 24', format(int(get_total(min_data, 'sim_cum_infection_deduction_s9', total_cities)[-1]), ','),
'Jan 26', format(int(get_total(min_data, 'sim_cum_infection_deduction_s10', total_cities)[-1]), ','),
'Jan 28', format(int(get_total(min_data, 'sim_cum_infection_deduction_s11', total_cities)[-1]), ','),
'Jan 30', format(int(get_total(min_data, 'sim_cum_infection_deduction_s12', total_cities)[-1]), ','), )
print('max:')
print('Jan 23', format(int(get_total(max_data, 'sim_cum_infection_deduction_s1', total_cities)[-1]), ','),
'Jan 24', format(int(get_total(max_data, 'sim_cum_infection_deduction_s9', total_cities)[-1]), ','),
'Jan 26', format(int(get_total(max_data, 'sim_cum_infection_deduction_s10', total_cities)[-1]), ','),
'Jan 28', format(int(get_total(max_data, 'sim_cum_infection_deduction_s11', total_cities)[-1]), ','),
'Jan 30', format(int(get_total(max_data, 'sim_cum_infection_deduction_s12', total_cities)[-1]), ','), )
print('==================================', str(final_date))
plot12_shade(get_total(data, 'real_confirmed', total_cities),
get_total(data, 'sim_confirmed', total_cities), get_total(min_data, 'sim_confirmed', total_cities), get_total(max_data, 'sim_confirmed', total_cities),
get_total(data, 'sim_new_infection', total_cities), get_total(min_data, 'sim_new_infection', total_cities), get_total(max_data, 'sim_new_infection', total_cities),
'The newly number of confirmed cases ',
'img/{}/increase_real_sim_infect.{}'.format('0000', fmt))
plot12_shade(get_total(data, 'real_cum_confirmed', total_cities),
get_total(data, 'sim_cum_confirmed', total_cities), get_total(min_data, 'sim_cum_confirmed', total_cities), get_total(max_data, 'sim_cum_confirmed', total_cities),
get_total(data, 'sim_cum_infection_deduction_s1', total_cities)[:lreal], get_total(min_data, 'sim_cum_infection_deduction_s1', total_cities)[:lreal],get_total(max_data, 'sim_cum_infection_deduction_s1', total_cities)[:lreal],
'The newly number of confirmed cases ',
'img/{}/cum_real_sim_infect.{}'.format('0000', fmt))
bias = (today - date(2020,3,13)).days - 1
print('==================================', str(cur_date + timedelta(bias)))
print('current total infections and confirmed cases')
print('confirmed')
print('middle: ')
print('100\%', format(int(get_total(data, 'sim_cum_confirmed_deduction_s1', total_cities)[lreal-1 + bias]), ','))
print('min: ')
print('100\%', format(int(get_total(min_data, 'sim_cum_confirmed_deduction_s1', total_cities)[lreal-1 + bias]), ','))
print('max: ')
print('100\%', format(int(get_total(max_data, 'sim_cum_confirmed_deduction_s1', total_cities)[lreal-1+ bias]), ','))
print('real', format(int(get_total(data,'real_cum_confirmed', total_cities)[-1]), ','))
print('infected')
print('middle: ')
print('100\%', format(int(get_total(data, 'sim_cum_infection_deduction_s1', total_cities)[lreal-1+ bias]), ','))
print('min: ')
print('100\%', format(int(get_total(min_data, 'sim_cum_infection_deduction_s1', total_cities)[lreal-1+ bias]), ','))
print('max: ')
print('100\%', format(int(get_total(max_data, 'sim_cum_infection_deduction_s1', total_cities)[lreal-1+ bias]), ','))
print('infe-confi: ')
print('{} ({}-{})'.format(format(int(data['sim_cum_infe_minus_conf_s1']['0000'][lreal-1+bias]),','),
format(int(min_data['sim_cum_infe_minus_conf_s1']['0000'][lreal - 1 + bias]), ','),
format(int(max_data['sim_cum_infe_minus_conf_s1']['0000'][lreal - 1 + bias]), ',')))
print('ratio')
print('middle: ')
print('100\%', format(int(get_total(data, 'sim_cum_infection_deduction_s1', total_cities)[lreal - 1+ bias]), ','))
print('min: ')
print('100\%', format(int(get_total(min_data, 'sim_cum_infection_deduction_s1', total_cities)[lreal - 1+ bias]), ','))
print('max: ')
print('100\%', format(int(get_total(max_data, 'sim_cum_infection_deduction_s1', total_cities)[lreal - 1+ bias]), ','))
print('cum self_cure')
print('100\%', format(int(get_total(data, 'sim_cum_self_cured_deduction_s1', total_cities)[lreal - 1+ bias]), ','),
'({}-{})'.format(format(int(get_total(min_data,'sim_cum_self_cured_deduction_s1', total_cities)[lreal - 1+ bias]), ','),
format(int(get_total(max_data,'sim_cum_self_cured_deduction_s1', total_cities)[lreal - 1+ bias]), ',')))
print('total infection')
print('100\%', format(int(get_total(data, 'sim_total_infection_deduction_s1', total_cities)[lreal - 1+ bias]), ','),
'({}-{})'.format(format(int(get_total(min_data,'sim_total_infection_deduction_s1', total_cities)[lreal - 1+ bias]), ','),
format(int(get_total(max_data,'sim_total_infection_deduction_s1', total_cities)[lreal - 1+ bias]), ',')))
print('nosymbol')
print('100\%', format(int(get_total(data, 'sim_cum_nosymbol_deduction_s1', total_cities)[lreal - 1+ bias]), ','),
'({}-{})'.format(format(int(get_total(min_data,'sim_cum_nosymbol_deduction_s1', total_cities)[lreal - 1+ bias]), ','),
format(int(get_total(max_data,'sim_cum_nosymbol_deduction_s1', total_cities)[lreal - 1+ bias]), ',')))
print('total_iso')
# sim_total_isolation_deduction_s1
print('100\%', format(int(get_total(data, 'sim_total_isolation_deduction_s1', total_cities)[lreal - 1+ bias]), ','),
'({}-{})'.format(format(int(get_total(min_data,'sim_total_isolation_deduction_s1', total_cities)[lreal - 1+ bias]), ','),
format(int(get_total(max_data,'sim_total_isolation_deduction_s1', total_cities)[lreal - 1+ bias]), ',')))
print('ratio cur')
print('{} ({}-{})'.format(data['current_asym']['0000'], min_data['current_asym']['0000'], max_data['current_asym']['0000']))
print('final')
print('{} ({}-{})'.format(data['final_asym']['0000'], min_data['final_asym']['0000'], max_data['final_asym']['0000']))
print('==================================', str(cur_date))
print('\n\n')
cur_data_4_03 = json.load(open('./data/cur_confirmed-{}.json'.format(str(today)), 'r'))
for item in total_cities:
print('{} &'.format(code_dict[item]), sep='', end='')
print('{} &'.format(format(int(cur_data_4_03[str(item)]), ',')), sep='',end='')
print('{} &'.format(format(int(data['sim_cum_confirmed_deduction_s1'][str(item)][lreal-1+bias]), ',')), sep='',end='')
print('{} &'.format(format(int(data['sim_cum_infection_deduction_s1'][str(item)][lreal-1+bias]), ',')), sep='',end='')
print('{} &'.format(format(int(data['sim_total_infection_deduction_s1'][str(item)][lreal-1+bias]), ',')), sep='',end='')
print('{} \\\\'.format(format(int(data['sim_cum_self_cured_deduction_s1'][str(item)][lreal-1+bias]), ',')), sep='',end='')
print('')
pass
print('\n\n')
print('{} &'.format('China\'s Mainland'), sep='', end='')
total_it = sum([cur_data_4_03[key] for key in cur_data_4_03.keys()])
print('{} &'.format(format(int(total_it), ',')), sep='', end='')
print('{} ({}-{}) &'.format(
format(int(get_total(data, 'sim_cum_confirmed_deduction_s1', total_cities)[lreal - 1 + bias]), ','),
format(int(get_total(min_data, 'sim_cum_confirmed_deduction_s1', total_cities)[lreal - 1 + bias]), ','),
format(int(get_total(max_data, 'sim_cum_confirmed_deduction_s1', total_cities)[lreal - 1 + bias]), ','),
), sep='', end='')
print('{} ({}-{}) &'.format(
format(int(get_total(data, 'sim_cum_infection_deduction_s1', total_cities)[lreal - 1 + bias]), ','),
format(int(get_total(min_data, 'sim_cum_infection_deduction_s1', total_cities)[lreal - 1 + bias]), ','),
format(int(get_total(max_data, 'sim_cum_infection_deduction_s1', total_cities)[lreal - 1 + bias]), ','),
), sep='', end='')
print('{} ({}-{}) &'.format(
format(int(get_total(data, 'sim_total_infection_deduction_s1', total_cities)[lreal - 1 + bias]), ','),
format(int(get_total(min_data, 'sim_total_infection_deduction_s1', total_cities)[lreal - 1 + bias]), ','),
format(int(get_total(max_data, 'sim_total_infection_deduction_s1', total_cities)[lreal - 1 + bias]), ','),
), sep='', end='')
print('{} ({}-{}) \\\\'.format(
format(int(get_total(data, 'sim_cum_self_cured_deduction_s1', total_cities)[lreal - 1 + bias]), ','),
format(int(get_total(min_data, 'sim_cum_self_cured_deduction_s1', total_cities)[lreal - 1 + bias]), ','),
format(int(get_total(max_data, 'sim_cum_self_cured_deduction_s1', total_cities)[lreal - 1 + bias]), ','),
), sep='', end='')
print('')
for item in total_cities:
print('{} &'.format(code_dict[item]), sep='', end='')
print('{} &'.format(format(int(cur_data_4_03[str(item)]), ',')), sep='',end='')
print('{} ({}-{}) &'.format(
format(int(data['sim_cum_confirmed_deduction_s1'][str(item)][lreal-1+bias]), ','),
format(int(min_data['sim_cum_confirmed_deduction_s1'][str(item)][lreal - 1 + bias]), ','),
format(int(max_data['sim_cum_confirmed_deduction_s1'][str(item)][lreal - 1 + bias]), ','),
), sep='',end='')
print('{} ({}-{}) &'.format(
format(int(data['sim_cum_infection_deduction_s1'][str(item)][lreal-1+bias]), ','),
format(int(min_data['sim_cum_infection_deduction_s1'][str(item)][lreal - 1 + bias]), ','),
format(int(max_data['sim_cum_infection_deduction_s1'][str(item)][lreal - 1 + bias]), ','),
), sep='',end='')
print('{} ({}-{}) &'.format(
format(int(data['sim_total_infection_deduction_s1'][str(item)][lreal-1+bias]), ','),
format(int(min_data['sim_total_infection_deduction_s1'][str(item)][lreal - 1 + bias]), ','),
format(int(max_data['sim_total_infection_deduction_s1'][str(item)][lreal - 1 + bias]), ','),
), sep='',end='')
print('{} ({}-{}) \\\\'.format(
format(int(data['sim_cum_self_cured_deduction_s1'][str(item)][lreal-1+bias]), ','),
format(int(min_data['sim_cum_self_cured_deduction_s1'][str(item)][lreal - 1 + bias]), ','),
format(int(max_data['sim_cum_self_cured_deduction_s1'][str(item)][lreal - 1 + bias]), ','),
), sep='',end='')
print('')
# print variables
x_list = construct_x(data, total_cities)
min_x_list = construct_x(min_data, total_cities)
max_x_list = construct_x(max_data, total_cities)
format_out(x_list, min_x_list, max_x_list)
exit(0)
def run_opt(city,
budget,
start_date,
important_dates,
infectratio_range=None,
dummy_range=None,
unob_flow_num=None,
repeat_time=1,
init_samples=None,
training_date_end=None,
json_name='data_run.json',
seed=1,
loss_ord=0.0,
unob_period=None,
obs_period=None,
iso_period=None,
cure_period=None,
isoratio_it=None):
if city == 420000:
infectratio_range = [0., 0.05]
dummy_range = [0.0000, 400.00001]
else:
assert infectratio_range is not None and dummy_range is not None
days_predict = 0
# load data
real_data = pd.read_csv(get_data_path())
history_real = prepareData(real_data)
flow_out_data = flowOutData()
# initialize models
infectratio = InfectRatio(1, [infectratio_range], [True])
touchratio = TouchRatio(1, [[0.0, 0.6]], [True])
touchratiointra = TouchRatio(1, [[0, 1]], [True])
obs = ObservationRatio(1, [[0.0, 0.3]], [True])
dead = DeadRatio(1, [[0., 0.01]], [True])
if isoratio_it is None:
isoratio = IsolationRatio(1, [[0.2, 0.5]], [True])
else:
isoratio = IsolationRatio(1, [isoratio_it], [True])
dummy = DummyModel(1, [dummy_range], [True, True])
cure_ratio = InfectRatio(1, [[0., 0.1]], [True])
# set the time of applying touchratio
simulator = Simulator(city,
infectratio,
touchratio,
obs,
dead,
dummy,
isoratio,
touchratiointra,
cure_ratio,
important_dates,
unob_flow_num=unob_flow_num,
flow_out_data=flow_out_data,
training_date_end=training_date_end)
# set period here
simulator.set_period()
test_date = datetime.strptime(history_real['date'].max(),
'%Y-%m-%d').date() - timedelta(days_predict)
history_real = history_real[history_real['adcode'] == city]
history_real = history_real[history_real['date'] >= str(start_date)]
history_train = history_real[history_real['date'] <= str(test_date)]
x, y = simulator.fit(history_train,
budget=budget,
server_num=get_core_num(),
repeat=repeat_time,
seed=seed,
intermediate_freq=10000,
init_samples=init_samples,
loss_ord=loss_ord)
print('best_solution: x = ', x, 'y = ', y)
simulator.set_param(x)
run_simulation(x,
city,
60,
60,
start_date,
get_important_date(city),
unob_flow_num=unob_flow_num,
json_name=json_name)
duration = len(real_data["date"].unique()) - 1
sim_res, _ = simulator.simulate(str(start_date), duration)
print('RMSE: ', get_newly_loss(sim_res, history_real))
return x, sim_res
def initHubei(x, start_date, important_date, travel_from_hubei):
if travel_from_hubei is None:
return None
days_predict = 0
# load data
real_data = pd.read_csv(get_data_path())
history_real = prepareData(real_data)
infectratio = InfectRatio(1, [[0, 1]], [True])
touchratio = TouchRatio(1, [[0., 0.3]], [True])
touchratiointra = TouchRatio(1, [[0, 10]], [True])
obs = ObservationRatio(1, [[0.0, 1.]], [True])
dead = DeadRatio(1, [[0., 0.1]], [True])
isoratio = IsolationRatio(1, [[0., 1]], [True])
cure_ratio = InfectRatio(1, [[0., 100]], [True])
dummy = DummyModel(1, [[0, 200000]], [True, True])
flow_out_data = flowOutData()
test_date = datetime.strptime(history_real['date'].max(),
'%Y-%m-%d').date() - timedelta(days_predict)
history_real = history_real[history_real['adcode'] == 420000]
history_real = history_real[history_real['date'] >= str(start_date)]
history_train = history_real[history_real['date'] <= str(test_date)]
duration = len(history_train["date"].unique())
city = 420000
simulator = Simulator(city,
infectratio,
touchratio,
obs,
dead,
dummy,
isoratio,
touchratiointra,
cure_ratio,
important_date,
flow_out_data=flow_out_data)
simulator.set_param(x)
total_population = get_populations()[420000]
simulated_result, detailed_result = simulator.simulate(
str(start_date), duration + 60)
init_unob = [item for item in reversed(detailed_result.unob_total_init)]
unob_ratio = {}
for i, item in enumerate(init_unob):
date_now = start_date - timedelta(i + 1)
unob_ratio[date_now] = item / total_population
for it_date, unob_num in zip(simulated_result['date'],
simulated_result['total_unobserved']):
unob_ratio[datetime.strptime(
it_date, '%Y-%m-%d').date()] = unob_num / total_population
unob_flow_num = {}
lst_travel = {}
lst_date = None
for it_date in travel_from_hubei:
if it_date not in unob_ratio:
continue
if it_date not in unob_flow_num:
unob_flow_num[it_date] = {}
for it_code in travel_from_hubei[it_date]:
unob_flow_num[it_date][it_code] = travel_from_hubei[it_date][
it_code] * unob_ratio[it_date]
lst_travel = travel_from_hubei[it_date]
lst_date = it_date
lst_date = lst_date + timedelta(1)
while True:
if lst_date in unob_ratio:
if lst_date not in unob_flow_num:
unob_flow_num[lst_date] = {}
for it_code in lst_travel:
unob_flow_num[lst_date][
it_code] = lst_travel[it_code] * unob_ratio[lst_date]
else:
break
lst_date = lst_date + timedelta(1)
return unob_flow_num
while True:
if lst_date in unob_ratio:
if lst_date not in unob_flow_num:
unob_flow_num[lst_date] = {}
for it_code in lst_travel:
unob_flow_num[lst_date][
it_code] = lst_travel[it_code] * unob_ratio[lst_date]
else:
break
lst_date = lst_date + timedelta(1)
return unob_flow_num
if __name__ == '__main__':
province_travel_dict = flowHubei()
real_data = pd.read_csv(get_data_path())['adcode'].unique()
province_code_dict = codeDict()
training_end_date = None
x_buffer = []
rerun_list = None
if rerun_list is not None:
old_param_buffer = [
pd.read_csv('params{}.csv'.format(int(i)))
for i in range(get_seed_num())
]
for i in range(get_seed_num()):
global_seed(i)
all_param = {}
if rerun_list is None or 420000 in rerun_list:
x = run_opt(420000,
loss_ord=loss_ord)
print('best_solution: x = ', x, 'y = ', y)
simulator.set_param(x)
run_simulation(x,
city,
60,
60,
start_date,
get_important_date(city),
unob_flow_num=unob_flow_num,
json_name=json_name)
return x
if __name__ == '__main__':
real_data = list(pd.read_csv(get_data_path(True))['adcode'].unique())
province_code_dict = it_code_dict()
rerun_list = None
old_param_buffer = None
training_end_date = None
if rerun_list is not None:
old_param_buffer = [
pd.read_csv('params_foreign{}.csv'.format(int(i)))
for i in range(get_seed_num())
]
for i in range(get_seed_num()):
all_param = {}
for ind, item in enumerate(real_data):
print(i, ind, item, province_code_dict[item])
if rerun_list is None or item in rerun_list:
touch_range = [0, 0.33333]