def opti(x):
i = 0
aux = [0, 0, 0, 0]
# Beta
if type(self.beta_r) == list:
aux[0] = x[i]
i += 1
else:
aux[0] = self.beta_r
# Sigma
if type(self.sigma_r) == list:
aux[1] = x[i]
i += 1
else:
aux[1] = self.sigma_r
# Gamma
if type(self.gamma_r) == list:
aux[2] = x[i]
i += 1
else:
aux[2] = self.gamma_r
# Mu
if type(self.mu_r) == list:
aux[3] = x[i]
i += 1
else:
aux[3] = self.mu_r
model = SEIR(self.P, self.eta, self.alpha, self.S0, self.E0,
self.I0, self.R0, aux[0], aux[1], aux[2], aux[3])
model.integr(self.t0, self.T, self.h, True)
return (objective_funct(Ir, tr, model.I, model.t, 'fro'))
def opti(x):
i = 0
aux = [0, 0, 0, 0]
# Beta
if type(self.beta_r) == list:
aux[0] = x[i]
i += 1
else:
aux[0] = self.beta_r
# Sigma
if type(self.sigma_r) == list:
aux[1] = x[i]
i += 1
else:
aux[1] = self.sigma_r
# Gamma
if type(self.gamma_r) == list:
aux[2] = x[i]
i += 1
else:
aux[2] = self.gamma_r
# Mu
if type(self.mu_r) == list:
aux[3] = x[i]
i += 1
else:
aux[3] = self.mu_r
ie = 0
ie = i
def eta(t):
eta = np.ones(self.P.shape[0])
for j in range(self.P.shape[0]):
eta[j] = x[ie + j]
return (eta)
self.eta = eta
ia = 0
ia = ie + self.P.shape[0]
def alpha(t):
alpha = np.zeros([self.P.shape[0], self.P.shape[0]])
for j in range(self.P.shape[0]):
for k in range(self.P.shape[0]):
alpha[j][k] = x[j + ia] * x[k + ia]
return (alpha)
self.alpha = alpha
model = SEIR(self.P, eta, alpha, self.S0, self.E0, self.I0,
self.R0, aux[0], aux[1], aux[2], aux[3])
model.integr(self.t0, self.T, self.h, True)
self.eta = eta
self.alpha = alpha
return (objective_funct(Ir, tr, model.I, model.t, 'fro'))
def met_hast(self, I_r, tr, beta_i, sigma_i, gamma_i, mu_i, r0, steps, err,
objective_funct):
#print("Build SEIR")
x = SEIR(self.P, self.eta, self.alpha, self.S0, self.E0, self.I0,
self.R0, beta_i, gamma_i, sigma_i, mu_i)
#print("RK4")
if x.scikitsimport:
x.integr(self.t0, self.T, self.h, True)
else:
#print("RK4")
x.integr_RK4(self.t0, self.T, self.h, True)
e_0 = objective_funct(I_r, tr, x.I, x.t, 2)
e_o = e_0
params = [[beta_i, sigma_i, gamma_i, mu_i, e_o]]
i = 0
#print("Met-Hast")
while i < steps:
[b_p, s_p, g_p,
m_p] = self.transition_model(x.beta, x.sigma, x.gamma, x.mu, r0)
x_new = SEIR(self.P, self.eta, self.alpha, self.S0, self.E0,
self.I0, self.R0, b_p, s_p, g_p, m_p)
if x_new.scikitsimport:
x_new.integr(self.t0, self.T, self.h, True)
else:
x_new.integr_RK4(self.t0, self.T, self.h, True)
e_n = objective_funct(I_r, tr, x_new.I, x_new.t, 2)
# Acceptance
if (e_n / e_o < 1):
x = x_new
e_o = e_n
params.append([b_p, s_p, g_p, m_p, e_n])
i += 1
print("------------------")
print(b_p, s_p, g_p, m_p)
print(e_o, e_n)
# print("time: "+str(end-start))
# print("Step "+str(i))
else:
u = np.random.uniform(0, 1)
if (e_n / e_0 > u):
x = x_new
e_o = e_n
params.append([b_p, s_p, g_p, m_p, e_n])
i += 1
print("------------------")
print(b_p, s_p, g_p, m_p)
print(e_o, e_n)
#sleep(0.01)
# Dejo los params historicos por si hay que debuggear esta parte
return params
# print(error)
# outfile = "/home/sropert/sigmaerror4.csv"
# #Gamma
# for i in range(len(gamma)):
# test = SEIR(P,eta,alpha,S0,E0,I0,R0,betaopt, sigmaopt, gamma[i],muopt)
# test.integr(min(tr),max(tr),0.1,True)
# error.append(objective_funct(Ir,tr,test.I,test.t,'fro'))
# print(error)
# outfile = "/home/sropert/gammaerror4.csv"
#Mu
for i in range(len(mu)):
test = SEIR(P,eta,alpha,S0,E0,I0,R0,betaopt, sigmaopt, gammaopt,mu[i])
test.integr(min(tr),max(tr),0.1,True)
error.append(objective_funct(Ir,tr,test.I,test.t,'fro'))
print(error)
outfile = "/home/sropert/muerror4.csv"
np.savetxt(outfile, error, delimiter=",")
#error 1:
#mu = np.arange(1.8,2.2,0.002)
#error 2:
#mu = np.arange(1.98,2.02,0.0002)
#error 3:
def simulate_multi(state,
comunas,
params,
qp=0,
mov=0.2,
tsim=300,
tci=None,
movfunct='sawtooth'):
# params = beta sigma gamma mu
# Movility function shape
if movfunct == 'sawtooth':
def f(t):
return signal.sawtooth(t)
elif movfunct == 'square':
def f(t):
return signal.square(t)
if qp <= 0:
def e_fun(t):
return (1)
elif tci is None:
def e_fun(t):
return ((1 - mov) / 2 * (f(np.pi / qp * t - np.pi)) +
(1 + mov) / 2)
else:
def e_fun(t):
if t < tci:
return (1)
else:
return ((1 - mov) / 2 * (f(np.pi / qp * t - np.pi)) +
(1 + mov) / 2)
#Endpoint encuesta SECTR, no funcional aún
# endpoint="http://192.168.2.223:5004/v1/get_movilidad_por_comuna"
# r = requests.get(endpoint) #, params = {"w":"774508"})
# mydict = r.json()
# OD=pd.DataFrame(mydict)
#Endpoint encuensta ENE
# endpoint="http://192.168.2.223:5004/v2/get_movilidad?month=1&year=2019"
# r = requests.get(endpoint) #, params = {"w":"774508"})
# mydict = r.json()
# info=pd.DataFrame(mydict)
# Data csv encuenstra Sectra (funcional)
path = "../Data/"
OD = pd.read_csv(path + "Movilidad_diaria_comuna.csv", index_col=0)
P = OD.filter(comunas)
P = P.loc[comunas].to_numpy()
endpoint = "http://192.168.2.220:8080/covid19/findComunaByIdState?idState=" + state + "&&comuna="
r = requests.get(endpoint) #, params = {"w":"774508"})
mydict = r.json()
info = pd.DataFrame(mydict)
comunas = info[info.description.isin(comunas)]
init_dates = []
for i in comunas.cut:
endpoint = "http://192.168.2.220:8080/covid19/getDatosMinSalSummary?state=" + state + "&comuna=" + i
r = requests.get(endpoint) #, params = {"w":"774508"})
mydict = r.json()
data = pd.DataFrame(mydict)
data = data[data.data != 0]
init_dates.append(dt.datetime.strptime(data.labels.iloc[0], '%d/%m'))
init_date = max(init_dates).strftime('%d/%m')
Ic = []
for i in comunas.cut:
endpoint = "http://192.168.2.220:8080/covid19/getDatosMinSalSummary?state=" + state + "&comuna=" + i
r = requests.get(endpoint) #, params = {"w":"774508"})
mydict = r.json()
data = pd.DataFrame(mydict)
init_i = data[data.labels == init_date].index
if len(init_i) != 0:
init_i = data[data.labels == init_date].index[0]
if len(Ic) == 0:
Ic = data[['labels', 'data']][init_i:].set_index('labels')
Ic.rename(columns={'data': i}, inplace=True)
else:
Ic = Ic.join(data[['labels',
'data']][init_i:-1].set_index('labels'),
on='labels')
Ic.rename(columns={'data': i}, inplace=True)
else:
init_i = 0
if len(Ic) == 0:
Ic = data[['labels', 'data']][init_i:].set_index('labels')
Ic.rename(columns={'data': i}, inplace=True)
else:
Ic = Ic.join(data[['labels',
'data']][init_i:-1].set_index('labels'),
on='labels')
Ic.rename(columns={'data': i}, inplace=True)
Ic = Ic.fillna(0)
for i in (Ic.columns):
for j in range(max(Ic.count()) - 1):
if Ic[i].iloc[j + 1] < Ic[i].iloc[j]:
Ic[i].iloc[j + 1] = Ic[i].iloc[j]
tr = np.zeros(int(max(Ic.count())))
for i in range(1, int(max(Ic.count())), 1):
diff = dt.datetime.strptime(Ic.index[i],
'%d/%m') - dt.datetime.strptime(
Ic.index[i - 1], '%d/%m')
tr[i] = diff.days + tr[i - 1]
Cn = np.zeros(Ic.shape)
for i in range(Ic.shape[1]):
Cn[0][i] = Ic.iloc[0][i]
for j in range(1, Ic.shape[0], 1):
Cn[j][i] = Ic.iloc[j][i] - Ic.iloc[j - 1][i]
Ir = np.zeros(Ic.shape)
for i in range(Ic.shape[1]):
Ir[np.where(tr - 14 <= 0)[0], i] = Ic.iloc[np.where(tr - 14 <= 0)[0],
i]
for j in np.where(tr - 14 > 0)[0]:
ind = np.where(tr - tr[j] + 14 < 0)[0]
Ir[j, i] = Ic.iloc[j, i] - sum(Cn[0:ind[-1], i])
So = comunas.numPopulation.to_numpy()
Ro = np.array([0, 0, 0, 0, 0])
Ir = Ic.transpose().to_numpy()
Io = Ir[:, 0]
Eo = 2 * Io
S0 = So - Eo - Io
E0 = Eo
I0 = Io
R0 = Ro
h = 0.01
ia = 3 + P.shape[0]
def alpha(t):
alpha = np.zeros([P.shape[0], P.shape[0]])
for j in range(P.shape[0]):
for k in range(P.shape[0]):
alpha[j][k] = e_fun(t) * params[j + ia] * params[k + ia]
return (alpha)
def eta(t):
eta = np.ones(P.shape[0])
for j in range(P.shape[0]):
eta[j] = e_fun(t) * params[3 + j]
return (eta)
sim = SEIR(P, eta, alpha, S0, E0, I0, R0, params[0], params[1], params[2],
params[3])
sim.integr(min(tr), tsim, h, True)
return ({
'S': sim.S,
'E': sim.E,
'I': sim.I,
'R': sim.R,
'tout': sim.t,
'init_date': init_date
})