def fitness(self, x):
self.E0 = x[3] * self.I0
sol = pd.DataFrame(
SDSEIR.intger(self.S0, self.E0, self.I0, self.R0, min(self.tr),
max(self.tr), self.h, x[0], x[1], x[2], self.mov,
self.qp, self.tr[-1], self.movefunct))
idx = np.searchsorted(sol.t, self.tr)
res = LA.norm(self.Ir - sol.I[idx])
return ([res])
def rep_fun_eta(mu_i,rep):
out=[]
for i in range(int(rep)):
results = SDSEIR.ref_sim_national_mu(mov=mov,qp=0,tsim = tsim,tci=None,movfunct='sawtooth',mu=mu_i)
params=results['params']
params=np.append(params,mu_i)
params=np.append(params,results['err'])
params=np.append(params,results['rerr'])
print(params)
out.append(params)
return(out)
def refineuni():
'''
# ----------------------------------------------------------- #
# Parameters input when the simulate button is pressed #
# ----------------------------------------------------------- #
'''
print("Refine")
try:
# Manage input parameters
if request.form:
print("I have form data")
state = request.form.get('state') #State (Region)
comuna = request.form.get('comuna') # District
qp = int(request.form.get('qp')) # Quarantine period
mov = float(request.form.get('mov')) # Quarantine movilty
tsim = int(request.form.get('tSim')) # Simulation time
movfunct = str(request.form.get('movfunct'))
if request.json:
print("I have json")
state = request.json['state']
comuna = request.json['comuna']
qp = int(request.json['qp'])
mov = float(request.json['mov']) # Quarantine movilty
tsim = int(request.json['tSim'])
movfunct = str(request.json['movfunct'])
#tsim = 100
#mov = 0.2
if qp == -1:
qp = tsim
# ---------------------------- #
# Get Refine Data #
# ---------------------------- #
path = '../Data/unirefine/'
# Get data for different quarantine periods
# Import data, parameters and initdate
#parameters = pd.read_csv(path+'parameters_qp0.csv',index_col=0)
#initdate = pd.read_csv(path+'initdate_qp0.csv',index_col=0)
parameters = pd.read_csv(path + 'parameters.csv', index_col=0)
initdate = pd.read_csv(path + 'initdate.csv', index_col=0)
## Find data for paramters given
parameters = parameters[comuna]
initdate = initdate[comuna][0]
results = SDSEIR.simulate(state,
comuna,
parameters[0],
parameters[1],
parameters[2],
parameters[3],
qp=qp,
mov=mov,
tsim=tsim,
movfunct=movfunct)
S = results['S'].tolist()
E = results['E'].tolist()
I = results['I'].tolist()
R = results['R'].tolist()
t = list(results['t'])
# Round results:
S = [round(i) for i in S]
E = [round(i) for i in E]
I = [round(i) for i in I]
R = [round(i) for i in R]
Ti = 1 / parameters[1]
Tr = 1 / parameters[2]
#print(results)
print("done simulating")
print('beta,sigma,gama,mu')
print(parameters)
response = {
'status': 'OK',
'S': S,
'E': E,
'I': I,
'R': R,
't': t,
'Ti': Ti,
'Tr': Tr,
'beta': parameters[0],
'r0': parameters[3],
'initdate': initdate,
'I_peak': max(I),
'R_total': (max(R))
}
#print(response)
return jsonify(response), 200
except Exception as e:
print(e)
response = {"error": str(e)}
return response, 200
def simulateuni():
'''
# ----------------------------------------------------------- #
# Parameters input when the simulate button is pressed #
# ----------------------------------------------------------- #
'''
# Manage input parameters
try:
if request.form:
print("I have form data")
state = str(request.form.get('state'))
comuna = str(request.form.get('comuna'))
qp = int(request.form.get('qp'))
beta = float(request.form.get('beta'))
Ti = int(request.form.get('Ti')) #sigma-1
Tr = int(request.form.get('Tr')) #gamma-1
r0 = float(request.form.get('r0'))
mov = float(request.form.get('mov')) #Quarantine movilty
tsim = int(request.form.get('tSim'))
movfunct = str(request.form.get('movfunct'))
if request.json:
print("I have json")
state = str(request.json['state'])
comuna = str(request.json['comuna'])
qp = int(request.json['qp'])
beta = float(request.json['beta'])
Ti = int(request.json['Ti']) #sigma-1
Tr = int(request.json['Tr']) #gamma-1
r0 = float(request.json['r0'])
mov = float(request.json['mov'])
tsim = int(request.json['tSim'])
movfunct = str(request.json['movfunct'])
#mov = 0.2#request.form.get('aten')
#tsim = 100
#tci = request.form.get('tci')
#print('Inputs')
#print(state,comuna,qp,beta,Ti,Tr,r0,mov,tsim,movfunct)
#state, comuna, beta, Ti,Tr,r0,Q
if Ti == 0:
sigma = 1
else:
sigma = 1 / Ti
if Tr == 0:
gamma = 1
else:
gamma = 1 / Tr
#{"S":S,"E":E,"I":I,"R":R,"t":t}
print('state,comuna,beta,sigma,gamma,r0,qp,mov,tsim')
print(state, comuna, beta, sigma, gamma, r0, qp, mov, tsim)
results = SDSEIR.simulate(state,
comuna,
beta,
sigma,
gamma,
r0,
qp,
mov,
tsim,
movfunct=movfunct)
S = results['S'].tolist()
E = results['E'].tolist()
I = results['I'].tolist()
R = results['R'].tolist()
t = list(results['t'])
init_date = results['init_date']
# Round results:
S = [round(i) for i in S]
E = [round(i) for i in E]
I = [round(i) for i in I]
R = [round(i) for i in R]
a = pd.DataFrame(results)
a.to_csv('lastrequest.csv')
response = {
'status': 'OK',
'S': S,
'E': E,
'I': I,
'R': R,
't': t,
'init_date': init_date,
'I_peak': max(I),
'R_total': (max(R))
}
print(response.keys())
return response, 200
except Exception as e:
print(e)
response = {"error": str(e)}
return response, 200
print(tstate)
# Input parameters:
mov = 0.2 # movility level during quarantine
qp = 0 # Quarantine Period
simdata = pd.DataFrame()
simparameters = pd.DataFrame(index=['beta', 'sigma', 'gamma', 'mu', 'err'])
initdate = pd.DataFrame()
# Refine parameters per CUT
n = 0
for index, row in cutlist.iterrows():
state = str(row[0])[0:2]
comuna = str(row[0])
if tstate == None or tstate == state:
print('Refining Comuna ' + comuna)
result = SDSEIR.ref_pygmo(state, comuna, mov, qp)
if result:
simdata[comuna] = result['sim']['I'] #Check Initial date
pd.DataFrame(result['sim']['I']).to_csv(comuna + '_I.csv')
simparameters[comuna] = np.append(result['params'],
result['err']) # Parameters
initdate[comuna] = [
(result['init_date'] +
datetime.timedelta(days=43830)).strftime("%d-%b-%Y")
] #Check Initial date
print('error: ' + str(result['err']))
print('Saving csv data')
path = '../Data/DatosConsolidados/'
if tstate == None:
simdata.to_csv(path + 'uni_sim.csv')
simparameters.to_csv(path + 'uni_parameters.csv')
import pandas as pd
import numpy as np
import datetime as dt
import Single_dist_ref_SEIR as SDSEIR
import matplotlib.pyplot as plt
# ---------------------------------- #
# Find Optimal Parameters #
# ----------------------------------- #
np.random.seed()
tsim = 400
mov = 0.2
results = SDSEIR.ref_sim_national(mov=mov,
qp=0,
tsim=tsim,
tci=None,
movfunct='sawtooth')
Si = results['sim']['S'][0]
Ei = results['sim']['E'][0]
Ii = results['sim']['I'][0]
Ri = results['sim']['R'][0]
ti = 0
beta = results['params'][0]
sigma = results['params'][1]
gamma = results['params'][2]
Ir = pd.DataFrame(results['Ir'], columns=['Ir'])
tr = pd.DataFrame(results['tr'], columns=['tr'])
# Total active cases each day
Ir = np.zeros(data.shape[0])
Ir[np.where(np.array(tr) <= 14)[0]] = data[ncomuna][np.where(
np.array(tr) <= 14)[0]]
for i in np.where(np.array(tr) > 14)[0]:
ind = np.where(np.array(tr) - tr[i] + 14 < 0)[0]
Ir[i] = data[ncomuna][i] - sum(Cn[0:ind[-1]])
qp = 0
mov = 0.2
tsim = 100
tci = None
movfunct = 'sawtooth'
# refine again madafacas
refresults = SDSEIR.ref_sim_cons(state, comuna, mov, qp, tsim, tci, movfunct)
beta = refresults['params'][0]
gamma = refresults['params'][1]
sigma = refresults['params'][2]
mu = refresults['params'][3]
err = refresults['err']
# ------------ #
# Simulate #
# ------------ #
results = SDSEIR.simulate(state, comuna, beta, sigma, gamma, mu, qp, mov, tsim,
tci, movfunct)
results['init_date'] = '22-feb-2020'
S = results['S']
E = results['E']