def execute_sim(function,Ts,tf,Nsims,Pi,name=None,cluster=False,informative=True):
nSteps = int(tf/Ts)+1
## matrix of initial conditions, size 2 x N
X0 = np.random.multivariate_normal(mux0,Pi,size=(Nsims,)).transpose()
## simulation output/measurement times
tsim = np.arange(0.0,tf+Ts,Ts)
## simulation output measurements
YK = np.zeros((nSteps,Nsims))
## simulation state history
XK = np.zeros((nSteps,2*Nsims))
t1 = time.time()
for k in range(Nsims):
if not cluster:
sim = cp_dynamics.cp_simObject(function,X0[:,k],Ts)
if cluster:
if informative:
sim = cp_dynamics.cp_simObjectCluster(function,X0[:,k],Ts)
else:
sim = cp_dynamics.cp_simObjectNonInformative(function,X0[:,k],Ts)
# simulate
(YK[:,k],XK[:,(2*k):(2*k+2)],tk) = sim.simFull(Tf=tf)
t2 = time.time()
etaCalc(k,Nsims,t2-t1)
t2 = time.time()
print("Completed %d sims in %g secs" % (Nsims,t2-t1))
return(tsim,XK,YK,mux0,Ts,tf)
def generate_sim(function,Ts,tf,name=None,cluster=False,informative=True):
nSteps = int(tf/Ts)+1
## matrix of initial conditions, size 2 x N
if not cluster:
X0 = np.random.multivariate_normal(mux0,P0,size=(Ns,)).transpose()
else:
X0 = np.random.multivariate_normal(mux0,Pcluster,size=(Ns,)).transpose()
## simulation output/measurement times
tsim = np.arange(0.0,tf+Ts,Ts)
## simulation output measurements
YK = np.zeros((nSteps,Ns))
## simulation state history
XK = np.zeros((nSteps,2*Ns))
t1 = time.time()
for k in range(Ns):
if not cluster:
sim = cp_dynamics.cp_simObject(function,X0[:,k],Ts)
if cluster:
if informative:
sim = cp_dynamics.cp_simObjectCluster(function,X0[:,k],Ts)
else:
sim = cp_dynamics.cp_simObjectNonInformative(function,X0[:,k],Ts)
# simulate
(YK[:,k],XK[:,(2*k):(2*k+2)],tk) = sim.simFull(Tf=tf)
t2 = time.time()
etaCalc(k,Ns,t2-t1)
t2 = time.time()
print("Completed %d sims in %g secs" % (Ns,t2-t1))
if name is not None:
# write to file
# write settings
FID = open(name + "_settings.ini",'w')
FID.write("[%s]\n" % (name))
FID.write("Function: " + function.__name__ + "\n")
FID.write("Ts: %g\n" % (Ts))
FID.write("tf: %g\n" % tf)
FID.write("Ns: %d\n" % Ns)
if not cluster:
FID.write("P0_11: %f\n" % P0[0,0])
FID.write("P0_12: %f\n" % P0[0,1])
FID.write("P0_21: %f\n" % P0[1,0])
FID.write("P0_22: %f\n" % P0[1,1])
else:
FID.write("P0_11: %f\n" % Pcluster[0,0])
FID.write("P0_12: %f\n" % Pcluster[0,1])
FID.write("P0_21: %f\n" % Pcluster[1,0])
FID.write("P0_22: %f\n" % Pcluster[1,1])
FID.write("mux_1: %f\n" % mux0[0])
FID.write("mux_2: %f\n" % mux0[1])
FID.close()
print("Wrote settings file")
# write data
datafilename = name + "_data.csv"
FID = open(datafilename,'w')
for k in range(nSteps):
FID.write("%f," % tsim[k])
for j in range(Ns):
FID.write("%f,%f,%f," % (XK[k,2*j],XK[k,2*j+1],YK[k,j]))
FID.write("\n")
FID.close()
print("Wrote data file")
pass
else:
# plot histories and measurements
fig = plt.figure()
ax = []
for k in range(2):
nam = 'x' + str(k+1)
ax.append(fig.add_subplot(1,2,k+1,ylabel=nam))
inplot1 = range(k,2*Ns,2)
print(inplot1)
ax[k].plot(tsim,XK[:,inplot1])
if k == 0:
ax[k].plot(tsim,YK)
fig.show()
raw_input("Return to continue")
return
