def showResults(time, x, sqrtP, y, Sy, y_full, csvTrue, m):
# convert results
x = numpy.array(x)
y = numpy.squeeze(numpy.array(y))
sqrtP = numpy.array(sqrtP)
Sy = numpy.array(Sy)
y_full = numpy.squeeze(numpy.array(y_full))
# Read from file
simResults = CsvReader.CsvReader()
simResults.OpenCSV(csvTrue)
# Get time series values
simResults.SetSelectedColumn("chi.etaPL")
t = simResults.GetDataSeries().index
eta_PL = simResults.GetDataSeries().values
simResults.SetSelectedColumn("P")
P = simResults.GetDataSeries().values
simResults.SetSelectedColumn("T_CH_Lea")
T_ch = simResults.GetDataSeries().values
simResults.SetSelectedColumn("T_CW_lea")
T_cw = simResults.GetDataSeries().values
simResults.SetSelectedColumn("chi.COP")
COP = simResults.GetDataSeries().values
# plot and save images
fig0 = plt.figure()
fig0.set_size_inches(12, 8)
ix = 2
ax0 = fig0.add_subplot(111)
ax0.plot(t, eta_PL, 'g', label='$\eta_{PL}$', alpha=1.0)
ax0.plot(time, x[:, ix], 'r', label='$\hat{\eta}_{PL}$')
ax0.fill_between(time,
x[:, ix] - sqrtP[:, ix, ix],
x[:, ix] + sqrtP[:, ix, ix],
facecolor='red',
interpolate=True,
alpha=0.3)
ax0.set_xlabel('Time [s]')
ax0.set_ylabel('COP')
ax0.set_xlim([t[0], t[-1]])
legend = ax0.legend(loc='upper center',
bbox_to_anchor=(0.5, 1.1),
ncol=1,
fancybox=True,
shadow=True)
legend.draggable()
ax0.grid(False)
plt.savefig('PartLoadEffectiveness.pdf',
dpi=400,
bbox_inches='tight',
transparent=True,
pad_inches=0.1)
fig1 = plt.figure()
ax2 = fig1.add_subplot(111)
ax2.plot(t, T_ch, 'bo', label='$T_{CH}$', alpha=1.0)
ax2.plot(t, T_cw, 'ro', label='$T_{CW}$', alpha=1.0)
ax2.plot(time, x[:, 0], 'r', label='$Tcw$', alpha=1.0)
ax2.fill_between(time,
x[:, 0] - sqrtP[:, 0, 0],
x[:, 0] + sqrtP[:, 0, 0],
facecolor='red',
interpolate=True,
alpha=0.3)
ax2.plot(time, x[:, 1], 'b', label='$Tch$', alpha=1.0)
ax2.fill_between(time,
x[:, 1] - sqrtP[:, 1, 1],
x[:, 1] + sqrtP[:, 1, 1],
facecolor='blue',
interpolate=True,
alpha=0.3)
ax2.set_xlabel('Time [s]')
ax2.set_ylabel('Output temperatures')
ax2.set_xlim([t[0], t[-1]])
legend = ax2.legend(loc='upper center',
bbox_to_anchor=(0.5, 1.1),
ncol=1,
fancybox=True,
shadow=True)
legend.draggable()
ax2.grid(False)
plt.savefig('OutputTemperatures.pdf',
dpi=400,
bbox_inches='tight',
transparent=True,
pad_inches=0.1)
fig2 = plt.figure()
ax3 = fig2.add_subplot(111)
ax3.plot(t, P, 'go', label='$P$', alpha=1.0)
ax3.plot(time, y_full[:, 0], 'r', label='$\hat{P}$')
ax3.fill_between(time,
y_full[:, 0] - Sy[:, 0, 0],
y_full[:, 0] + Sy[:, 0, 0],
facecolor='red',
interpolate=True,
alpha=0.3)
ax3.set_xlabel('Time [s]')
ax3.set_ylabel('Output Variable')
ax3.set_xlim([t[0], t[-1]])
legend = ax3.legend(loc='upper center',
bbox_to_anchor=(0.5, 1.1),
ncol=1,
fancybox=True,
shadow=True)
legend.draggable()
ax3.grid(False)
fig3 = plt.figure()
ax4 = fig3.add_subplot(111)
ax4.plot(t, COP, 'k--', label='$COP$', alpha=1.0)
ax4.plot(time, y_full[:, 1], 'r', label='$\hat{COP}$')
ax4.fill_between(time,
y_full[:, 1] - Sy[:, 1, 1],
y_full[:, 1] + Sy[:, 1, 1],
facecolor='red',
interpolate=True,
alpha=0.3)
ax4.set_xlabel('Time [s]')
ax4.set_ylabel('COP')
ax4.set_xlim([t[0], t[-1]])
legend = ax4.legend(loc='upper center',
bbox_to_anchor=(0.5, 1.1),
ncol=1,
fancybox=True,
shadow=True)
legend.draggable()
ax4.grid(False)
plt.show()
def showResults(time, x, sqrtP, y, Sy, y_full, csvTrue):
# convert results
x = numpy.array(x)
y = numpy.array(y)
sqrtP = numpy.array(sqrtP)
Sy = numpy.array(Sy)
y_full = numpy.squeeze(numpy.array(y_full))
# Get the true and unmeasured metal temperature
simResults = CsvReader.CsvReader()
simResults.OpenCSV(csvTrue)
simResults.SetSelectedColumn("heatExchanger.metal.T")
res = simResults.GetDataSeries()
t = res["time"]
d = numpy.squeeze(numpy.asarray(res["data"]))
fig1 = plt.figure()
ax1 = fig1.add_subplot(211)
id = 0
ax1.plot(time, x[:, id], 'r', label='$\hat{T}_{Metal}$', alpha=1.0)
ax1.fill_between(time,
x[:, id] - sqrtP[:, id, id],
x[:, id] + sqrtP[:, id, id],
facecolor='red',
interpolate=True,
alpha=0.3)
ax1.plot(t, d, 'g', label='$T_{Metal}$', alpha=1.0)
ax1.set_xlabel('Time [s]')
ax1.set_ylabel('Metal temperature')
ax1.set_xlim([time[0], time[-1]])
legend = ax1.legend(loc='upper center',
bbox_to_anchor=(0.5, 1.1),
ncol=5,
fancybox=True,
shadow=True)
legend.draggable()
ax1.grid(False)
ax2 = fig1.add_subplot(212)
id = 1
ax2.plot(time, x[:, id], 'b', label='$\hat{G}_{Hot}$', alpha=1.0)
ax2.fill_between(time,
x[:, id] - sqrtP[:, id, id],
x[:, id] + sqrtP[:, id, id],
facecolor='blue',
interpolate=True,
alpha=0.3)
id = 2
ax2.plot(time, x[:, id], 'c', label='$\hat{G}_{Cold}$', alpha=1.0)
ax2.fill_between(time,
x[:, id] - sqrtP[:, id, id],
x[:, id] + sqrtP[:, id, id],
facecolor='cyan',
interpolate=True,
alpha=0.3)
ax2.set_xlabel('Time [s]')
ax2.set_ylabel('Thermal conductances')
ax2.set_xlim([t[0], t[-1]])
legend = ax2.legend(loc='upper center',
bbox_to_anchor=(0.5, 1.1),
ncol=2,
fancybox=True,
shadow=True)
legend.draggable()
ax2.grid(False)
plt.savefig('Heat_Exchanger_UKF.pdf',
dpi=400,
bbox_inches='tight',
transparent=True,
pad_inches=0.1)
plt.show()
plt.show()
def showResults(time, x, sqrtP, y, Sy, y_full, Xsmooth, Ssmooth, Yfull_smooth,
csvTrue):
# Convert list to arrays
time = time / 3600.0
x = numpy.squeeze(numpy.array(x))
y = numpy.squeeze(numpy.array(y))
sqrtP = numpy.squeeze(numpy.array(sqrtP))
Sy = numpy.squeeze(numpy.array(Sy))
xs = numpy.array(Xsmooth)
Ss = numpy.array(Ssmooth)
Ys = numpy.array(Yfull_smooth)
print "smoothed end", xs[-1, :]
print "smoothed start", xs[0, :]
print "smoothed average", numpy.average(xs, 0)
print "filtered end", x[-1, :]
print "filtered start", x[0, :]
print "filtered average", numpy.average(x, 0)
####################################################################
# Display results
simResults = CsvReader.CsvReader()
simResults.OpenCSV(csvTrue)
simResults.SetSelectedColumn("Pump.kW")
res = simResults.GetDataSeries()
t = res["time"]
t = t / 3600.0
d_kW = numpy.squeeze(numpy.asarray(res["data"]))
simResults.SetSelectedColumn("Pump.Speed")
res = simResults.GetDataSeries()
d_rpm = numpy.squeeze(numpy.asarray(res["data"]))
simResults.SetSelectedColumn("Pump.gpm")
res = simResults.GetDataSeries()
d_gpm = numpy.squeeze(numpy.asarray(res["data"]))
fig0 = plt.figure()
fig0.set_size_inches(12, 8)
ax0 = fig0.add_subplot(111)
#ax0.plot(time,x,'r',label='$a_5$',alpha=1.0)
#ax0.fill_between(time, x - sqrtP, x + sqrtP, facecolor='red', interpolate=True, alpha=0.3)
idx = 0
ax0.plot(time, x[:, idx], 'r', label='$a_1$', alpha=1.0)
ax0.fill_between(time,
x[:, idx] - sqrtP[:, idx, idx],
x[:, idx] + sqrtP[:, idx, idx],
facecolor='red',
interpolate=True,
alpha=0.05)
ax0.plot(time, xs[:, idx], 'r--', label='$a_1$', alpha=1.0)
ax0.fill_between(time,
xs[:, idx] - Ss[:, idx, idx],
xs[:, idx] + Ss[:, idx, idx],
facecolor='red',
interpolate=True,
alpha=0.05)
idx = 1
ax0.plot(time, x[:, idx], 'b', label='$a_3$', alpha=1.0)
ax0.fill_between(time,
x[:, idx] - sqrtP[:, idx, idx],
x[:, idx] + sqrtP[:, idx, idx],
facecolor='blue',
interpolate=True,
alpha=0.05)
ax0.plot(time, xs[:, idx], 'b--', label='$a_3$', alpha=1.0)
ax0.fill_between(time,
xs[:, idx] - Ss[:, idx, idx],
xs[:, idx] + Ss[:, idx, idx],
facecolor='blue',
interpolate=True,
alpha=0.05)
idx = 2
ax0.plot(time, x[:, idx], 'k', label='$a_5$', alpha=1.0)
ax0.fill_between(time,
x[:, idx] - sqrtP[:, idx, idx],
x[:, idx] + sqrtP[:, idx, idx],
facecolor='black',
interpolate=True,
alpha=0.05)
ax0.plot(time, xs[:, idx], 'k--', label='$a_5$', alpha=1.0)
ax0.fill_between(time,
xs[:, idx] - Ss[:, idx, idx],
xs[:, idx] + Ss[:, idx, idx],
facecolor='black',
interpolate=True,
alpha=0.05)
idx = 3
ax0.plot(time, x[:, idx], 'c', label='$a_7$', alpha=1.0)
ax0.fill_between(time,
x[:, idx] - sqrtP[:, idx, idx],
x[:, idx] + sqrtP[:, idx, idx],
facecolor='cyan',
interpolate=True,
alpha=0.05)
ax0.plot(time, xs[:, idx], 'c--', label='$a_7$', alpha=1.0)
ax0.fill_between(time,
xs[:, idx] - Ss[:, idx, idx],
xs[:, idx] + Ss[:, idx, idx],
facecolor='cyan',
interpolate=True,
alpha=0.05)
ax0.set_xlabel('Time [hours]')
ax0.set_ylabel('Coefficients [$\cdot$]')
ax0.set_xlim([time[0], time[-1]])
ax0.set_ylim([0.0, 1.0])
legend = ax0.legend(loc='upper center',
bbox_to_anchor=(0.5, 1.0),
ncol=4,
fancybox=True,
shadow=True)
legend.draggable()
ax0.grid(False)
plt.savefig('Coefficients.pdf',
dpi=400,
bbox_inches='tight',
transparent=True,
pad_inches=0.1)
####################################################################
# Display results
fig1 = plt.figure()
fig1.set_size_inches(20, 8)
ax1 = fig1.add_subplot(111)
ax1.plot(t, d_kW, 'g', label='$P_{EL}^{Measured}$', alpha=1.0)
#ax1.plot(t,d_gpm,'g',label='$\dot{m}_{PUMP}^{Measured}$',alpha=1.0)
ax1.plot(time, y, 'r', label='$P_{EL}^{UKF}$', alpha=1.0)
ax1.plot(time, Ys[:, 0], 'b', label='$P_{EL}^{Smooth}$', alpha=1.0)
#ax1.plot(time,y,'r',label='$\dot{m}_{PUMP}^{UKF}$',alpha=1.0)
ax1.set_xlabel('Time [hours]')
ax1.set_ylabel('Electrical power [kW]')
#ax1.set_ylabel('Volume flow rate [gpm]')
ax1.set_xlim([t[0], t[-1]])
ax1.set_ylim([0, 4.6])
#ax1.set_ylim([300, 600])
legend = ax1.legend(loc='upper center',
bbox_to_anchor=(0.5, 1.1),
ncol=4,
fancybox=True,
shadow=True)
legend.draggable()
ax1.grid(False)
plt.savefig('Power.pdf',
dpi=400,
bbox_inches='tight',
transparent=True,
pad_inches=0.1)
plt.show()
def showResults(time, results, csvPath, pars):
####################################################################
# Display results
simResults = CsvReader.CsvReader()
simResults.OpenCSV(csvPath)
simResults.SetSelectedColumn("Pump.kW")
res = simResults.GetDataSeries()
t = res["time"]
t = t / 3600.0
time = time / 3600
d_kW = numpy.squeeze(numpy.asarray(res["data"]))
P_el = results["P_el"]
Ndata = numpy.max([len(d_kW), len(P_el)])
new_t = numpy.linspace(time[0], time[-1], Ndata)
d_kW = numpy.interp(new_t, t, d_kW)
P_el = numpy.interp(new_t, time, P_el)
error = d_kW - P_el
MSE = numpy.sqrt(numpy.sum(numpy.power(error, 2.0)))
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.plot(new_t, P_el, 'r', label='$P_{kW}^{MODEL}$', alpha=1.0)
ax1.plot(new_t, d_kW, 'g', label='$P_{kW}^{DATA}$', alpha=1.0)
ax1.set_xlabel('Time [hours]')
ax1.set_ylabel('Power [kW]')
ax1.set_title('$MSE=%.6f$ \n $p_1=%.4f \ p_2=%.4f \ p_3=%.4f \ p_4=%.4f$' %
(MSE, pars[0], pars[1], pars[2], pars[3]))
ax1.set_xlim([time[0], time[-1]])
ax1.set_ylim([0, 5])
legend = ax1.legend(loc='upper right',
bbox_to_anchor=(1.0, 1.0),
ncol=1,
fancybox=True,
shadow=True)
legend.draggable()
ax1.grid(False)
plt.savefig('PumpElectrical.pdf',
dpi=300,
bbox_inches='tight',
transparent=True,
pad_inches=0.1)
corr_coeff = numpy.corrcoef(d_kW, P_el)
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
ax2.plot(d_kW / numpy.max(d_kW), P_el / numpy.max(P_el), 'ro', alpha=0.5)
ax2.plot(d_kW / numpy.max(d_kW), d_kW / numpy.max(d_kW), 'b', alpha=1.0)
ax2.set_xlabel('Normalized measured power')
ax2.set_ylabel('Normalized simulated power')
ax2.set_title('$r=%.6f$' % (corr_coeff[0, 1]))
ax2.set_ylim([0, 1.1])
ax2.set_ylim([0, 1.1])
ax2.grid(False)
plt.savefig('PumpElectrical_2.pdf',
dpi=300,
bbox_inches='tight',
transparent=True,
pad_inches=0.1)
plt.show()