def test_boxplot(self):
'''
Test the :mod:`buildingspy.io.Plotter.boxplot`
function.
'''
import numpy as np
# Construct 3 days of data with 1/2 hour time interval
# The first time stamp is 0, the last time stamp is 72
t = np.arange(0, 24 * 3 + 0.5, 0.5)
y = t
# Convert to period of 24 hours
# (tP, y) = Plotter.convertToPeriodic(24, t, y)
# Create plot
Plotter.boxplot(t,
y,
increment=0.5,
nIncrement=2 * 24,
notch=0,
sym='b+',
vert=1,
whis=1.5,
positions=None,
widths=None,
patch_artist=False,
bootstrap=None,
hold=None)
def test_boxplot(self):
"""
Test the :mod:`buildingspy.io.Plotter.boxplot`
function.
"""
import numpy as np
# Construct 3 days of data with 1/2 hour time interval
# The first time stamp is 0, the last time stamp is 72
t = np.arange(0, 24 * 3 + 0.5, 0.5)
y = t
# Convert to period of 24 hours
# (tP, y) = Plotter.convertToPeriodic(24, t, y)
# Create plot
Plotter.boxplot(t, y, increment=0.5, nIncrement=2 * 24,
notch=0, sym='b+', vert=1, whis=1.5,
positions=None, widths=None, patch_artist=False, bootstrap=None, hold=None)
def test_interpolate(self):
'''
Tests the :mod:`buildingspy.io.Plotter.interpolate`
function.
'''
t10 = range(10)
t100 = range(100)
f = lambda x: 10 + 2 * x
y10 = map(f, t10)
y100 = map(f, t100)
y10Int = Plotter.interpolate(t10, t100, y100)
numpy.testing.assert_allclose(y10, y10Int)
# Add one more element to t100. This emulates an event
# at t=10, in which case dymola adds two points
t100.append(10)
y100 = map(f, sorted(t100))
y10Int = Plotter.interpolate(t10, sorted(t100), y100)
numpy.testing.assert_allclose(y10, y10Int)
def test_interpolate(self):
'''
Tests the :mod:`buildingspy.io.Plotter.interpolate`
function.
'''
t10 = range(10)
t100 = range(100)
f = lambda x: 10+2*x
y10 = map(f, t10)
y100 = map(f, t100)
y10Int = Plotter.interpolate(t10, t100, y100)
numpy.testing.assert_allclose(y10, y10Int)
# Add one more element to t100. This emulates an event
# at t=10, in which case dymola adds two points
t100.append(10)
y100=map(f, sorted(t100))
y10Int = Plotter.interpolate(t10, sorted(t100), y100)
numpy.testing.assert_allclose(y10, y10Int)
def test_interpolate(self):
"""
Tests the :mod:`buildingspy.io.Plotter.interpolate`
function.
"""
t10 = list(range(10))
t100 = list(range(100))
def f(x): return 10 + 2 * x
y10 = list(map(f, t10))
y100 = list(map(f, t100))
y10Int = Plotter.interpolate(t10, t100, y100)
numpy.testing.assert_allclose(y10, y10Int)
# Add one more element to t100. This emulates an event
# at t=10, in which case dymola adds two points
t100.append(10)
y100 = list(map(f, sorted(t100)))
y10Int = Plotter.interpolate(t10, sorted(t100), y100)
numpy.testing.assert_allclose(y10, y10Int)
def test_convertToPeriodic(self):
'''
Test the :mod:`buildingspy.io.Plotter.convertToPeriodic`
function.
'''
import numpy as np
import random
import copy
t = np.arange(0, 1000, 1.0)
y = copy.copy(t)
# Shuffle the y vector
random.shuffle(y, random.seed(1))
(tP, yP) = Plotter.convertToPeriodic(10, t, y)
# Test whether the time vector is periodic
for i in range(10):
numpy.testing.assert_allclose(tP[i * 10:(i + 1) * 10], range(10))
# Test whether y remains unchanged
numpy.testing.assert_allclose(y, yP)
def test_convertToPeriodic(self):
"""
Test the :mod:`buildingspy.io.Plotter.convertToPeriodic`
function.
"""
import numpy as np
import random
import copy
t = np.arange(0, 1000, 1.0)
y = copy.copy(t)
# Shuffle the y vector
random.shuffle(y, random.seed(1))
(tP, yP) = Plotter.convertToPeriodic(10, t, y)
# Test whether the time vector is periodic
for i in range(10):
numpy.testing.assert_allclose(tP[i * 10:(i + 1) * 10], list(range(10)))
# Test whether y remains unchanged
numpy.testing.assert_allclose(y, yP)
def _extract_data(matFile, relVal):
"""
Extract time series data from mat file.
:param matFile: modelica simulation result path
:param relVal: list of variables that the data should be extracted
"""
from buildingspy.io.outputfile import Reader
from buildingspy.io.postprocess import Plotter
import re
nPoi = 8761
try:
r = Reader(matFile, TOOL)
except IOError:
raise ValueError("Failed to read {}.".format(matFile))
result = list()
for var in relVal:
time = []
val = []
try:
var_mat = var
# Matrix variables in JModelica are stored in mat file with no space e.g. [1,1].
var_mat = re.sub(' ', '', var_mat)
(time, val) = r.values(var_mat)
tMin = float(min(time))
tMax = float(max(time))
ti = _getTimeGrid(tMin, tMax, nPoi)
except KeyError:
raise ValueError("Result {} does not have variable {}.".format(
matFile, var))
else:
intVal = Plotter.interpolate(ti, time, val)
temp = {'variable': var, 'time': ti, 'value': intVal}
result.append(temp)
return result
r=Reader(resultFile, "dymola")
DNI.append(r.values('DNI.y'))
NN.append(r.values('EuroTrough.N'))
T_su.append(r.values('SensTsu.fluidState.T'))
T_ex.append(r.values('SensTex.fluidState.T'))
TimeSim.append(T_ex[KKK][0])
T_ex_exp.append(r.values('t_htf_ex.y'))
Delta_T.append(r.values('DeltaT.Delta'))
m_wf.append(r.values('m_dot_htf.y'))
# Create a time vector which values every 10 seconds
TimeExp.append(np.linspace(StartModTime[KKK],StopModTime[KKK],num=int((StopModTime[KKK]-StartModTime[KKK])/10)))
# Interpolate the Modelica results over the Time vector
DNIExp.append(Plotter.interpolate(TimeExp[KKK], DNI[KKK][0], DNI[KKK][1]))
T_suExp.append(Plotter.interpolate(TimeExp[KKK],T_su[KKK][0],T_su[KKK][1]-273.15))
T_exExp.append(Plotter.interpolate(TimeExp[KKK],T_ex_exp[KKK][0],T_ex_exp[KKK][1]-273.15))
m_wfExp.append(Plotter.interpolate(TimeExp[KKK],m_wf[KKK][0],m_wf[KKK][1]))
##########################################################################
# Steady-state values for model and experiments
##########################################################################
# Get index for Modelica model
#Define function to get to closer value of index
def find_closest(A, target):
#A must be sorted
idx = A.searchsorted(target)
idx = np.clip(idx, 1, len(A)-1)
left = A[idx-1]
plt.savefig('plotHeatingCoolingPower.pdf')
plt.savefig('plotHeatingCoolingPower.png')
fig = plt.figure()
#ax = fig.add_subplot(311)
#ax.plot(1./3600/24*ep_tim, ep_int_con, label='e+ convective')
#ax.plot(1./3600/24*ep_tim, ep_int_rad, label='e+ radiative')
#ax.plot(1./3600/24*mo_par, mo_qConGai_flow, label='Modelica convective')
#ax.plot(1./3600/24*mo_par, mo_qRadGai_flow, label='Modelica radiative')
#ax.set_xlabel('time [days]')
#ax.set_ylabel('heat gains [$\mathrm{W}$]')
#ax.legend()
#ax.grid(True)
ax = fig.add_subplot(111)
ax.plot(1./3600/24*ep_tim, ep_HBeaInc-Plotter.interpolate(ep_tim, mo_tim, mo_HBeaInc), label='beam')
ax.plot(1./3600/24*ep_tim, ep_HTotInc-Plotter.interpolate(ep_tim, mo_tim, mo_HTotInc), label='total')
#ax.plot(1./3600/24*ep_tim, ep_HBeaInc, label='e+ total')
#ax.plot(1./3600/24*ep_tim, ep_HTotInc, label='e+ beam')
#ax.plot(1./3600/24*mo_tim, mo_HBeaInc, label='Modelica total')
#ax.plot(1./3600/24*mo_tim, mo_HTotInc, label='Modelica beam')
ax.set_xlabel('time [days]')
ax.set_ylabel('difference in solar incidence [$\mathrm{W/m^2}$]')
ax.legend(loc='lower right')
ax.grid(True)
# Save figure to file
plt.savefig('plotBoundaryCondition.pdf')
plt.savefig('plotBoundaryCondition.png')
T_su.append(r.values('SensTsu.fluidState.T'))
T_ex.append(r.values('SensTex.fluidState.T'))
TimeSim.append(T_ex[KKK][0])
T_ex_exp.append(r.values('t_htf_ex.y'))
Delta_T.append(r.values('DeltaT.Delta'))
m_wf.append(r.values('m_dot_htf.y'))
# Create a time vector which values every 5 seconds
TimeExp.append(
np.linspace(StartModTime[KKK],
StopModTime[KKK],
num=int((StopModTime[KKK] - StartModTime[KKK]) / 10)))
# Interpolate the Modelica results over the Time vector
DNIExp.append(Plotter.interpolate(TimeExp[KKK], DNI[KKK][0], DNI[KKK][1]))
T_suExp.append(
Plotter.interpolate(TimeExp[KKK], T_su[KKK][0], T_su[KKK][1] - 273.15))
T_exExp.append(
Plotter.interpolate(TimeExp[KKK], T_ex_exp[KKK][0],
T_ex_exp[KKK][1] - 273.15))
m_wfExp.append(
Plotter.interpolate(TimeExp[KKK], m_wf[KKK][0], m_wf[KKK][1]))
T_lim_min = [170, 150, 140, 280, 200, 150]
T_lim_max = [300, 400, 300, 400, 320, 350]
NUMBER = [r'$I$', r'$II$', r'$III$', r'$IV$', r'$V$', r'$VI$']
fig = plt.figure()
fig.set_size_inches(8.27, 11.69)
fig.subplots_adjust(hspace=.21, right=.52, left=.06, top=.97)
plt.savefig('plotHeatingCoolingPower.png')
fig = plt.figure()
#ax = fig.add_subplot(311)
#ax.plot(1./3600/24*ep_tim, ep_int_con, label='e+ convective')
#ax.plot(1./3600/24*ep_tim, ep_int_rad, label='e+ radiative')
#ax.plot(1./3600/24*mo_par, mo_qConGai_flow, label='Modelica convective')
#ax.plot(1./3600/24*mo_par, mo_qRadGai_flow, label='Modelica radiative')
#ax.set_xlabel('time [days]')
#ax.set_ylabel('heat gains [$\mathrm{W}$]')
#ax.legend()
#ax.grid(True)
ax = fig.add_subplot(111)
ax.plot(1. / 3600 / 24 * ep_tim,
ep_HBeaInc - Plotter.interpolate(ep_tim, mo_tim, mo_HBeaInc),
label='beam')
ax.plot(1. / 3600 / 24 * ep_tim,
ep_HTotInc - Plotter.interpolate(ep_tim, mo_tim, mo_HTotInc),
label='total')
#ax.plot(1./3600/24*ep_tim, ep_HBeaInc, label='e+ total')
#ax.plot(1./3600/24*ep_tim, ep_HTotInc, label='e+ beam')
#ax.plot(1./3600/24*mo_tim, mo_HBeaInc, label='Modelica total')
#ax.plot(1./3600/24*mo_tim, mo_HTotInc, label='Modelica beam')
ax.set_xlabel('time [days]')
ax.set_ylabel('difference in solar incidence [$\mathrm{W/m^2}$]')
ax.legend(loc='lower right')
ax.grid(True)
# Save figure to file
plt.savefig('plotBoundaryCondition.pdf')
